Edge-Computing-Enabled Smart Cities: a Comprehensive Survey Latif U

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey Latif U. Khan, Ibrar Yaqoob, Senior Member, IEEE, Nguyen H. Tran, Senior Member, IEEE, S. M. Ah- san Kazmi, Tri Nguyen Dang, Choong Seon Hong, Senior Member, IEEE

Abstract—Recent years have disclosed a remarkable prolifer- not limited and can refer to many different processes, resulting ation of compute-intensive applications in smart cities. Such in more reliable, secure, sustainable, and innovative cities with applications continuously generate enormous amounts of data unique entrepreneurial opportunities. The key contributors to which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology smart cities are platform developers, research communities, to compensate for stringent latency related issues, its deployment governments, citizens, service providers, and application de- engenders new challenges. In this survey, we highlight the velopers [7]. role of edge computing in realizing the vision of smart cities. A remarkable increase in city populations is expected in First, we analyze the evolution of edge computing paradigms. the foreseeable future, which will impose substantial computa- Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we tional complexity regarding advancements in the smart cities. categorize and classify the literature by devising a comprehensive Moreover, this rapid rise in populations will impose further and meticulous taxonomy. Furthermore, we identify and discuss challenges on the city infrastructure due to an exponential key requirements, and enumerate recently reported synergies of increase in data generated by different devices, such as smart- edge computing enabled smart cities. Finally, several indispens- phones, global positioning systems, smart sensors, and smart able open challenges along with their causes and guidelines are discussed, serving as future research directions. cameras. One way to cope with the challenges of massive scale computing and storage is the use of expensive dedicated com- Index Terms—Smart cities, Internet of Things, Mobile cloud puting hardware with sufficient storage capacity. To eliminate computing, Cloudlet, Fog computing, Mobile edge computing, Micro data centers. the need for dedicated expensive computing hardware, cloud computing was introduced. This is a powerful technology that is intended to enhance the Quality of Experience (QoE) by I.INTRODUCTION providing on-demand storage and processing capabilities in a The unprecedented plethora of miniaturized sensing tech- cost-effective and elastic manner [8]. The striking features of nologies has shown a remarkable breadth of smart cities vision. cloud computing, such as scalability, elasticity, multitenancy, Smart cities enable citizens to experience sustainable, secure, sufficient storage capacity, and resource pooling, have made its and reliable developments [1], [2]. Smart cities are defined for- adoption possible in different areas [9]–[11]. Recently, cloud mally in numerous ways [3]–[6] and thus, there is no absolute computing with virtually unlimited resources has emerged as definition of the smart cities. Generally, these definitions refer a promising paradigm to tackle the high computational com- to advancements related to information and communication plexity associated with smart cities [12]. However, its inherent technologies (ICT) infrastructure, that enable citizens to realize limitations of high latency, non-context-aware behavior, and high quality of life via smart services. Smart city services are no support for mobility pose serious limitations on its use in real-time smart environments. Apart from these downsides, arXiv:1909.08747v2 [cs.NI] 12 Oct 2020 cloud computing suffers from processing time inefficiency due This work was supported by Institute of Information & communications to the large overhead of smart city device data. On the other Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2019-0-01287, Evolvable Deep Learning Model Generation hand, edge computing extends the cloud computing resources Platform for Edge Computing) and partially supported by the MSIT(Ministry to the network edge and offers context-awareness, low latency, of Science and ICT), Korea, under the Grand Information Technology Re- mobility support, scalability, to name a few. Hence, to address search Center support program(IITP-2020-2015-0-00742) supervised by the IITP(Institute for Information & communications Technology Planning & the limitations of cloud computing for enabling real-time smart Evaluation). *Dr. C S Hong is the corresponding author. city environments, edge computing is a viable solution [13]– [16]. L. U. Khan, I. Yaqoob, T. N. Dang and C. S. Hong are with the Department of Computer Science & Engineering, Kyung Hee University, Yongin-si 17104, Figure 1 elucidates a high-level overview of Internet South Korea. of Things (IoT) based smart city environments enabled by N. H. Tran is with School of Computer Science, The University of Sydney, edge computing. These IoT based smart environments leverage Sydney, NSW 2006, Australia. S. M. A. Kazmi is with the Networks & Blockchain Lab, Secure System connected smart IoT devices and sensors to improved life and Network Engineering, Innopolis University, 420500 Tatarstan, Russia, and standards for citizens. Apart from that, IoT can be defined the Department of Computer Science and Engineering, Kyung Hee University, in different ways because it is associated with a variety of Yongin-si 17104, South Korea. technologies and concepts in literature [17]–[19]. Mostly, the definitions of IoT are inspired from research projects [20]. In 2

Smart Buildings Smart Airport

Waste Recycling plant Smart People Smart Healthcare

Smart Waste Smart Police bin

Cloudlet`

Augmented Reality Cloud glasses Blockchain enabled FOG Node Banking

Mobile Edge Computing Server hospital

Cellular BS hospital Road Smart Parking Side Unit

Smart Smart Grid Agriculture 1 2 3 4 5

Fig. 1: An overview of edge computing enabled smart city.

[17], the authors found the common and frequently occurring context-awareness, mobility support, and low latency. It mi- features among different IoT definitions, which include: grates computing resources, such as computing power, data, • Existence of a global network infrastructure that enables and applications, from the remote cloud to the network edge, a unique addressing scheme, seamless integration, and and thus enables numerous real-time smart city services. connectivity among heterogeneous IoT nodes. Recently, three different emerging technologies, cloudlet, fog • IoT nodes must be addressable, readable, locatable, rec- computing, and mobile edge computing, have been used in ognizable, and autonomous. the literature to bring the striking features of cloud computing • Existence of heterogeneous technologies that must be to the edge [24]. For example, edge computing reduces the given assiduous attention. network bandwidth usage by alleviating the transmission of • Association of services to objects to emphasize its object data from end users to the remote cloud [13]. dependent nature. • Intelligent interfaces between things and humans. A. Research Trend and Statistics We can consider the above-mentioned features (as emphasized Figure 2 illustrates edge computing and smart cities re- in [17]) in a platform to constitute IoT. However, it may not search trends showing the exponential increases in the numbers always be possible for IoT system to possess all of these of publications in both domains. Apart from the research features. IoT is broadly classified into Industrial Internet of publications trend, according to statistics, the percentages of Things (IIoT) and Consumer Internet of Things (CIoT) in people living in cities were 29% and 50% in the years 1950 [18]. The IoT assisted applications for smart cities include and 2008, respectively, which is expected to reach 65% in smart grids, smart lighting systems, smart transportation, smart 2040 [25]. Approximately 1.3 million people are moving health-care, smart homes, augmented reality, and smart agri- to cities every week. In 1975, there were three megacities, culture [1], [21]–[23]. Therefore, it is evident from literature but currently there are 21 mega cities having a population that IoT is an integral part of smart cities. The next step is more than 10 million. Moreover, the number of megacities in enabling the resource intensive and strict latency IoT based 2025 is expected to increase by one city in Africa, two in smart city applications. Edge computing provides a promising Latin America, and five in Asia. Furthermore, cities utilize way of enabling these applications by offering computation approximately 60%-80% of the world energy and generate and storage resources with low latency. 50% of greenhouse gas emissions. Edge computing is a cutting-edge computing paradigm The considerable rise in the number of smart cities in the characterized primarily by its geo-distributed operation, foreseeable future will likely to increase smart cities market 3

autonomous cars ranging from level 0 to level 5 [28]. The level 0 represents no automation and level 5 represents full automation, such as operating without driver assistance. Moreover, autonomous cars can handle lane changing, warning signals, collision avoidance, and in-car infotainment, to name a few. To enable real-time analytics for autonomous cars, the use of edge computing enabled Road Side Units (RSUs) is a viable solution. For instance, consider autonomous car accidents, where timely reporting of accidents is required for instant first aid. The key stakeholders of the emergency response system are medical services, smart police, road operators, and edge enabled public safety answering points [29]. When an autonomous car crash occurs, time is the most critical parameter. The emergency response time must be short to minimize any damage. Reporting such accidents can be performed either by Fig. 2: Number of published articles on edge computing and a person or by the edge enabled public answer safety points. smart cities. Those involved in the crash might suffer from severe injuries and cannot inform authorities about the accident. Therefore, the edge enabled public answer safety points must perform share. It has been predicted that the smart cities market share accident evaluation which requires the execution of complex will increase at a Compound Annual Growth Rate (CAGR) of algorithms. To execute such resource intensive and latency 18.4%. The smart cities market share will reach USD 717.2 sensitive tasks, edge computing is required. billion by 2023 compared to USD 308 billion in 2018 [26]. 2) Smart Forest Fire Detection: A smart forest fire de- The major players of the smart city service providers include tection can substantially reduce damage caused by fires. UAV Toshiba (Japan), Schneider Electric (France), Siemens (Ger- enabled edge computing offers a method of timely reporting of many), Hitachi (Japan), Ericsson (Sweden), Huawei (China), the forest fire. We can use UAVs assisted by edge computing Cisco (US), Oracle (US), IBM (US), Microsoft (US), among for surveillance. The UAVs use cameras to continually take others. On the other hand, the number of smart IoT devices images of the forest and requires further processing of these enabling smart city applications is expected to reach 500 images to determine the presence of fire. One way to determine billion by 2030. the presence of fire is to use the remote cloud to perform video Grand View Research, Inc. estimates the market share images analytics. However, this approach suffers from high of edge computing to reach USD 3.24 billion by 2025 at a latency which is not desirable for immediate action against CAGR of 41.0% [27]. Smart health-care is expected to witness fires. Processing images at the edge computing enabled UAVs the highest CAGR growth in the period from 2017 to 2025. alleviates this latency issue and allows for quick decision Health-care is expected to exceed the market share of USD 326 making for emergency services. Furthermore, we can also million. The potential players in edge computing market are leverage UAVs assisted by edge computing to enable on- SAP SE, Microsoft, Intel Corporation, International Business demand telecommunication and computing infrastructure to Machines (IBM), Huawei Technologies Co. Ltd., Amazon assist in rescue activities [30], [31]. Web Services, Inc. (AWS), Hewlett Packard Enterprise Devel- 3) Smart Parking System: Traditional parking systems opment LP, General Electric, Cisco Systems, Inc., among oth- suffer from challenges of inefficient parking space manage- ers. Edge computing in the market is expected to continually ment and high computational time in finding empty parking increase. Apart from edge computing market, the rapid rise in slots. To cope with these limitations of the traditional parking IoT based smart cities and edge computing research is evident systems, one can use smart parking systems which offer instant from the statistics and research trends. These technological computation of empty parking slots and efficient parking space advancements in edge computing, IoT, and smart cities have management via different schemes based on machine learning revealed captivating research and development opportunities [32]. The smart parking can be enabled via existing surveil- for manufacturers to enhance their market shares. lance cameras and image detection systems. These image detection systems use artificial intelligence (AI) schemes (i.e., B. Motivational Scenarios Convolutional Neural Network) to find the empty parking We provide two motivational scenarios to demonstrate the slots. Therefore, enabling of empty parking slots detection immense importance of edge computing in developing smart with low-latency requires on-demand computational resources. cities. The first scenario deals with reporting of autonomous Edge computing seems to be a promising solution for enabling car accidents, and the second scenario describes the usage of the smart parking system through on-demand computation Unmanned Aerial Vehicles (UAVs) with edge computing for resources [33], [34]. smart forest surveillance. 4) Smart Home: A smart home offers enhanced security, 1) Autonomous Cars Accident Reporting: A gigantic smart meters, and smart control [35]. Enabling smart homes increase in the number of autonomous cars is expected in with these features requires a multi-layer system with the the future. There are six different levels of autonomy in ability to make a decision regarding home automation using 4

TABLE I: Summary of existing surveys and tutorials with their primary focus

Reference Smart city Internet of Mobile edge Cloudlet Fog computing Edge computing Things computing evolution Bilal et al. , [36] Shaukat et al. , [37] Ai et al.,[38] Mouradian et al. , [39] Hu et al. , [40] Taleb et al. , [41] Abbas et al. , [24] Roman et al. , [42] Mao et al. , [43] Khan et al. , [44] Hassan et al. , [45] Yu et al. , [46] Gharaibeh et al. , [47] Lim et al. , [48] Alavi et al. , [49] Silva et al. , [50] Our Survey

real-time and historical data. Such types of multi-layer systems computing. Speciﬁcally they presented general architecture, use different smart IoT devices along with AI techniques to standardization efforts, applications, and design principles of provide various automatic operations at the home. Numerous the edge computing paradigms. In addition, open research AI-based smart home appliances are smart washing machines, issues are also highlighted in their paper. Mouradian et al. smart refrigerators, smart speakers, smart TVs, and smart in [39] proposed a comprehensive survey on fog computing security systems. However, performing smart home tasks architecture and algorithms. The authors proposed evaluation based on AI requires instant computational resources at the criteria, such as heterogeneity management, scalability, mo- edge. Therefore, edge computing can be one of the promising bility, federation, and interoperability. The evaluation criteria solutions to enable smart homes based on AI by offering on- are further used to evaluate the literature related to archi- demand computational resources with low-latency. tecture and algorithms for fog computing. Additionally, the architectural and algorithmic open research problems are also C. Existing Surveys and Tutorials outlined. A comprehensive survey proposed in [40] discussed fog computing architecture, applications, key technologies, In the literature, numerous surveys and tutorials have been and research challenges. The authors proposed a hierarchical presented to provide considerable insights into edge computing architecture of fog computing, followed by a discussion of and smart cities [24], [36]–[50]. A summary of recently key technologies. Apart from that, they also discussed various published surveys along with their primary contribution areas smart environments. Finally, open research challenges are is given in table I. also presented. In [41], Taleb et al. presented the evolution 1) Edge Computing Surveys: Several surveys pertain- of edge computing, use cases of mobile edge computing, ing to edge computing have been conducted in [24], [36]– key mobile edge computing enablers, reference architecture, [46]. In [36], the authors discussed different edge computing deployment challenges, and open research challenges. Abbas paradigms, such as cloudlets, mobile edge computing, and et al. discussed architecture, emerging smart applications, fog computing. Additionally, they discussed applications and security and privacy, and open research challenges in mobile summarized the architectures of state-of-the-art edge comput- edge computing[24]. The authors in [42] provided a survey ing paradigms. Finally, they outlined open research problems on security and privacy issues in mobile edge computing. In regarding edge computing. A survey of cloudlet architec- addition, a detailed overview of edge paradigms concepts, tures, cloudlet solution taxonomy, and challenges pertaining challenges, and security threats are also presented. They to the deployment of cloudlets in local wireless networks is identiﬁed threat models and discussed guidelines for their presented in [37]. The taxonomies are devised using three solutions. Furthermore, a state-of-the-art security mechanism different parameters: augmentation models, cloudlet service used in edge computing paradigms is also outlined. The study models, and architecture. Furthermore, cloudlet deployment in [43] presented a comprehensive survey on computation requirements are also discussed. In [38], Ai et al. pre- and radio resource management in mobile edge computing. sented a study on cloudlets, fog computing, and mobile edge 5

