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Emerging Edge Computing Technologies for Distributed Iot Systems

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Emerging Edge Computing Technologies for Distributed Iot Systems ACCEPTED FROM OPEN CALL Emerging Edge Computing Technologies for Distributed IoT Systems Ali Alnoman, Shree Krishna Sharma, Waleed Ejaz and Alagan Anpalagan ABSTRACT This remarkable increase in the number of con- nected devices needs to be accompanied by an The ever-increasing growth of connected equivalent increase in resource provisioning to avoid smart devices and IoT verticals is leading to the any sort of service disruption. Although the existing crucial challenges of handling the massive amount cloud computing paradigm is highly capable of han- of raw data generated by distributed IoT systems dling the massive amount of data, it is not suitable and providing timely feedback to the end-users. for distributed IoT systems due to the potentially Although the existing cloud computing paradigm incurred delays [2]. For this reason, providing com- has an enormous amount of virtual computing puting, storage, and communication functionalities power and storage capacity, it might not be able at the network edge helps