They discussed green mobile edge computing, privacy-aware mobile edge computing, mobility management for mobile edge Sec. 1.1 Research Trend and Statistics Sec. 1 Introduction Sec. 1.2 Motivational Scenarios computing, and cache enabled mobile edge computing. Along Sec. 1.3 Existing Surveys and Tutorials with this, open research challenges are also discussed. In Sec. 1.4 Contributions [44], Khan et al. presented survey on edge computing. They Sec. 2 Evolution or Sec. 2.1Metrics Origin of Edge Sec. 2.2 Evolution have discussed different edge computing paradigms, such as Computing cloudlets, mobile edge computing, and fog computing. key Sec. 3.1 Smart Transportation System characteristics of edge computing are presented. Additionally, Sec. 3.2 Augmented Reality Sec. 3 Recent Sec. 3.3 Smart Health-care fundamental requirements and open research challenges for Sec. 3.4 Smart Grids Advances realizing edge computing are presented. In another study in Sec. 3.5 Smart Farming Sec. 3.6 Smart Buildings [45], the role of edge computing in IoT has been discussed. Sec. 3.8 Smart Management Sec. 3.9 Lessons Learned: Summary Taxonomy has been devised based on edge computing enabled and Insights IoT. Furthermore, numerous smart applications, key requirements, and few open research challenges are presented. Yu Sec. 4.1 Characteristics Sec. 4.2 Edge Analytics et al. in [46] conducted a comprehensive survey on the role Sec. 4.3 Resources of edge computing in IoT networks. They classified the edge Sec. 4 Taxonomy Sec. 4.4 Resource Management Strategies computing based on architecture into the far-end, near-end, Sec. 4.5 Edge Intelligence and front-end. Furthermore, they discussed the advantages of Sec. 4.6 Caching Sec. 4.7 Sustainability edge computing-based IoT infrastructure and reported open Sec. 4.8 Security Sec. 4.9 Lessons Learned: Summary research challenges. and Insights 2) Smart Cities Surveys: In the past, several surveys have Sec. 5.1 Scalability and Reliability been conducted on smart cities [47]–[50]. Gharaibeh et al. in Sec. 5.2 Contex-awareness Sec. 5.3 Efficacious Resource [47] surveyed data management techniques used for ensuring Management reuse-ability, granularity, interoperability, and consistency of Sec. 5 Requirements Sec. 5.4 Sustainability Sec. 5.5 Elasticity smart IoT device data. Additionally, the authors identified Sec. 5.6 Interoperability techniques used for security and privacy in smart cities. Other Sec. 5.7 Privacy and Security Sec. 5.8 Business Model than that, emerging technologies enabling smart cities are also Sec. 5.9 Lessons Learned: Summary discussed. In [48], the authors discussed the use of big data and Insights along with different projects that enable the development of Sec. 6.1 Barcelona Smart City smart cities. The authors have classified the use of urban Sec. 6 Case Studies Sec. 6.2 # SmartME big data into four kind of reference models. Furthermore, Sec. 6.3 WATCH Sec. 6.4 Lessons Learned: Summary the challenges of transforming smart city data into informa- and Insights tion are also presented. The study [49] discussed IoT based Sec. 7.1 Intelligent Caching Sec. 7.2 Collaborative Edge Computing smart city applications. The authors provided an overview of Sec. 7 Open Sec. 7.3 Cooperative and Sustainable Research Challenges current IoT based projects. Additionally, a prototype for a Load Balancing real-time monitoring system using IoT for smart cities has Sec. 7.4 Network Slicing Sec. 7.5 Intelligent Edge Computing been presented. An overview of smart cities characteristics, Sec. 7.6 Cyber and Physical Security architecture, developments, and challenges has been presented in [50]. Sec. 8 Conclusions Sec. 8.1 Conclusions and Future Prospects 3) Our Survey: To the best of our knowledge, we are Sec. 8.2 Future Prospects the first to jointly consider (as can be seen in table I) smart cities, IoT, and edge computing. Previously published surveys Fig. 3: Structure of the survey. and tutorials reviewed smart cities with IoT [48]–[50], whereas [47] considered smart city only. However, [24], [36]–[43], [46] reviewed either edge computing alone or edge computing with • We categorize and classify the literature by devising a IoT. Unlike the existing surveys, we comprehensively present comprehensive and meticulous taxonomy using important premier smart city environments enabled by edge computing, parameters, such as characteristics, security, edge ana- the evolution of edge computing, taxonomy, core requirements, lytics, resources, edge intelligence, resource management and open research challenges. strategies, caching, and sustainability. • A few indispensable requirements for the design of the smart cities architectures using edge computing are pre- D. Contributions sented. The key contributions of this survey are as follows: • Several open research challenges along with their causes • We present the evolution of edge computing using scala- and guidelines regarding the implementation of edge bility, flexibility, cost optimization, automation, mobility, computing in smart cities are discussed. and latency, as metrics. The rest of the paper is organized (illustrated in figure 3) • We report, critically analyze and evaluate the premier as follows: Section 2 discusses the evolution of edge comput- advances made in edge computing enabled smart cities. ing. Recent advances and literature categorization are given 6 in section 3. Section 4 presents the devised taxonomy based size than mainframe computer and are mainly used as mid-size on different parameters. The core requirements in designing servers to process scientific applications. Different from main- edge computing enabled smart cities are given in section 5. frame computing and mini computing, client-server computing The recently reported synergies and case studies along with is based on providing resources to clients on request. Xerox their key objectives, organizations involved, and deployment PARC was the first client-server computing model introduced year are given in section 6. Section 7 identifies, discusses, and in the 1970s. provides guidelines for solutions of the indispensable open 2) Desktop Cloud Computing: The concept of cloud research challenges. Finally, the paper is concluded in section computing dates back to 1963, when the Defense Advanced 8. Research Projects Agency (DARPA) provided $2 million to MIT for project Project on Mathematics and Computation II.EVOLUTIONOR ORIGINOF EDGE COMPUTING (MAC) [52]. The purpose of project MAC was to establish a technology that enables simultaneous use of a computer In this section, we consider the chronological develop- by multiple people. In 1969, the Advanced Research Projects ment and attributes of different computing paradigms, namely, Agency Network (ARPANET) was developed by J. C. R. mainframe computing, mini computing, client server com- Licklider. The vision “Intergalactic Computer Network” (also puting, desktop cloud computing, Mobile cloud Computing known as the Internet) was promoted by Licklider to enables (MCC), and edge computing. Similar to [51], two paradigms, connectivity between all humans on the earth. Later in 1997, functionalism and structuralism are considered in this paper Ramnath Chellappa of Emory University devised the definition to extend our findings. Structuralism enables identification of cloud computing as a ”computing paradigm, where the of the building blocks and their relationships to provide a boundaries of computing will be determined by economic clearer understanding of the phenomenon, whereas function- rationale, rather than technical limits alone”. alism provides insights into the behavior and features of a The paradigm of cloud computing began gaining more phenomenon through analysis of its role in present and future interest when the different organizations started exploiting with functionalities. it to provide services to the end-users. In 1999, Salesforce successful implemented cloud computing to provide software A. Metrics services using the Internet. The end-users were given the We consider six metrics, namely, scalability, flexibility, ability to download an application on demand in a cost- cost optimization, automation, latency, and mobility support effective way from a remote location [53]. Amazon in 2002 for evaluation of different computing paradigms: offered its retail services through the Web. In 2006, Amazon started providing web-based services to clients on-demand. • Scalability: This parameter reflects the ability of a system Google also started providing Google doc services in the same to expand its services in response to increasing demands year. Following this, IBM in 2011 offered IBM SmartCloud of the users. and Apple introduced iCloud to enable storage of personal • Flexibility: An ability of the system to provide computing information, such as videos, music, photos, among others. In infrastructure in an elastic way. 2012, Oracle started offering Oracle Cloud which enabled • Cost Optimization: This refers to the ability of a system three categories of services: SaaS (Software-as-a-Service), to provide users on-demand computing resources for PaaS (Platform-as-a-Service), and IaaS (Infrastructure-as-a- optimizing the overall cost. In addition, it also refer to Service). the cost associated with hardware and software in the 3) Mobile Cloud Computing: MCC was introduced to acquisition of the computing resources. enable mobile computing with the ability to leverage cloud • Automation: The ability to perform cloud updates with- computing. Mobile devices have inherent limitations of fre- out any intervention of the end users. quent disconnections, resource scarcity, and energy con- • Latency: This parameter is used to measure the total straints. Therefore, to execute high complexity tasks, it is execution time of smart city applications. preferable to execute a resource-intensive task on a remote • Mobility Support: It aims to provide an ability for cloud. MCC has been defined in three ways in [54]: enabling seamless execution of IoT-based applications. • MCC refers to the running of mobile device applications (e.g. Google’s Gmail) on a remote server. B. Evolution • MCC deals with the use of mobile devices and other Figure 4 presents the gradual evolution of edge computing stationary devices resources as a cloud resource. Mobile proceeds by other computing technologies, such as mini com- devices form a peer-to-peer network and allow use of puting, mainframe computing, client-server computing, desk- their resources by other devices. top cloud computing, and Mobile cloud Computing (MCC). • MCC refers to a cloudlet which is a micro data center Further details are provided below. consisting of several multi-core computers and which 1) Mainframe, Mini, and Client-Server Computing: In provides computation resources to nearby mobile devices the early era of computing, mainframe computers that include [55]. ENIAC, Markl, BINAC, Whirlwind, UNIVAC, IBM 701, and In this paper, we use the term MCC to mean running of IBM 360, were primarily used to enable bulk data processing. client applications of mobile devices on remote servers, such In contrast, minicomputers (developed by IBM) have a smaller as Google servers and Amazon servers. On other hand, various 7

1960-1989  Information Management System  Virtual Machines  Customer Information Control System  Multiple Virtual Storage 1990-2006 2009-2019  AS400  .Net 2007-2008  Cloudlets  Relational Database  J2EE

Key Technologies  iOS  Web Sphere  Fog Computing  Android  Mobile Edge Computing

Mainframe Computing, Mini Desktop Cloud Mobile Cloud Computing, Client Server Edge Computing Computing Computing Computing

 Deployed at Network Core  Deployed at Network Core  Deployed at Network Edge  Mainframe Computing  Centralized Architecture  Centralized Architecture  Low Latency - High Processing Power  No Support For Mobility  Mobility Support  High Scalability - High Storage  High Latency  High Latency  Elastic Services - Industrial Usage  Average Scalability  Average Scalability  Context-awareness  Mini Computing  Elastic Services  Elastic Services - Low Storage  Support for Mobility

- Low Processing Power - Easier Management

 Client Server Computing Characteristics - Centralized Control System - Increased Security - High Complexity in Management

Fig. 4: Evolution of edge computing. operating systems, such as iPhone OS (IoS) and android, European Telecommunications Standards Institute (ETSI) to which were introduced in 2007 and 2008, respectively, provide enable the placement of computing resources at the radio mobile devices with more capabilities by using apps. access network and core network of mobile telecommunication 4) Edge Computing: Edge computing refers to practices systems [59], [60]. The mobile edge computing servers can be that move computing and storage resources to the network placed at different positions in the radio access network such edge. In the literature, three different practices, such as as a macrocell BS [61], [62], assigned to a group of BSs [63], cloudlets, fog computing, and mobile edge computing have or in a hierarchical fashion at different levels [64]. Consider been used, that extend cloud computing to the network edge. table II, which lists the differences between cloud computing, All of these three terms used in the literature refer to edge mobile edge computing, cloudlets, and fog computing [65]. computing [24], [38], [56]. Edge computing is distributed Mobile edge computing differs from cloudlets and fog com- architecture that enables real-time data processing and accel- puting in its primary scope, which is telecommunication net- eration of data-streams with low latency. Along with this, edge works. In terms of context-awareness, mobile edge computing computing also minimizes the Internet bandwidth usage, which has the highest value while cloud computing has no context- further reduces network congestion and delay. awareness. Fog computing and cloudlets have medium and low The concept of cloudlets was introduced in [55] by context-awareness. Other than context-awareness, mobility is Satyanarayanan et al. to place a small scale data center near supported by all three categories of edge computing, whereas the network edge to enable operation of resource-intensive cloud computing has no or limited mobility support. All of the applications with low latency. A cloudlet represents a middle four computing paradigms such as cloud computing, cloudlet, layer in an architecture having three tiers: a centralized cloud, fog computing, and mobile edge computing offer scalability edge cloud platform (cloudlet), and end device [41]. CISCO and reliability [42]. Both cloudlets and fog computing have coined the term fog computing, which refers to an architecture low deployment cost compared to cloud computing due to that allows the use of edge devices to provide computation low-cost availability of wireless access points and routers [65]. services near the network devices [57]. Similar to cloudlets, fog nodes (edge devices) represent the middle tier of a three- III.RECENT ADVANCES tier hierarchy that consists of cloud, fog nodes, and end This section critically analyzes the dominant trends to- devices. Fog nodes cover a wide range of devices, such as ward the edge computing enabled smart cities. Apart from routers, wireless access points, and data centers [58]. providing insight into the architectural and algorithmic aspects, The term mobile edge computing was introduced by we perform a rigorous evaluation of the literature using various 8

TABLE II: Edge computing paradigms comparison

Cloud computing Cloudlets Fog computing Mobile edge computing Context-awareness No Low Medium High Geo-distribution Centralized Distributed Distributed Distributed Latency High Low Low Low Mobility support No/Limited Yes Yes Yes Distance Multi hop Single hop Single hop/ Multi hop Single hop Scalability Yes Yes Yes Yes Flexibility Yes Yes Yes Yes Deployment cost High Low Low High assessment parameters. These assessment parameters are pri- energy, software, and hardware dimensions. All of these marily derived from the literature of recent advances in edge dimensions are key drivers of sustainability in edge enabled computing enabled smart cities [66]–[85]. We consider five smart cities. We use a check mark () for a parameter if has different assessment parameters, namely, context-awareness, one or more dimensions are considered and cross mark () scalability, sustainability, caching, and security for evaluation if its no dimension that is considered. Table IV-V highlight of the recent advances: key contributions and categorize the recent advances according • Context-awareness: An ability of the system to obtain to different applications. Moreover, these tables present an information pertaining to the nodes locations and sur- evaluation of the literature. rounding environment. Context-awareness offers several advantages including the addition of more meaning to A. Smart Transportation System smart IoT device data, M2M communication facilitation, A smart transportation system is envisioned to enable emergency management assistance, and automatic execu- innovative traffic management schemes, advanced transport tion of smart city services, to name a few. modes, autonomous driving, and in-car infotainment services. • Sustainability: This deals with energy efficient design 1) Smart Transportation Applications: Giang et al. in and use of renewable energy sources. In addition, sus- [66] discussed the development of smart transportation ap- tainability refers to energy harvesting, which takes energy plications leveraging fog computing. They have discussed from environmental sources and radio frequency sources the requirements, state-of-the-art, and research challenges re- and stores it for further use. The primary objective of garding implementation of the fog computing enabled smart sustainability is to reduce the carbon footprint. A sig- transportation applications. Although the authors discussed the nificant portion (more than 80%) of energy is generated requirements of scalability and context-awareness in fog com- using brown sources [86]. Therefore, sustainability is an puting assisted vehicular networks they did not consider ser- important aspect that must be considered in the design of vice migration [70] and security [88], which have significant edge computing enabled infrastructure. Additionally, this importance in vehicular networks. The vehicle might change metric offers an increase in profit, reduction in carbon connectivity from one roadside unit to another during video footprint, and hardware reliability [87]. streaming. Therefore, service migration must be considered in • Scalability: The ability of a system to enable elastic ser- the design of fog assisted application for vehicles. Similarly, vices as per user demands without losing QoS, resulting the security aspect must be addressed to avoid any accidents in a cost-efficient operation. and physical damage. • Caching: The temporary storing of popular content at 2) 5G-enabled Software Defined Vehicular Networks: different locations in a network to enable access with low In [67], the authors proposed a 5G-enabled software defined latency. Caching also reduces congestion in a network vehicular network (5G-SDVN) architecture, that consists of by avoiding repeated traffic flow.Note that caching deals three planes, such as data plane for the transmission of data, with the storage of popular content and edge computing social plane for Vehicular Neighbor Groups (VNGs) network- provides computation resources, but these two concepts ing, and control plane with mobile edge computing. The key can be leveraged simultaneously in smart cities to enable contributions of the authors include the discovery of VNGs a variety of smart applications. using real data set and the integration of software-defined • Security: This refers to a devices physical security and networking (SDN) with mobile edge computing to enable cyber security. More specifically, cyber security deals flexible architecture. In addition, the proposed architecture with the protection of the network, computing infrastruc- provides the benefits of efficient data sharing, ubiquitous ture, and data from attacks. mobile interaction, and reliable resource cooperation. Apart Table III summarizes explanation, key advantages, and dimen- from these benefits, SDN offers separation of the control sions of the parameters to evaluate recent advances. Every logic from the underlying infrastructure, which provides more parameter has a set of dimensions. For instance, sustainabil- freedom for adding functionality (i.e.intelligence) to a network ity has architectural, algorithmic, communication, renewable regardless of change in hardware. Although, the proposed 9

TABLE III: Evaluation parameters: explanation, dimension, and advantages

Parameter Explanation Dimension Advantage

푃1: Context-awareness It deals with locations of smart devices and their surrounding en- • Context-aware architecture • Helps in accurate decision- vironment. • Context-aware algorithm making • Efﬁcient emergency management assistance • Enables automated smart services

푃2: Scalability It refers to an ability of a system to operate effectively with an in- • Scalable architecture • Cost optimization crease in demand by adding more • Scalable hardware • QoS maintenance resources elastically. • Scalable Software • Instant service availability • Scalable algorithm

푃3: Sustainability It deals with energy efﬁcient design and use of renewable energy • Sustainable architecture • Carbon footprint reduction sources to minimize the carbon • Sustainable Software • Proﬁtable infrastructure footprint. • Sustainable hardware • Hardware reliability • Sustainable algorithm • Sustainable communication • Renewable energy sources

푃4: Caching It allows temporary storage of popular contents at different locations • Caching architecture • Instant access to popular con- in a network. • Caching algorithm tents • Caching hardware • Back-haul trafﬁc reduction • Caching software

푃5: Security It deals with physical and cyber security of smart devices and servers • Security algorithm • Continuous service availabil- involve in edge computing. • Security architecture ity • Users privacy preservation

architecture offers significant advantages, but it does not 4) Lessons Learned: Summary and Insights: Herein, consider the case of active service migration as in [66]. we summarized various applications of edge computing in enabling smart transportation, and discussed lessons learned, 3) Cost Optimized Edge Enabled Transportation: The insights, and future enhancements: study [68] considered a smart city scenario where vehicles • Security is one of the important parameters that must be ran applications that take data from the environment and considered in the design of edge computing enabled smart send them to edge computing servers through roadside units transportation systems. For example, if a hacker accesses (RSUs) to enable further processing and analysis. The edge the vehicle and alters the lane change information, this computing servers are co-located with the RSUs, which are might result in accidents, specifically in autonomous cars. further connected to the cloud through the Internet. In a • The challenge of active service migration which arises vehicular network, maintaining seamless connectivity between due to the mobility of vehicles must be tackled. For the vehicles and RSUs is difficult. On the other hand, the instance, moving infotainment enabled smart cars must computational capacities of the edge servers also have limita- have seamless connectivity with the edge server-based tions. Therefore, it is imperative to deploy the RSUs to enable RSU during contents downloading [89]. A set of contents effective resource management and seamless connectivity. The stored at a RSU edge computing server must be provided objective of the paper is to minimize the network deployment seamlessly to the requesting passenger in a car. The cost while fulfilling the minimum required QoS for vehicular passenger in a moving car might lose its connectivity with applications. The problem is formulated as a mixed integer lin- the existing RSU and moved to a new RSU range during ear programming problem, whose goal is to jointly minimize the content downloading phase. This challenge can be the total cost associated with the deployment of RSUs, the handled using effective service migration schemes [70]. cost associated with assigned power levels to each RSU, and For example, one way is to migrate the content to the the cost associated of the distances of RSUs from the cells it new RSU for serving the user. Another way is the use serves (the target area is divided into cells). A single RSU can of content placement at the shared memory placed in serve more than one cell. Therefore, only the cells adjacent to close vicinity to end users at a common location with fast an RSU cell must be considered as opposed to distant cells, connectivity to different RSUs. A requesting end user can which increase the cost. access the contents from the shared memory through its 10

associated RSU. considered compression of the video data prior to transmission. • The set of edge computing server-based RSUs must This adds further delay in addition to transmission delay, be deployed in a fashion that simultaneously minimizes which is not desirable. Apart from that delay, the processing the cost associated with transmission power, number of delay must not exceed the maximum delay limit. The better RSUs, and interference level for improving QoS. option is to use communication technologies with higher data rates instead of compression. 4) Smart Web-based Augmented Reality: In [72], Qiao B. Augmented Reality et al. considered web-based AR enabled by edge computing. Augmented Reality (AR) enabled smart cities provide The latency sensitive and resource-intensive applications of users with smart Industrial Augmented Reality, smart tourism, the web-based AR are hampered by the weak computational smart remote live support, smart web-based augmented reality, efficiency of web browsers. To enable resource intensive and as we discuss in the following subsections. latency sensitive web-based AR applications, web-based AR 1) Smart Industrial Augmented Reality: The authors in assisted by edge computing is proposed by the authors. Differ- [69] proposed an industrial ARarchitecture based on fog ent from edge computing, cloud computing can be leveraged computing, named, Navantia’s industrial AR(IAR). The use to assist computation intensive web-based AR applications. of IAR in smart industries leads to enhancing productivity. However, this approach causes an increase in latency due to The proposed IAR architecture consists of three layers, such the remote location of the cloud. The key contributions of as IAR layer, edge layer, and cloud layer. The IAR layer the authors include a proposal of edge computing assisted includes ARdevices that use WiFi along with other wireless web-based augmented reality, design details, and deployment technologies for connectivity with the edge layer. The IAR challenges. layer devices interact with the edge layer through local edge 5) Lessons Learned: Summary and Insights: In this sec- layer gateways that provide low-latency services, such as tion, we have discussed different smart applications based on data caching and sensor fusion. The authors considered both augmented reality enabled by edge computing. Several lessons scalability and caching in their architecture. However, they did learned and future recommendations derived from this section not consider security which is required for safe operation. are as follows: 2) Smart Tourism: In [70], Taleb et al. envisioned mobile • We derived the use of socially-aware device-to-device edge computing enabled architecture for high definition video communication in edge computing-based AR for smart streaming and considered two use cases of smart tourism. tourism. A set of users requesting the same content from One case is interactive glasses to obtain information about the edge computing-based BS might result in performance the visited site, whereas the other case involves tourists taking degradation due to the extra overhead of requests. On videos, commenting on these videos, and sending them to the the other hand, a user can first check the requesting edge computing server. Although migration of such a service content within its social group before requesting to BS. from one edge server to another is considered in their work, Downloading the content from the nearby devices sig- but they did not consider resource management for multiple nificantly reduces latency compared to requesting from users, which is more realistic than a single user scenario. the BS. Additionally, the requests overhead at the BS is Generally, tourists visit historical places in groups (social minimized. The notion of requesting the content from the communities), therefore device-to-device communication with other users within a social group is attractive due to the social awareness must also be considered for performance fact of similar interests of all the users within a social improvement. The user may request content from another user group. This approach can be used to significantly improve that already has it instead of sending a request from the access the performance of edge computing-based AR for smart point. This will improve the transmission rate of the requesting tourism. user and reduce congestion of the access point. • We derived that novel and lightweight security mecha- 3) Smart Remote Live Support: Edge computing enabled nisms must be proposed for AR-based industrial appli- AR has been considered in [71] to enable smart management cations. For example, an unauthorized user can access of industrial tasks. The authors considered a scenario of the remote live support system and interrupt the proper remote live support that uses guidance from the remote expert operation of the system, which can lead to serious con- through AR and video transmission to tackle complex faults sequences. in a machine. The main parts of the proposed architecture include a client device, edge server, and a remote device. The job of the client device with an integrated camera and C. Smart Health-care running an operating system is to grab video frames for Smart health-care is realized by having low cost and further transport to the edge server. The edge server process effective real-time health-care facilities ubiquitously. all the computationally expensive operations. Additionally, the 1) Smart Voice Disorder Treatment Framework: A voice edge server forwards the video stream to a remote expert. disorder assessment and treatment framework enabled by edge The remote expert has the privilege of pausing the video for computing and deep learning is proposed in [73]. Initially, addition of hints in it. The video contents are sent back to smart sensors are used to collect samples of users voice, the edge server, which are further sent back to the client which are then processed on edge nodes and finally, sent after rendering annotations into the camera feed. The author to core cloud. Decision of automatic assessment is then sent 11

TABLE IV: Classiﬁcation, key contributions, and evaluation of the recent advances Evaluation parameters Application Reference Key contributions P1 P2 P3 P4 P5 Giang et Smart transportation al. [66] • Studied a fog assisted smart transportation applications requirements. • Discussed state-of-the-art transportation applications. • Identiﬁed research challenges in fog enabled smart transportation systems.

Huang et al. [67] • Proposed a 5G SDN enabled vehicular network architecture. • Discovered VNG using real data set. • Proposed architecture considered sustainability, context- awareness, and security.

Premsankar et al. [68] • Proposed deployment of RSUs to jointly reduce deployment and transmission power cost. • QoS is considered for vehicular applications during deployment of RSUs.

Fernandez- Carames et • Proposed a three layered edge computing and AR Augmented reality al. [69] enabled architecture for industries. • Presented a real time implementation of the proposed architecture.

Taleb et al. [70] • Proposed an architecture assisted by AR for high deﬁ- nition video streaming. • Presented a shared memory concept for effective active service migration. • Validated the proposed architecture using a practical use case.

Schneider et al. [71] • Proposed an architecture for remote live support. • A video compression is adopted to reduce the bandwidth requirement.

Qiao et al. [72] • Proposed an edge assisted Web-based augmented reality. • Discussed the deployment challenges of the proposed system.

Muhammad et Smart health-care al. [73] • Proposed an edge computing assisted voice disorder assessment and treatment framework. • The proposed framework provides interoperability.

Rahmani et al. [74] • Proposed a three layer fog assisted framework for smart hospitals. • Presented implementation of the IoT based remote health-care system as proof-of-concept.

Gia et al. [75] • Proposed a remote Health monitoring System for ECG along with joint analysis and notiﬁcation. • The fog assisted gate-ways also provides security by enabling restriction to un-authorized access.

by cloud manager to specialists for issuing a prescription to 2) Smart Hospitals: A three-layered fog based health- patients. A database used for training, testing and validation care architecture for homes/ smart hospitals was proposed in is the Saarbruken Voice Disorder (SVD) database. Prime [74]. The proposed architecture comprises of smart devices limitation of their work lies in the usage of few disorders, layer, edge layer, and cloud layer. The devices layer has such as vocal fold polyp, vocal fold paralysis, laryngitis, medical sensors that captures the biomedical signals from the Kontakt pachydermia, hyper-functional dysphonia, functional human body. These biomedical signals are further sent to the dysphonia, and cordectomy, whose samples have sufficient gateways through wireless technologies, such as ZigBee, Wi- amount in the database. Fi or Bluetooth. The cloud layer consists of cloud comput- 12 ing that performs data analytics. The edge layer consists of and routers in the backbone network make second layer of smart e-health gateways, that are distributed geographically the proposed architecture. Third layer has different types of and capable of supporting protocols for wireless communi- servers, such as a certificate authority server, file server, and cation and inter-device communication. In addition, the edge database server and fourth layer is made by a distributed layer also performs local data processing, data filtering, data cloud (infrastructure as a service). In a smart city environment, compression, data fusion, data analysis, local storage, and charging of PHEV batteries from the nearby charging station interoperability functions. may result in many requests at charging stations. The proposed 3) Remote Electrocardiography Monitoring: In [75], a architecture is based on the use of VDTN for delivery of smart health-care monitoring system is proposed, that enable charging and discharging decisions. The authors compared the continuous remote monitoring of Electrocardiography (ECG) delay incurred using the traditional core-cloud infrastructure signals. The monitoring system consists of a cloud, fog as- and edge computing enabled infrastructure and observed less sisted gateways, and sensor nodes. Purpose of sensor nodes incurred delay, more throughput, and less response time for is to acquire bio-signals (such as human temperature, respi- edge computing. ration, and ECG) and contextual information (such as room 2) Cloud–Fog–Based Smart Grid Model: In [78], a three- temperature and humidity). Fog assisted gateway receives the layered framework named cloud-fog-based smart grid is pro- signals from sensor nodes and processes it for further sending posed. The first layer is the end users layer, followed by the it to cloud. For example, pre-processing of the bio-signals second fog layer, and the third cloud layer. The end layer are necessary for removal of noise and extraction of useful comprises of buildings which have homes. Energy storage information from it. Furthermore, the fog assisted gateways systems with renewable energy generation units are installed have two storage databases, such as local and external user at every home. The information related to energy generation database. A local database is used to store the information and consumption is sent to the fog layer. The fog layer that that can be updated or edited by the administrators only. On physically exists close to the consumers region, performs ef- the other hand, an external database is used for storage of fective management of the network resource with low latency. bio-signals along with contextual information, such as room Furthermore, the authors proposed hybrid artificial bee ant temperature and humidity. Data stored within the external colony optimization (HABACO) algorithm for optimal allo- database has synchronization with cloud servers. Additionally, cation of virtual machines in the cloud-fog-based smart grid the fog assisted gate-ways also provides security to restricts model. The authors considered two scenarios for performance the unauthorized access. evaluation of their cloud-fog-based smart grid model but did 4) Lessons Learned: Summary and Insights: We con- not consider the commercial scenario that has a significantly cluded that it is necessary to use federated learning for different nature than residential areas. enabling intelligent edge-based smart health-care applications A reference model for integration of fog computing and [90], [91]. Different health-care applications driven by ma- smart grids is introduced in [77]. The three-tier model consists chine learning are disease identification and diagnosis, drug of smart grid tier, fog tier, and cloud tier. The smart grid discovery and manufacturing, better radiotherapy, outbreak tier has the responsibility of enabling communication between prediction, crowdsourced data collection, and medical imag- different smart devices, such as smart meters, mobile devices, ing diagnosis [92]. Traditional machine learning requires the electrical vehicles, and smart appliances. The fog tier acts migration of data from devices to a centralized location. as an intermediate layer between the cloud and smart grid However, this approach suffers from the serious privacy con- tier. Every fog server communicates with smart meters for cerns. On the other hand, federated learning offers on-device the collection of data of their associated users. Additionally, distributed machine learning and thus, preserves user privacy the fog layer servers take the private data from the smart grid which is of significant interest in smart health-care. Therefore, layer in encrypted form to ensure the privacy of the users. The it is recommended to enable smart health-care applications via fog server does not have the capability to decrypt the data federated learning. However, the enabling of federated learning and the key is only shared among the cloud and the users. at the edge requires on-demand computational resources [93]. The main purpose of the fog servers is to provide temporary Therefore, federated learning and edge computing must be storage and computation capability for the smart grids data. used simultaneously to enable smart health-care applications. The prime limitation of their work lies in non-consideration of the challenge of interoperability, which is one of a most important issue among different power domain areas due to D. Smart Grids the usage of multiple semantic data models in a smart grid A smart grid is a cutting edge technology to enable the environment [94]. transformation of the traditional grids to reduce utility costs 3) Lessons Learned: Summary and Insights: We con- and global warming using renewable energy resources, smart cluded that privacy and security must be given primary impor- meters, and smart appliances. tance in smart grids enabled by edge computing. Specifically, 1) Smart Grid Data Management: Authors in [76] pro- certain power plants such as nuclear and hydroelectric power posed a four-layer architecture for data management based on plants must be provided with effective security and privacy Vehicular Delay-tolerant Network (VDTN) and edge comput- mechanisms. A typical smart grid based on edge computing ing for smart grids. First layer consists of mobile devices, such and cloud has multiple layers: Cloud layer, edge layer, and as Plug-in Hybrid Electric Vehicles (PHEV). The gateways IoT layer [95]. Enabling security at the cloud is easier than the 13 edge layer because of its centralized nature. On the other hand, Some malicious user might access the system and perform edge computing-based power plants are based on distributed wrong activities, such as unnecessary alarms and windows architectures, thereby more vulnerable to security attacks. For opening. instance, consider energy trading in edge computing-based 2) Smart Animal Welfare Monitoring System: In [80], smart grid. To make edge computing-based smart grids more a smart farm assisted by edge computing for the welfare secure, we can use blockchain for energy trading at network of animals is proposed. The proposed architecture consists edge between subscribers and energy suppliers. However, of an animal-centric system, environmental system, and farm using blockchain in edge computing-based smart grids have controller. The farm controller can be implemented either in a significant challenges with respect to resource optimization centralized fashion or in a distributed fashion. A Raspberry Pi (i.e., computational and communication resources) for reach- (R-Pi) which is a small sized micro-computer is used in the ing a consensus among blockchain nodes. implementation of the environmental subsystem and animal- centric system. In system implementation, the environment R- Pi monitors the environment, whereas wearable R-Pi monitors E. Smart Farming the health of animals. A workstation that implements the farm Smart farming is aimed to utilize advanced technologies controller performs the central tasks of the system. One of the for increasing productivity and reduce cost simultaneously most prominent advantages of the proposed architecture is its [96]. ability to send alarms to mobile. These alarms are use full to 1) Smart Precision Agriculture: In [79], a distributed detect whether animals are ill or not, especially when the cow IoT-based smart framing infrastructure assisted by edge nodes is pregnant which can be detected from its body temperature is proposed to experience farmer with enhanced benefits. The [98]. The authors did not consider the robustness of the sensors proposed architecture consists of four levels, i.e., things layer, network used for the collection of data which might suffer edge layer, fog layer, and communication layer. In things from the data loss due to battery depletion or other sources as layer, a thing can be either actuators, controller or sensors, observed in the [99]. which are designed to meet production facility. The function 3) Lessons Learned: Summary and Insights: The lessons of edge computing is to provide interoperability, storage, smart learned and derived future prospects from this section are as analysis, and prediction near the edge. Reason for using edge follows: computing is due to the low latency requirements of control, • A typical precision agriculture system consists of several analysis response, and monitoring sensors. Although authors layers: Sensors layers, edge computing layer, and cloud considered interoperability but did not consider caching in layer. To enable efficient operation of smart agriculture, the proposed framework. Caching can be utilized to further sensors layer require instant instructions and computa- improve the performance of the smart framing [97]. In [97], tional resource for processing of complex algorithms a joint latency and energy optimized communication were at the network edge. One way is to store frequently achieved through data caching algorithm in Precision Agricul- requested instructions at the remote cloud due its high ture (PA) system. The data caching algorithm was based on storage capacity than edge computing servers. However, the optimization of sleep-up/wake-up time of wireless sensor this will increase the delay and affects the system perfor- network nodes. Data requests of sensor nodes are analyzed in mance. Another way is to store these frequently requested a cache prior to transmission. This prolongs the life of the instructions at the network edge. Therefore, we concluded sensor nodes and thus, improves the energy efficiency of the that intelligent edge caching to store the frequently re- PA system. quested instructions by the sensors layer can be used In [81], the authors integrated PA with edge computing to in precision agriculture for performance enhancement. enable real-time actions for better productivity. They proposed In a typical layered architecture of precision agriculture three planar architecture, such as the local plane, edge plane, consisting of a sensor layer, edge server layer, and cloud and cloud plane. In a local plane, cyber-physical systems layer, intelligent edge caching reduces the transmissions perform a collection of data through interaction with crop required by the sensors with a remote cloud for perform- devices to enable real-time control actions. The second edge ing different operations. The instructions requests by the plane is responsible for performing tasks of the PA near the sensors layer are analyzed by the intelligent cache at the network edge to enhance reliability. The third cloud tier exists edge server before forwarding the request to the cloud. remotely to enable data analytics and smart management. The The requested instructions are provided instantly to the cyber-physical systems are connected to sensors and actuators, sensors if they are available in the intelligent edge cache which are installed at the crop premises for the collection of otherwise the request is forwarded to the remote cloud. temperature, humidity, electrical conductivity, and pH meter. This type of transmissions reduction between the sensors All the cyber-physical systems have inter-connectivity with and remote cloud due to intelligent caching significantly the Internet using multiple access networks technologies, such increases the overall throughput of the network and thus, as digital subscriber line, optical fiber, and microwave radio improves the precision agriculture performance. links. In the edge plane, the tasks including energy and alarm • Similar to smart health-care, AR, and smart transporta- management, greenhouse control, and nutrition control are tion, it is necessary to propose effective and lightweight performed. In the proposed architecture, the authors did not security mechanisms for edge computing-based smart consider security which must be given proper consideration. farming. A malicious user can access the system and 14

TABLE V: Classiﬁcation, key contributions, and evaluation of the recent advances Evaluation parameters Application Reference Key contributions P1 P2 P3 P4 P5 Kumaret Smart grids al. [76] • Proposed a generalized architecture for data management based on VDTN using edge computing for smart grids. • An energy efﬁcient virtual machine migration utilizing load forecasting is also proposed.

Okay et al. [77] • Proposed a model for integration of smart grid with fog computing. • Evaluated the proposed model using qualitative use case.

Zahoor et al. [78] • Proposed a three layered cloud-fog-based smart grid framework. • A hybrid artiﬁcial bee ant colony optimization algorithm is also proposed for virtual machines allocation.

Ferrandez- Smart farming Pastor et • Proposed a ﬂexible layered IoT assisted PA architecture al. [79] enabled by edge computing. • Provides interoperability among different sub systems in the proposed PA architecture. • Experimental validation of the proposed PA architecture is also provided.

Caria et al. [80] • Proposed a programmable Fog computing enabled architecture of smart farm for welfare of animals. • Presented a Raspberry Pi assisted implementation of the proposed architecture.

Zamora- Izquierdo et • Proposed a three planar edge assisted PA architecture. al. [81] • NFV is exploited in edge layer to enable ﬂexible generic control modules. • The proposed architecture is validated using prototype implementation at real green-house in Spain.

Vallatiet Smart buildings al. [82] • Proposed an edge computing enabled smart home architecture using LTE. • Discussed the advantages and disadvantages pertaining to placement of edge severs at different positions, such as eNB, home, and UE.

Stojkoska et al. [83] • Proposed a three tier IoT framework enabled by Fog computing for smart homes.

Smart management Jia et al. [85] • Proposed an IMCS enabled by edge computing. • Context-awareness, scalability, and sustainability are also exploited in the proposed system. • Narrow-band IoT is used for communication due to its high energy efﬁciency, low cost, and support for massive number of manhole covers.

perform some malicious activity, such as unnecessary 1) IoT based Smart Homes: A smart home architecture alarms and windows opening in a precision agriculture is presented in [82], that leverages edge computing and scenario. Long-Term Evolution (LTE) network. Different alternatives for placement of edge computing servers are considered that include:(a) placement of edge computing server at Evolved F. Smart Buildings Node B (eNB), (b) placing the edge computing server at Smart buildings provide the users with advanced au- femtocells BS in a home, and (c) placing the edge computing tomated controls, i.e., smart meters, smart water control, server at User Equipment (UE). The positioning of edge smart plug-in hybrid electric vehicles (PHEV) charging, smart computing server at eNB needs to cover a large number of elevators, and smart security. far users and thus, require high computational power. On the 15 other hand, placement of edge computing server near the UEs considered context-awareness, scalability, and sustainability in require less capacity due to its small coverage area. Simulation the design of IMCS, but did not consider security. for two different scenarios, such as device-to-infrastructure 1) Lessons Learned: Summary and Insights: We con- (D2I) and D2D communication in IoT was performed using cluded that the edge computing can serve as a PaaS for running SimuLTE tool[100]. of compute-intensive algorithms required for smart manage- 2) Sustainable Smart Homes: A three-tier framework for ment. Apart from that, sensors used in smart management smart homes based on IoT and edge computing is proposed generates an enormous amount of data which motivates the in [83]. Three tiers of the framework are a smart homes tier, use of effective machine learning techniques for making the a nanogrid tier, and a microgrid tier. The smart home tier management system smarter. On the other hand, different cities consists of household items with interfaces to enable wireless have different context-awareness features, such as location communication. All of data at smart homes is collected at of the sensors and their surrounding environment. Therefore, the sink, which has ability of local processing and storage. In it is necessary to consider context-aware machine learning the nanogrid tier, sinks of different smart homes communicate models for smart management systems in cities rather than with each other. To enable communication between sinks, considering a general machine learning model. Based on different typologies, such as mesh, cluster or star can be context-awareness, machine learning for smart cities can be used. In the microgrid tier, sinks of different smart homes either global context-aware or local-context aware. The former communicate with utility through gateways. IoT devices have is trained for a set of users located in a large geographic area, limited energy, therefore edge computing enabled gateways whereas the latter covers the users of similar interests reside can be used to provide the computation services to smart at the network edge. homes. Major limitation of the framework lies in lack of considering security which poses significant challenges to IV. TAXONOMY smart homes operation. A malicious might access the IoT- Figure 5 elucidates the taxonomy which is devised using based smart home objects and operates it in an irregular way edge intelligence, edge analytics, resources, caching, char- [101]. acteristics, sustainability, security, and resource management. 3) Lessons Learned: Summary and Insights: We derived Further details are provided in the following subsections. the following lessons from this section: • The key requirement of interoperability (discussed later in A. Characteristics section V) is derived from this subsection. A large number of heterogeneous IoT devices must perform seamless Edge computing paradigms generally appear in three interaction with each other to enable edge computing and different formats, such as cloudlets, fog computing, and mobile IoT-based smart homes. edge computing [42]. All of these formats use the same notion • We derived that it is necessary to propose novel forensic of migrating the computing resources from the cloud to the tools and models for edge computing-based smart envi- edge of the networks. However, there exist unique differences ronments. A typical smart home consists of a wide variety among them in terms of context-awareness, inter-node commu- of IoT devices that interact with each other and edge nication, and the position of the computing resources. Mobile computing server. These IoT devices and edge servers edge computing has the highest context-awareness due to their are highly susceptible to security attacks. Therefore, it availability of user device location, network load, and network is necessary to develop novel forensic models and tools capacity at the edge servers [102]. Cloudlets have the lowest for edge computing-based smart environments to enable context-awareness due to their standalone architecture, which investigation of the attacks. is connected to a cloud. Fog computing has context-awareness between that of mobile edge computing and cloudlets. The ability of inter-node communication somewhat improves the G. Smart Management context-awareness level of fog nodes. Other than context- In the smart city era, information and communication awareness, inter-node communication has more support in fog technologies are incorporated to enable smarter urban in- computing than both cloudlets and mobile edge computing. frastructure management for improving overall user QoE. The key feature of robust edge computing architecture for In [85], an Intelligent Manhole Cover Management System smart cities is derived from this section for future research. (IMCS) that leverages edge computing is proposed for avoid- A typical edge computing-based smart environment consists ing accidents due to the manhole cover. The proposed IMCS of a wide variety of players that interact with each other. comprise of three parts: perceived recognition technology, The commonly used players are edge servers, IoT devices, network transmission technology, and information processing remote cloud, and communication infrastructure. Every tech- technology. In perceived recognition technology, a Radio- nology has distinct specifications and requirements. Therefore, Frequency Identification (RFID) tags and sensors are attached it is necessary to propose novel robust architectures that with manhole to facilitate active communication. In the second enable the integration of edge computing with other emerging part, narrow band-IoT and the Internet are used for commu- technologies to offer different smart services. One way to nication between the manholes and end users. The third part achieve robust operation is through fail-over and redundancy consists of an edge computing enabled intelligent management techniques. A fault affected edge server can be replaced by system that allows immediate decision making. The authors another backup server to continue the operation. However, this 16

Data Mining Devices Physical Predictive Security Forecasting

Standard Reports Advanced Networking Access Network Service Communication Descriptive Availability Enablers Resources Adhoc Reports Machine Learning Core Network Mathematical Programming Edge Server Evolution Server Computation Encryption/ Hardware Computation Prescriptive Decryption Requirements Resources Optimization End Node Evolution Local Computation Query Tools Micro Services Edge Node Storage Software Storage Authentication Diagnostic Virtual Machines Decision Trees Requirements Resources Statistical Tools Containers Local Storage

Security Edge Analytics Edge Intelligence Resources

Taxonomy

Resource Caching Characteristics Sustainability Management Medium Context- awareness Biomass Content Caching Resource Partial Internode Renewable Content Cloudlets Geothermal Discovery Communication Energy Sources Service Caching Data Center in a Box Wind Access Point/ Optimization Low Contex-awareness Base Station Resource Graph Theory Environmental Energy Cache Placement Management Game Theory Fog Computing Internode Communication is Core Network Support Harvesting Tools Heuristic Radio User Device Hybrid Heterogonous Fog Frequency Nodes Energy Efficient High Context-awareness Fairness Sotware Distributed Resource QoS Caching Control Management Mobile Edge Partial Internode Energy Efficient Energy Efficient Centralized Computing Communication Design Criteria Throughput Resource Management MEC Servers at BS or Caching Game Theory Energy Energy Efficient Mathematical Radio Access Network Optimization Hardware Tools

Fig. 5: Taxonomy of edge computing enabled smart cities. approach increases the cost of the system. On the other hand, the future, fog nodes, cloudlets, and mobile edge computing different critical smart applications (i.e., autonomous driving, servers will be designed with the ability to perform big data delivery UAV control, and smart industrial manufacturing) analytics. require ultra-reliability. Therefore, there must be a trade-off Similar to cloud computing, edge analytics can be de- between the robustness and cost of the system. scriptive, diagnostic, predictive, and prescriptive. Descriptive analytics is a simple class of analytics that enables users to transform big data into small chunks with useful information. B. Edge Analytics It is preferable to use descriptive analytics when understanding Edge analytics deals with the analysis of data near the is required at an aggregate level. This allows users to learn network edge, and then sending back these results to the from past events, which can be from either not long (e.g., edge devices. This has a significant impact on reducing the 2 min) or from a long time ago (e.g., 10 weeks). Examples delay incurred when sending data to a centralized cloud of descriptive analytics are percentage changes, average, sum, for analysis. In smart cities many devices, including smart among others. Predictive analytics allow the use of machine sensors, and other smart devices from a variety of real-time learning and statistical modeling for analyzing historical data smart applications, including augmented reality, smart health- and predicting future trends. Sentimental analysis is an ex- care, smart industrial control, smart transportation, and smart ample of predictive analysis. Prescriptive analytics allows surveillance, generate a huge amount of data. To enable such performing near optimal or optimal actions in accordance with real-time applications, it is necessary to perform data analytics the predicted or actual data. Prescriptive analytics include at the edge rather than at the centralized remote cloud. In mathematical programming (such as integer programming, 17 non-linear programming, and linear programming.) and opti- necessary to use computation replication in edge computing mization. On the other hand, diagnostic analytics is based on enabled smart services. In computation replication, multiple examining to understand ”Why somethings happened?”. For copies of the same task or a sub-task are transmitted to diagnostic analytics at the edge, query tools, decision trees, different servers. After the computation of the task by all and statistical tools can be used. the edge servers, the result is transmitted back to the user using a diversity scheme to further decrease the download C. Resources time and improve the signal quality. Although the computation replication increases robustness and decreases download time, Edge computing converges information technology it suffers from increased upload time due to generally low up- and telecommunication services to enable computation of link bandwidth nature. Therefore, a trade-off must be made resource-intensive tasks at the network edge. To carry out between the robustness and up-link upload time. Additionally, the above process, we need three types of resources, such as we derived that there must be effective mobility management computation resources, communication resources, and storage schemes for edge computing enabled smart environments. A resource [24]. Computation resource can be either a local set of IoT nodes involved in different smart environments (i.e., device or server. A user offloads either all components of an smart transportation) can have significant mobility. Therefore, application (binary offloading) [103]–[105] or some compo- it is necessary to propose novel mobility management schemes nents of an application to the edge server (partial offloading) for edge computing-based smart environments to enable seam- [106], [107]. A complication might arise in partial offloading less connectivity with edge computing servers. when the task components are dependent on each other, and making it difficult to separate the task components for local execution and execution at server. Apart from computation E. Edge Intelligence resource there are two types of communication resources, that Edge intelligence will transform edge computing ana- are required to enable edge computing in smart cities. One is lytics capabilities to the next level by enabling self-learning the access network, which allows connectivity of the end-user solutions. The three fundamental aspects of edge intelligence devices to the edge servers, and the other is a core network are key enablers, hardware requirements, and software re- communication resource, which allows transmission of data quirements [122]. Edge intelligence can be enabled through from the edge server to cloud server at the time of insufficient advanced network capabilities and machine learning. Other computation resources at the edge server. Different from than key enablers, the edge server must be evolved to yield a computation and communication resources, storage resources virtualized and truly dynamic micro data center. Additionally, can be either at a local device or at an edge. end devices must be evolved to handle intelligent operations. Apart from hardware evolution, the key goal of edge com- D. Resource Management Strategies puting is to isolate the functionality from the hardware that is provided by services form. Such isolation has the advantage of In smart cities enabled by edge computing, the key being deployable anywhere in edge computing enabled smart resources are the massive number of smart devices, edge cities. The concept of micro-services can be used for isolation, servers, and communication infrastructure. To enable edge whose deployment requires the same run-time environment at computing-based smart environments, it is necessary to ef- all deployment locations. Both virtual machines and containers fectively manage the available resources. The main aspects can be used to run micro-services. However, containers offer of resource management include resource discovery, resource less overhead than virtual machines. Moreover, containeriza- management tools, and resource management criteria. Fairness tion is also preferable in the cloud for deployment of micro- [108], [109], QoS [110], [111], throughput [112], energy services. Therefore, at the edge it is also preferable to use [113]–[116], among others, can be utilized as criteria to containerization to provide a uniform run-time environment. design resource management schemes. Other than resource We derived the open research challenge of Intelligent Edge management criteria, we can use optimization-based schemes from this section that is further discussed along with possible [117], game theory-based schemes [118], heuristic [119], guidelines in section. VII-D. Edge analytics can play an graph theory-based schemes [63], [120], and hybrid schemes important role in future smart cities. Massive numbers of smart (which jointly utilize optimization and game theory) [121], devices and sensors produce a huge volume of data, out of for resource allocation. An optimization-based technique can which only a small portion of data is useful. This requires be either dynamic programming, linear programming, convex data filtering and intelligent instant processing, which can be optimization, or integer programming. On the other hand, enabled by intelligent edge-based AI. game theory-based schemes can be based on either cooperative games or non-cooperative games. Herein, we derived the use of computation replication F. Caching in edge computing enabled smart environments. Typically, Caching is temporarily storing the network contents to a task or sub-task in edge computing is migrated toward a avoid repeated transmissions. In a typical communication single edge computing node. The edge computing node sends network, the back haul-links suffer from congestion due to back the computed result to the requesting user. On the other repeated requests of the same contents. For instance, in the hand, a wireless channel is specified by random variations Internet traffic, the video stream is the major source of traffic that significantly degrades the signal quality. Therefore, it is and as counts for 54% of all traffic, which is estimated 18 to reach 71% in 2019 [123]. Therefore, it is necessary to H. Security store the video contents temporarily at different locations to Edge computing infrastructure exhibits novel security avoid repeated transmissions. Edge computing with caching risks and challenges due to its distributed nature in contrast can be used to enable resource-intensive applications, such to a centralized cloud [42], [137]–[139]. In edge computing, as smart industries, smart transportation, augmented reality, numerous players, such as virtualization platforms, distributed smart tourism, to name a few [124]–[127]. Caching in edge and peer-to-peer systems, and wireless networking technolo- computing enabled smart cities has four main aspects such as gies are involved [140]. To enable security, we must protect all cache location, cache contents, caching control, and caching of these players from attacks by implementing diverse security mathematical tools. Caches can also be placed either at the mechanisms, whose orchestration is often very complex [42]. core network or at the Access Point (AP) depending the on In addition, the security schemes must be autonomous due to the requirements. Cache placed at the core network requires a the decentralized nature of edge computing. The prominent high capacity due to the fact that multiple access points will benefit of autonomous and distributed security mechanisms is be connected to it. Apart from that, a cache can be placed their lower latency compared to centralized security mecha- at the AP or at the UE. Different from cache placement, nisms. caching control may be either centralized [128] or distributed Generally, edge computing security risks can be classified [129]. Furthermore, control structure and mathematical tools into denial of service attacks, man in the middle situations, for caching can be based on machine learning, optimization, physical damage, and privacy leakage [42]. An attacker can and game theory [130], [131]. The challenge of Intelligent easily access end user devices or connect to the edge servers, caching which is discussed in detail along with possible and then install malicious software on it. Therefore, it is guidelines in section. VII-A, is derived from edge caching very difficult to secure end-user devices and edge servers for different smart applications. Intelligent caching based on physically due to their geographically distributed nature. On AI and machine learning is expected to offer a considerable the other hand, in man in the middle attacks, a malicious decrease in latency for real-time applications. Moreover, it user takes control of the wireless network, followed by traffic increases the core network throughput and offers a solution injections, whereas privacy leakage refers to unauthorized to mobility-aware caching. access of a users information stored at the edge data center. G. Sustainability Denial of service attacks refer to wireless jamming. Both denial of service and privacy leakage have a limited scope Sustainability refers to the use of energy efficient de- than a man in the middle attacks. Therefore, to avoid man signs and renewable energy resources to reduce the overall in the middle attacks, we must use effective and lightweight carbon footprint. In future smart cities, densification of devices encryption/decryption schemes [141]–[143]. Furthermore, ef- and servers is expected, which in turn will result in energy fective authentication protocols must be devised for edge limitations. Therefore, sustainable developments are required. enabled smart cities to ensure user privacy [144]–[149]. In Sustainability in edge computing can be achieved by using [147], a novel authentication scheme based on certificateless renewable energy sources [132], energy harvesting [133]– public key cryptosystem has been proposed to provide secure [135], and energy-efficient design [136]. Renewable energy transport of messages between mobile devices acting as edge sources include biomass, geothermal, and wind energy. Energy computing nodes and autonomous connected vehicles. Another efficient design has three important aspects, such as energy paper studied radio frequency identification (RFID) based efficient software, energy-efficient hardware, and energy effi- authentication scheme for edge computing [148]. The authors cient resource management. Other than energy efficient design proved that authentication schemes based on states-limited and renewable energy sources, energy harvesting deals with synchronization are not able to well defend the denial of the extraction of energy from external sources for further service attacks. Moreover, a privacy-preserving authentication use in the operation of small devices, such as sensors and protocol with low-complexity is proposed. On the other hand, wearable devices. Energy can also be harvested either from a clustering-based physical-layer authentication scheme has the environmental sources, such as wind, solar, and thermal, or been proposed in [149]. The proposed scheme combined from radio frequency sources. There are significant variations clustering and lightweight symmetric cipher with channel in the harvested energy from both natural sources and radio state information to provide mutual authentication between frequency sources [133]. Therefore, it is imperative to design edge nodes and IoT devices. Moreover, the proposed scheme hybrid systems that utilize both harvested energy and energy is validated via a real-time multiple-input multiple output- from the grid. A hybrid system initially must entirely use orthogonal frequency division multiplexing (MIMO-OFDM) harvested energy, and then later use energy from the grid system. The authors proved that their proposed scheme can in instances when the harvested energy level becomes lower resist again the spoofing attacks, replay attacks, and integer than the required energy for operation. The use of hybrid attacks. energy sources to enable sustainable operation is derived in this section. Hybrid energy sources make use of both harvested energy and grid energy on demand. First, such a system V. REQUIREMENTS utilizes the available harvested energy. The grid energy is used This section outlines and explains core requirements to only when the harvested energy level falls below the required turn the vision of edge computing enabled smart cities into energy level of the application. reality. These requirements (illustrated in figure 6) include 19

Scalability & Reliability  Resilient to Hardware Failures  Resilient to Software Flaws  Scalable Hardware Sustainability Interoperability  Scalable Software  Hybrid Energy  Interoperable Sources Interfaces  Energy Harvesting  Open Source  Renewable Energy Frameworks Sources  Energy Efficient Design

Resources Management Security  User-defined Utility-  Decentralized based Resource Security Edge Enabled Management  Cyber Security  Adaptive Resource Smart City Management Requirements

Elasticity Business Model  Prediction of User  Pricing Model Demands  Services and  Elastic Services products  Customers Context-awareness  Distribution  Network Load and Capacity  Smart Devices Location

Fig. 6: Edge computing enabled smart cities requirements scalability and reliability, resource management, interoper- by the addition of new service providing points. Addition of ability, sustainability, elasticity, context-awareness, security, new service points require scalable hardware and software. and business models. A more detailed description of the On the other hand, prominent challenges of reliability in edge requirements is given below. computing infrastructure might arise because of its dynamic nature, software flaws, hardware flaws, and network failure. To cope with the system failures, we must use edge computing A. Scalability and Reliability based infrastructures within smart cities which are resilient How will edge computing enabled smart cities infras- to hardware and software flaws. Other way is to use duplicate tructure simultaneously respond to a gigantic increases in the service providing points. However, this will cause a significant numbers of end-user devices and system failures? increase in the cost of a system. Moreover, this approach adds In smart cities, we will witness massive increases in the complexity to the system because of its distributed nature. numbers of actuators, smart sensors, and other smart devices, As the edge computing is mainly intended to enable strict subsequently resulting in unprecedented growth in data traffic. latency applications. Therefore, the addition of duplicate nodes To enable the resource-intensive and latency sensitive smart must be immediate, which is very challenging due to their city applications, edge computing provides a solution. To distributed nature. Other ways to ensure reliability include the create a scalable edge computing infrastructure, we must use of robust hardware, fault-tolerant networking protocols, consider reliable performance for an increasing number of and robust software. end users, dynamic smart devices, and diverse networks [13]. The open research challenge of collaborative edge com- Scalability in edge computing infrastructure can be achieved puting is derived from this section. The authors in [150] and 20

[151] considered collaborative edge computing to offer scala- communication, caching, and computation at the network bility and reliability. Edge computing servers are characterized edge. Therefore, it is recommended to use federated learning at by a low computing capacity that must be utilized efficiently. the network edge for resource management in different smart Additionally, there are significant variations in smart device environments. traffic. To cope with variations in the massive amount of smart devices traffic along with scalability and reliability constraints, the use of collaborative edge computing is a viable solution. D. Sustainability B. Context-awareness How does one develop edge computing enabled smart How does one enable smart cities with mission-critical cities without losing QoS and adding pollution to the environ- applications such as smart health-care services and smart ment? transportation services using edge computing? Sustainability is another important requirement when To answer this question, the edge server must be context- designing edge computing infrastructures for smart cities. aware (such as it must know the locations of smart devices) In smart cities, the densification of end-user devices and to enable computation with strict latency [152]–[155]. More- edge computing servers is expected. Consequently, this puts over, the edge server requires knowledge of network load significant energy limitations on the overall smart city in- and capacity to enable resource-efficient communication with frastructure design. Therefore, it is imperative to tackle the smart devices. For example, if an accident occurs in a smart challenge of high energy consumption without degrading QoS. transportation system, then it is necessary to respond as soon The three main aspects are related to sustainability of the as possible for first aid. The arrival of first aid services is edge computing enabled smart cities: energy efficient design heavily dependent on the user emergency call. A severely [167]–[170], use of renewable energy sources [86], [171], injured user might not be able to call for emergency service. [172], and energy harvesting [173]–[176]. The edge computing On the other hand, context-aware edge servers at RSU can enabled smart cities infrastructure is designed to reduce energy enable reporting of emergencies in a timely manner with very consumption by employing a novel architecture design and low latency. Therefore, it is evident that context-awareness is communication technologies. Numerous ways to improve the one of the key requirement in the design of edge computing energy efficiency of edge computing system design include enabled smart cities infrastructure. energy-efficient caching [167], energy-constrained offloading and sleeping [168], and energy-aware computation offload- C. Resource Management ing and user association [170]. Furthermore, energy-efficient How does one effectively manage computation, commu- wireless networking technologies, such as IPv6 over low- nication, and storage resources between different players in power WPAN (6LoWPAN) and ZigBee can be used to enable smart cities to optimize the overall system performance? sustainable operation of a system [177]. In [178], Gai et To answer this question, we must devise resource man- al. considered cognitive wireless communication that uses agement schemes that consider energy [156]–[159], latency reinforcement learning and edge computing to enable energy [160], traffic handling capacity [63], and user-defined utility efficient operation. Other than energy efficient design, an [161] to optimize the overall performance of the system [162]. edge computing ecosystem that utilizes energy from the fossil However, it is very difficult to simultaneously consider all of fuels-based power plants will add to the carbon footprint. these factors when designing a resource management scheme. Therefore, an edge computing infrastructure must utilize en- There are three main categories of resources in edge comput- ergy from renewable energy sources such as wind, solar, ing enabled smart cities: computing resources, communication and hydropower. Apart from that, energy can be harvested resources, and storage resources [163]. To enable cost-efficient either from environmental sources, such as wind and sun operation with better QoS, resource management algorithms [173], [174] or radio frequency sources, such as from ra- must be adaptive and perform management according to an dio frequency transmitters and interference signals [175], applications nature. For example, more computing resources [176], [179]. Energy harvesting offers significant advantages must be given to a self-driving car than AR to enable faster but suffers from random variations in both environmental execution of the self-driving car functions to avoid accidents and radio frequency energy. Therefore, we must use hybrid [164]. On the other hand, the throughput requirement of AR sources that also utilize harvesting energy and grid energy is larger than for self-driving cars [165]. Therefore, a trade- on demand if the harvested energy level falls below the off must be made between computation and radio resource required energy. From this section, we derived the adoption allocation. of high-performance memory requirement for sustainable and We derived that there must be novel resource optimization reliable operation. The edge servers are intended for use in schemes based on AI for edge computing-based smart environ- multiple diverse applications to serve massive number of users. ments. It is expected that a typical 5G node will have more Moreover, edge servers are also expected to enable support than 2000 configurable parameters [93]. It is recommended for computationally expensive machine learning algorithms. in [166] to use AI for data link and physical layer. In Therefore, use of high-performance memory is one of the addition, deep reinforcement learning and federated learning promising solution for enabling sustainable edge computing based framework has been proposed in [93] for managing enabled smart environments. 21

E. Elasticity Security in edge computing infrastructure has two main How does one enable smart citizens with services in an aspects: devices cyber security and physical security. Cyber elastic manner to avoid both under and over-utilization of security refers to protection of the network, computing in- resources? frastructure, and data from attacks. Specifically, data security Unprecedented increases in the numbers of sensors and is the protection of data from unauthorized access, whereas other smart devices are expected in smart cities. This will privacy can be considered as legislation (a set of rules) that impose limitations on the available resources, such as com- defines the levels of protection. Privacy places restrictions puting, communication, and storage resources. Therefore, it is on the authorization of users regarding data access and the necessary to design systems that permit efficient utilization of purpose of data usage. Apart from privacy, security must the resources. Elasticity in edge computing enables to avoid be properly addressed in smart cities. At the core of edge over-allocation and under-allocation of computing resources. computing paradigms, there are many different technology This can be realized in smart city infrastructure by providing enablers, i.e., virtualization, wireless networks, peer-to-peer, elastic services based on the allocation of resources with the and distributed systems. Therefore, enabling physical security ability to grow and shrink dynamically according to demand of edge devices is necessary and very challenging (due to [180]–[182]. For elasticity, it is necessary to properly predict the distributed nature of the edge nodes). An unauthorized user demand via edge service providers prior to assignment user can easily physically access any of the edge devices of computational resources, such as bandwidth, processing and install malicious software. Other than physical security, power, and storage. Other than prediction, edge computing- effective cryptographic techniques can be used to provide based smart services must be elastic. In [183], a proactive improved security against the attacker. Furthermore, security scheme is presented to enable auto-scaling by predicting future mechanisms in edge computing ecosystems can be either resources based on past data. Auto-scaling techniques can be centralized or decentralized based on continuous dependence either vertical, horizontal, or hybrid [184]. Vertical scaling on the centralized infrastructure. Centralized security requires refers to re-sizing of resource allocation on the same node, continuous availability of the centralized infrastructure. Al- whereas horizontal scaling is re-sizing of resource allocation though this has the advantage of easier management it is more among a cluster of nodes cluster. A hybrid approach combines vulnerable to failure by an attack on the centralized server. both vertical and horizontal schemes. On the other hand, a decentralized security mechanism has The open research challenge of network slicing-based the advantages of reduced delay and being less vulnerable to smart city is derived from this section. In a typical smart failure by attacks. However, such a mechanism has a higher city, we will expect a wide variety of players: Edge ser- complexity than centralized algorithms due to its distributed vice providers, cloud service providers, telecommunication nature. infrastructure providers, among others, that interact with each We derived from this section that the use of blockchain in other to offer different smart services. Enabling successful edge computing-based smart cities is necessary. A blockchain interaction between these service providers and smart citizens is a type of distributed ledger technology (DLT) that of- that jointly optimize users QoS and service providers profit fers several striking features of decentralization, transparency, can be made possible via network slicing. The network slicing democracy, enhanced security, and immutability [2]. These providers will buy resources from the physical infrastructure promising features of blockchain made its use favorable in providers and sell it back to smart citizens to increase the secure and transparent sharing of data by citizens for enabling providers profit and improve users experience. different smart applications. However, blockchain suffered from the scalability barrier with an increase in the number of F. Interoperability transactions [185]. To cope with this challenge of scalability, edge computing is a promising solution by providing on- How does one operate the massive number of heteroge- demand computational resources and storage. However, the neous devices in edge computing enabled smart cities? integration of edge computing and blockchain in a smart city Interoperability is the ability of different systems to further brings challenges that must be resolved. understand and utilize each others functions. Similar to IoT, interoperability is one of the most important requirements in the design of the edge computing enabled smart city infrastructure. H. Business Model The challenge of interoperability in edge computing enabled How does one increase revenue of the edge computing smart city infrastructure arises due to massive number of service providers while maintaining user QoS? heterogeneous devices running different protocols. To enable Smart cities enable a plethora of horizontal and vertical seamless operation, edge computing architectures must be able use cases that have diverse requirements. Additionally, the to provide support for interoperability. Interoperability can cities involve different players, such as end users, cloud be implemented in smart cities by exploiting inter-operable providers, edge computing service providers, and communi- interfaces and open source frameworks. cation infrastructure providers, interplay with each other. The already available business models for cloud computing are not G. Privacy and Security feasible because of the distributed nature of edge computing. How does one preserve user privacy and data from attacks Therefore, to commercially deploy the edge computing infras- by malicious users? tructure, the existing service providers will have to adopt new 22 pricing models to generate more revenue while maintaining a this project are Future Intelligence Ltd. London, and London better QoS. We derived from this section that novel business South Bank University, United Kingdom. The goal of this models must be developed for edge computing enabled smart project is to exploit novel micro-data center and telecom- cities. The already developed business models for cloud communication nodes to provide joint storage resources, local puting are insufficient for edge computing enabled smart cities processing, and networking to enable support for user applica- due to their distributed architecture. tions in smart cities. Further, they will utilize software-defined networking and Network Function Virtualization (NFV) to VI.CASE STUDIES enable the formation of interconnected devices islands. This This section presents recently reported synergies and case resulted in fogs, which are actually small scale clouds at the studies of edge enabled smart cities. A summary of these case network edge (i.e. edge computing). The main objective of studies listing the goals, the organization involved, deployment this project is improvement of smart surveillance by leveraging status, and country is given in the section. edge computing.

A. Barcelona smart city D. Lessons Learned: Summary and Insights Barcelona smart city is a joint project of Barcelona City This section presented the recently reported case studies Council, Prismtech, i2cat, Technical University of Catalo- of edge computing enabled smart cities. We discussed the ob- nia, Barcelona Supercomputing Center, Schneider Electric, jectives, organizations involved, and their current deployment Plat.one, Sensefields, and CISCO to realize smart services status. From this section, we concluded that the development [186], [187]. The project partners focused on five use cases that of high-performance simulation tools for implementation and include power monitoring/element control, access control and validation of edge computing based smart cities applications cabinet telemetry, event-based video, traffic management, and is required to be done. For instance, the IoTIFY is an online on-demand connectivity. To realize a smart city, installation cloud based network simulator that enables simulation of of large number of IoT driven devices is required, further smart waste management, smart parking spots, smart street creating operational and space problems. In this regard, the lights, smart traffic signals, and smart transportation [190]. number of deployed cabinets is more than 3000 for Barcelona We must develop novel high-performance simulators to test smart city realization. On the other hand, fog computing is edge computing and other emerging technologies in smart city validated to enable real-time decision making, power supplies environments. for autonomous operation, guaranteed access control, local data analysis, and execution of complex algorithms for the VII.OPEN RESEARCH CHALLENGES assistance of vehicles flow. This section presents open research challenges in the realization of edge computing in smart cities. Furthermore, B. #SmartME a summary of these challenges, their causes, and possible #SmartME is a project provisioned to transform the city guidelines is given in table VI. of Messina into a smart city [188]. The project was initiated by a group of researchers from the University of Messina. A. Intelligent Caching The key goals of the #SmartME project are establishment In smart cities, connectivity of billions of smart IoT of a smart city infrastructure that allows all citizens to devices is expected. According to CISCO, 507.9 ZB data will contribute to infrastructure through hardware sharing. The be generated by smart IoT devices in 2019 [45]. This rapid #SmartME framework consists of three-layer: an applica- rise in data has resulted in a major bottleneck that requires tion layer, Stack4Things layer (for fog computing platform higher back-haul data rates. To tackle the dilemma of back- implementation), and city layer. The Stack4Things layer is haul congestion, caching can be used which deals with the a framework developed at the University of Messina with storage of popular contents at different locations in a network the objective of enabling the administrators to manage IoT to avoid repeated transmissions [191]. Additionally, caching devices without taking care of their physical location. Main enable different real-time IoT-based smart city applications by features of the Stack4Things include object virtualization, reducing latency and energy consumption [192]. On the other overlay network of things, remote control and customization, hand, caching has two main aspects: network type and caching and fog orchestration. The applications developed on the top strategy. Network type deals with cache positioning, such as of the #SmartME infrastructure include #SmartME Parking, cache enabled macro cellular networks [128], cache enabled #SmartME lighting, #SmartME pothole, #SmartME airport, heterogeneous networks [193], cache enabled D2D networks #SmartME taxi, #SmartME art, and #SmartME trashcan. [194], and cache enabled cloud radio access networks [195]. The caching strategy has two main phases, such as cache C. WATCH decision phase and content delivery phase. The Wide Smart Safe, Robust and Resilient Smart Cities The main challenges that exist in intelligent caching at the Application Using Fog Computing (WATCH) project is funded edge include the cold-start user problem [196] and security and by INNOVATE UK [189]. It was started on Nov 01, 2017 and privacy [197]. Intelligent caching based on machine learning is expected to be completed by 2020. The lead participants of uses information and data from all users within its range. 23

However, the mobility of the users poses many challenges usage subject to the constraints of server resource availability, and it is very difficult to satisfy this requirement for intel- tasks computation deadlines, and local computation capabili- ligent caching. To cope with user mobility, mobility-aware ties. Apart from formation of the collaboration space, we must hierarchical caching was proposed in [125]. Furthermore, we design social trust-based incentive policies and cooperation can leverage reinforcement learning for effective scheduling in policies. Within a collaboration space, only edge computing mobility-aware caching [198]. Additionally, intelligent cache nodes with social trust can share resources with each other decisions are based on the future content popularity, whose based on incentives in the form of payment. A coalition game computation is considerably challenging. The prediction of was used in [205] to design pricing incentive based on trust for content popularity using deep learning has been considered to collaboration in ultra-dense networks. Additionally, mobility enable intelligent cache decision making [196], [199]–[201]. must be managed properly within a collaboration space to enable efficient resource usage. The design of collaboration algorithms regarding the mobility of resource deficient nodes, B. Collaborative Edge Computing such as autonomous vehicles and UAVs, is challenging. Virtually enabled edge computing servers are charac- On the other hand, we must design novel algorithms for terized by low computation and storage capacity constraints intra-domain and inter-domain collaboration among edge com- [202], [203]. They use containers and virtual machines to puting nodes. Inter-node collaboration occurs between mobile enable different smart applications. Apart from that, edge edge computing servers, cloudlets, and fog nodes, whereas, computing servers can leverage dimensional parameters, such intra-domain collaboration involves similar edge computing as server number, server capacity, and server operation area nodes, such as mobile edge computing nodes, fog nodes, or to optimize the overall performance in terms of QoS and cloudlets. Enabling intra-domain collaboration is easier than operator profit. On the other hand, a tremendous increase intra-node collaboration due to the homogeneous nature of in the number of smart devices is expected in the future. edge computing nodes. Apart from inter-domain and intra- Consequently, this poses significant challenges pertaining to domain collaboration, smart networking technologies must be the scalability and QoS of smart city infrastructure. To enable designed to enable latency sensitive and energy optimized col- scalable and resource-optimized operation of edge computing laboration. In [151], the design of a smart internet architecture in 5G enabled smart cities, we can leverage collaborative allowing smart collaboration was proposed. Furthermore, deep edge computing. Collaborative edge computing aims to form learning and game theoretic approaches can be used to enable a collaboration space between edge computing servers in smart devices, edge servers, and other networking devices to close vicinity. Moreover, this enables the maximization of perform traffic flow control collaboratively [206]. resource usage, latency minimization, and reduction of back- haul traffic. Collaboration in edge computing enabled smart city infrastructure can be categorized into horizontal and C. Cooperative and Sustainable Load Balancing vertical collaboration [150]. Horizontal collaboration involves Smart device data exhibit significant temporal and spatial cooperation among different edge computing nodes that exist traffic variations resulting in possible congestion at edge simultaneously in the same edge computing layer. Vertical computing enabled AP/BS [207]. Consequently, this causes collaboration involves collaboration among different layers, performance degradation pertaining to higher latency. More- such as the smart devices layer, edge computing layer, and over, offering edge computing servers with dynamic loads remote cloud computing layer. Although vertical collaboration causes under-loaded and overloaded scenarios. As a result, may add additional latency to the tasks that are offloaded substantial QoS performance degradation and profit loss of to the edge layer, it offers significant advantages. One of edge service providers will occur. Additionally, the smart these advantages is the capability to process flooded deep cities infrastructure requirement of high scalability is also learning queries for enabling different smart applications by adversely affected by under-utilization and over-utilization. providing enhanced computing power. The edge computing Hence, it is necessary to perform adequate load balancing. On layer generally cannot handle flooded deep learning queries the other hand, edge computing servers consist of hardware due to its low-computational power [204]. Therefore, the resources, such as main memory and secondary storage which flooded deep learning queries must be processed at the remote are virtualized [208]. Virtual machines are then assigned to cloud. Another advantage of the vertical collaboration between requested applications. Every virtual machine operation is the edge layer and cloud layer is the preprocessing of raw data characterized by two states, the idle state and the active state. at the edge layer before sending it to the cloud layer for further The energy consumption of the idle state is approximately analysis. 60% that of the active state. Therefore, it is necessary to The major challenges that exist in collaborative edge avoid under-utilization of edge computing servers to enable computing include collaboration space formation, social trust- sustainable operation. based incentive policies, cooperation policies, inter-domain Numerous challenges exist for cooperative and sustain- cooperation, intra-domain cooperation, smart collaborative able load balancing, including novel pricing incentive design, networking, and mobility management. Collaboration space joint minimization of task dropout and task transfer, and secure formation must be adaptive and based on resource availability. authentication. In cooperative load balancing, the load balanc- Furthermore, within the collaboration space, collaboration ing between different edge computing servers involves nego- schemes must be designed that minimize back-haul bandwidth tiation between the involved players. To enable cooperation 24

TABLE VI: Summary of the research challenges and their guidelines

Challenges Causes Guidelines Intelligent caching • Data sparsity due to mobility • Deep learning assisted cache content prediction • High back-haul congestion • Reinforcement learning based mobility-aware • High latency in content delivery caching

Collaborative edge computing • Edge server inherent low computation and storage • Horizontal edge computing collaboration capacity constraints • Vertical edge computing collaboration • Proﬁt and QoS dependency on size, number, and • Inter-domain collaboration coverage area of edge servers • Intra-domain collaboration • Absence of high scalability and reliability • Smart collaborative networking • Social trust based incentive design for collaboration

Cooperative and sustainable load balancing • Under-loaded scenarios • Novel game theory assisted incentive mechanisms • Over-loaded scenarios • Adaptive cooperation mechanisms • Substantial energy wastage • Novel authentications based load balancing • Proﬁt margin reduction

Intelligent edge • Absence of instant big data analytics • AI based predictive and diagnosis analytics • High latency in instant and autonomous decisions • Machine learning based semantic inter-operable • Semantic non-interoperability interfaces • Novel privacy regulation rules for personal data sets

Network slicing • Diverse requirements of smart city applications • Separation of control plane from data plane • Lack of effective resource management of multiple • End-to-end slice management and orchestration service providers • SDN-based orchestrator security • Slice security challenges • Adaptive service function chaining

Cyber and physical security • Privacy leakage • Lightweight authentication schemes • Denial of service • Blockchain-based authentication schemes • Service manipulation • Certiﬁcateless public key cryptosystem-based au- • Multiple trust domains thentication scheme • SDN and NFV assisted security • Attribute based access control • Federated capability-based access control

between different edge computing servers, we need effective D. Intelligent Edge Computing incentive mechanisms. Design of the incentive mechanism A massive amount of data will be generated by smart depends on the nature of the edge server strategy, such as devices and sensors for real-time smart city applications that an open strategy or sealed strategy. In an open strategy, the require instant processing. Most of these data contain noise edge servers are allowed to share their resource information, and only a small portion of it is useful. The challenge is whereas the resource information of the edge servers is only how to separate large useful data sets (such as big data) from available to service providers in a sealed strategy. Game theory noisy data. A viable solution to this challenge is the use can be utilized in the design of an incentive mechanism to of AI in the edge. Intelligent edge computing can perform enable cooperative load balancing in edge computing [209]. data filtering to yield only useful data before sending to a The actions of the three main players of the edge computing ( remote cloud. Moreover, it can also offer real-time processing i.e., service providers, users, and edge servers) can be used to of data. Intelligent edge computing offers real-time analytics model various game theory parameters, such as the action set that enable us to answer questions, such as Why are some and preference set. Apart from incentive mechanism design, things happening and What should be done. Additionally, the cooperation mechanism must be adaptive and offer the it enables process analysis and prediction of future trends. ability to trade-off between task dropout and task transfer More specifically, the use of instant analytics at the edge is [210]. Furthermore, security must be considered in the design necessary to enable big data analytics. On the other hand, of cooperative and sustainable load balancing [211]. semantic interoperability between different edge computing enabled systems is one of the major bottlenecks regarding the exchange of information to enable effective training of the machine learning algorithms [212]. Intelligent edge computing 25 is a way of providing interoperability among non-interoperable theoretic, deep reinforcement learning, and auction theory- systems. Additionally, intelligent edge computing offers se- based schemes can be used [219]–[221]. Apart from manage- curity enhancement by enabling smart applications to avoid ment and orchestration of slices, slice security is another key sending data to remote clouds for predictive analytics. challenge in realization of network slicing in smart cities. The The challenges relevant to developing intelligent edge presence of numerous authorities and multiple stakeholders computing based on AI include its high computational power, in network slicing pose several novel security challenges. complex data acquisition and storage, user privacy, and inter- Network slicing security involves challenges of multiple op- operability. The execution of traditional AI algorithms requires erator resource sharing security and SDN-based orchestrator high computational power. However, the computational power security. The slices defined for different use cases have unique at the edge is lower than that of the remote cloud. Therefore, security requirements. For instance, a smart health-care slice it is necessary to devise low-complexity learning algorithms. has stronger security concerns than the infotainment slice. Apart from its high computational power, data acquisition Therefore, we must consider different security mechanisms and storage of smart devices and sensors in various smart for individual slices. Overall, security must be ensured during environments is also challenging. The humongous data sets resource sharing of the infrastructure. However, network wide- used for AI algorithm validation require high storage capacity. security has different nature than of individual slices security. In addition, the data set might contain noisy or missing data, Therefore, the development of novel and effective security which must be handled properly. Another challenge in training policies for network resource sharing and individual slices is AI algorithms is the integration of large data sets. Integrating necessary. and synthesizing large data sets from the different players in On the other hand, SDN-based orchestrator attacks in- a smart environment for effective AI algorithm training has a volve unauthorized access by a malicious user. An authorized very high computational complexity. To cope with this issue, user can access only a single network orchestrator but still an inter-operable system is a solution that enables systems alter the network resources of multiple operators. Therefore, to interact with each other and share data. In [212], an meticulous attention must be given to SDN-based orchestrator edge computing-based semantic gateway has been proposed security. Apart from SDN-based orchestrator security, another to enable inter-operability in health-care systems. To enable core challenge for network slicing is adaptive service function semantic interoperability machine learning-based interoperable chaining. The key driver of network slicing, NFV allows im- interfaces can be used. On the other hand, maintaining user plementation of virtual network functions on virtual machines. privacy while utilizing data sets for training AI algorithm To deliver a service, a service function chain can be considered pose challenges. For instance, consider an edge computing that leverages one or more virtual network functions in a enabled smart health-care system with patient personal data. sequence. The design of an adaptive service function chaining The privacy of users must be maintained via effective security mechanism that jointly reduces the network deployment cost mechanisms. Moreover, effective privacy regulation must be and maximizes user QoS is necessary. In [222], traffic-aware devised to ensure the user privacy. and energy-aware virtual network function placements are considered. A solution based on a matching game has been proposed to enable efficient placement of virtual network E. Network Slicing functions. In smart cities, a wide range of players including cloud server providers, edge computing service providers, telecommunication services providers, and IoT service providers, will F. Cyber and Physical Security interplay with one another. The goals of these service providers In edge computing enabled smart cities, the connectivity are to increase profit, whereas, users want to improve their of a massive number of smart devices is expected, which QoS. To enable easier and efficient resource management can be prone to serious security attacks. The different areas among users and service providers, the concept of network prone to security attacks include smart devices, network in- slicing was introduced [213]–[217]. Network slicing exploits frastructure, edge computing servers, and core infrastructure. software-defined networking (SDN) and NFV to separate the These security attacks can be either physical or cyber attacks. control plane from the data plane [214]. The concept of Physical security in edge enabled smart cities is difficult to network slicing is based on the creation of logical networks maintain due to the distributed nature of edge computing. A on top of a physical network infrastructure. malicious user can easily access the edge nodes and perform To enable network slicing in smart cities, it is imperative malicious activity. Therefore, enabling edge computing with to tackle the challenges, such as end-to-end slice manage- high cybersecurity is of primary importance. ment and orchestration, slices security, and adaptive service The challenges that exist in enabling effective security function chaining. A network slicing architecture consists of of edge computing enabled smart cities includes network different layers: a service layer, resource layer, and network security, effective authentication, and access control. Authen- slice instance layer [218]. End-to-end slice management and tication offers the ability to determine user validity by sharing orchestration spans all three layers and handles the creation identification information between two parties. Traditional and management of slices. Moreover, it deals with translation cryptography-based authentication schemes can be used to of all the smart city use cases into slices with its associated devise authentication schemes for edge computing [223]. How- network functions. To enable effective network slicing, game ever, the high computational complexity associated with these 26

authentication techniques limits its application to edge com- VIII.CONCLUSIONSAND FUTURE PROSPECTS puting. Indeed the computational resources are more limited A. Conclusions in edge computing than cloud computing. Therefore, we must Edge computing is a promising computing paradigm to devise novel light weight authentication schemes with low enrich smart cities with instantaneous computing and storage complexity [224], [225]. In [226], a Lightweight Anonymous resources. Typically, cloud computing is employed to offer Mutual Authentication scheme for ntimes Computing Offload- smart devices with computation and storage resources. How- ing (LAMANCO) was proposed for anonymous authentica- ever, inherent delay of cloud computing has paved the way tion between IoT devices and edge devices. A certificateless for migrating the computing and storage resources from a public key cryptosystem-based authentication scheme for edge centralized remote location to the edge of the network. On the computing enabled autonomous driving has been proposed other hand, real-time smart city applications require instant in [147]. Other than that, we can use energy-aware RIFD- analytic services. To enable these real-time applications, the based authentication schemes for edge computing-based smart use of edge computing is required. However, implementation environments [148]. In [227], a novel secure key management of edge computing in smart cities poses significant challenges. and user authentication scheme called as SAKA-FC has been In this survey, the adoption of edge computing in smart presented. The key advantage of using SAKA-FC is the use cities is comprehensively studied. To this end, we first pre- of lightweight operations, such as bitwise exclusive-OR and a sented the evolution of edge computing, discussing the gradual one-way cryptographic hash function. In [228], a blockchain- evolution of computing technologies towards edge computing. based authentication scheme has been proposed to offer the In addition, the technologies involved and the benefits of key features of trust, anonymity, and decentralization. Other different computing paradigms are also presented. Second, than authentication, network security is another important significant recent advances are presented and a rigorous eval- aspect of smart city infrastructure. Emerging communication uation is performed using different assessment parameters. technologies such as Sigfox, LoRaWAN, ZigBee, 6LowPAN, Apart from recent advances, we have classified the literature WAVE, and 5G used in smart cities have their own security and devised a taxonomy according to different parameters, protocols. However, there is room for further research regard- such as edge analytics, edge intelligence, resources, caching, ing network security in the context of edge computing. Session resource management, characteristics, sustainability, and secu- keys are negotiated using the distribution of credentials and rity. Additionally, numerous requirements for enabling smart novel and effective schemes are needed to distribute these cities via edge computing are proposed. We also reported a credentials. Furthermore, SDN andNFV can be used to enable few case studies of enabling edge computing in smart cities. secure networking. Specifically, they can be employed for Furthermore, numerous open research challenges are discussed isolation of different types of traffic [42]. They allow for traffic in detail, and causes and possible guidelines to cope with these diversion from insecure devices towards secure devices. challenges are summarized.

B. Future Prospects We expect network slicing to be widely adopted in Similarly, access control in edge computing must be given developing smart cities in the foreseeable future. Currently, considerable attention. Any edge devices after authentication, millions of devices are deployed in cities. In the future, with certain privileges, can access the virtualized edge servers the addition of trillions of smart devices and sensors is and misuse them. Additionally, the existence of multiple trust expected [234]. All of these devices generate data traffic domains associated with multiple service providers in a single which requires instant analytic services. Edge computing can ecosystem pose challenges for developing effective access enable instant analytics by providing on-demanding computing control schemes. Attribute-based access schemes can be used capabilities with extremely low latency. Furthermore, novel to restrict access control in edge computing [197], [229]. communication technologies with high throughput are required However, the major downside of attribute-based schemes is for data transmission between end devices and edge servers. the high complexity associated with the decryption phase. In On the other hand, the development of smart cities must be [230], an efficient access control scheme (namely CP-ABE) sustainable and reliable. To enable smart cities through novel was proposed for fog computing with outsourcing capability. technologies, different players such as telecommunication In [231], Gai et al. proposed a Dynamic Privacy Protection network operators, edge computing service providers, cloud (DPP) model based on dynamic programming that is used service providers, and the IoT service providers will interplay to enable privacy for mobile devices. The DPP model offers with each other. One main issue is how to effectively enable significant maximization of privacy weights and thus, might such interplay to maximize profit and improve QoS? Network be suitable for use in several edge computing enabled smart slicing is an answer, which can be leveraged to improve environments in the future. Additionally, a file hierarchy QoS and increase profit. This allows creating multiple logical attribute-based encryption scheme was proposed in [232] to networks (called slices) on top of a physical infrastructure enable fine-grained access control for fog computing. On the of different service providers [214], [235]–[237]. The key other hand, [233] proposed federated capability-based access enablers of network slicing enabled smart cities are SDN and control for IoT to offer effective access control with high NFV. SDN offers separation of the control plane from the scalability compared to attribute-based schemes. data plane, and NFV allows the use of generic hardware for 27

TABLE VII: List of abbreviations resulting in a cost-efficient operation. Allocation of an end-to- Abbreviation Word end network for a typical slice would be expensive. Therefore, it is necessary for multiple slices to coexist simultaneously. AP Access Point However, resource management of resources between co- AR Augmented Reality existing slices is challenging, thus requiring novel resource ARPA Advanced Research Projects Agency Network management schemes. Different organizations are also at- AI Artificial Intelligence tempting to enable smart applications via network slicing. For BS Base Station instance, consider reference [238], which introduces a network slicing-based testbed using edge computing at Hamburg port. CAGR Compound Annual Growth Rate Additionally, many organizations are working on the AutoAir CIoT Consumer Internet of Things project in the UK to produce a testbed based on edge comput- DARPA Defense Advanced Research Projects Agency ing and network slicing for testing connected and autonomous D2D Device-to-Device vehicles. To sum up, edge computing and network slicing will ECG Electrocardiography be the dominant solution for enabling future smart cities. eNB Evolved Node B APPENDIX ETSI European Telecommunications Standards Institute Abbreviations IaaS Infrastructure-as-a-Service IoT Internet of Things See table VII. IIoT Industrial Internet of Things REFERENCES IAR Industrial Augmented Reality [1] E. Ahmed, I. Yaqoob, A. Gani, M. Imran, and M. 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[237] S. Zhang, W. Quan, J. Li, W. Shi, P. Yang, and X. Shen, “Air-ground Nguyen H. Tran (S’10-M’11-SM’18) is currently integrated vehicular network slicing with content pushing and caching,” working as a senior lecturer in the School of Com- IEEE Journal on Selected Areas in Communications, vol. 36, no. 9, pp. puter Science, The University of Sydney. He re- 2114–2127, Sep. 2018. ceived his BS degree from Hochiminh City Univer- [238] P. Rost, M. Breitbach, H. Roreger, B. Erman, C. Mannweiler, R. Miller, sity of Technology and Ph.D. degree from Kyung and I. Viering, “Customized industrial networks: Network slicing trial Hee University, in electrical and computer engineer- at hamburg seaport,” IEEE Wireless Communications, vol. 25, no. 5, ing, in 2005 and 2011, respectively. He was an pp. 48–55, October 2018. Assistant Professor with Department of Computer [239] S. Chaabnia and A. Meddeb, “Slicing aware qos/qoe in software Science and Engineering, Kyung Hee University, deﬁned smart home network,” in IEEE/IFIP Network Operations and Korea from 2012 to 2017. His research interest is to Management Symposium, Taipei, Taiwan, 2018, pp. 1–5. applying analytic techniques of optimization, game [240] M. Boussard, D. Thai Bui, R. Douville, P. Justen, N. Le Sauze, theory, and stochastic modeling to cutting-edge applications such as cloud and P. Peloso, F. Vandeputte, and V. Verdot, “Future spaces: Reinventing mobile edge computing, data centers, heterogeneous wireless networks, and the home network for better security and automation in the iot era,” big data for networks. He received the best KHU thesis award in engineering Sensors, vol. 18, no. 9, p. 2986, 2018. in 2011 and best paper award at IEEE ICC 2016. He has been the Editor of [241] A. E. Kalør, R. Guillaume, J. J. Nielsen, A. Mueller, and P. Popovski, IEEE Transactions on Green Communications and Networking since 2016, “Network slicing in industry 4.0 applications: Abstraction methods and served as the Editor of the 2017 Newsletter of Technical Committee on and end-to-end analysis,” IEEE Transactions on Industrial Informatics, Cognitive Networks on Internet of Things. vol. 14, no. 12, pp. 5419–5427, Dec 2018.

S. M. Ahsan Kazmi is with the Institute of Informa- tion Systems (IIS), Innopolis University, Innopolis, Tatarstan, Russia, where he is currently working as an Assistant Professor. He received his Master degree in Communication System Engineering from the National University of Sciences and Technology (NUST), Pakistan, in 2012, and the Ph.D. degree in Computer Science and Engineering from Kyung Hee University (KHU), South Korea. His research Latif U. Khan is currently pursuing his Ph.D. degree interests include applying analytical techniques of in Computer Engineering at Kyung Hee University optimization and game theory to radio resource (KHU), South Korea. He is working as a leading management for future cellular networks. researcher in the intelligent Networking Laboratory under a project jointly funded by the prestigious Brain Korea 21st Century Plus and Ministry of Science and ICT, South Korea. He received his MS (Electrical Engineering) degree with distinction from Tri Nguyen Dang is currently pursuing his Ph.D. University of Engineering and Technology (UET), in Computer Science and Engineering under fully Peshawar, Pakistan in 2017. Prior to joining the funded scholarship at Kyung Hee University, South KHU, he has served as a faculty member and re- Korea. He received his B.S. in Information Teach- search associate in the UET, Peshawar, Pakistan. He has published his works ing from Hue University’s College of Education, in highly reputable conferences and journals. His research interests include Vietnam in 2014. His professional experience in- analytical techniques of optimization and game theory to edge computing and clude Mobile Application Developer, middle-ware end-to-end network slicing. programming. He had won consolation prize of Olympiad in Informatics ACM ICPC Vietnam in 2013. His research interest includes Network Op- timization, and Mobile Cloud computing, Mobile Edge Computing, Internet of Things.

Choong Seon Hong (S’95-M’97-SM’11) received the B.S. and M.S. degrees in electronic engineering from Kyung Hee University, Seoul, South Korea, in 1983 and 1985, respectively, and the Ph.D. degree Ibrar Yaqoob (S’16-M’18-SM’19) is a research from Keio University, Minato, Japan, in 1997. In professor with the Department of Computer Science 1988, he joined Korea Telecom, where he worked and Engineering, Kyung Hee University, South Ko- on broadband networks as a Member of Technical rea, where he completed his postdoctoral fellowship Staff. In September 1993, he joined Keio University. under the prestigious grant of Brain Korea 21st He worked for the Telecommunications Network Century Plus. Prior to that, he received his Ph.D. Laboratory, Korea Telecom, as a Senior Member of (Computer Science) from the University of Malaya, Technical Staff and the Director of the Networking Malaysia, in 2017. He worked as a researcher and Research Team until August 1999. Since September 1999, he has been a developer at the Centre for Mobile Cloud Computing Professor with the Department of Computer Science and Engineering, Kyung Research (C4MCCR), University of Malaya. His Hee University. His research interests include future Internet, ad hoc networks, numerous research articles are very famous and network management, and network security. He is a member of ACM, IEICE, among the most downloaded in top journals. He has reviewed over 200 times IPSJ, KIISE, KICS, KIPS, and OSIA. He has served as the General Chair, a for the top ISI- Indexed journals and conferences. He has been listed among TPC Chair/Member, or an Organizing Committee Member for international top researchers by Thomson Reuters (Web of Science) based on the number conferences such as NOMS, IM, APNOMS, E2EMON, CCNC, ADSN, ICPP, of citations earned in last three years in six categories of Computer Science. DIM, WISA, BcN, TINA, SAINT, and ICOIN. In addition, he is currently He is currently serving/served as a guest/associate editor in various Journals. an Associate Editor of the IEEE Transactions on Network and Service He has been involved in a number of conferences and workshops in various Management, International Journal of Network Management, and Journal of capacities. His research interests include big data, edge computing, mobile Communications and Networks and an Associate Technical Editor of the IEEE cloud computing, the Internet of Things, and computer networks. Communications Magazine.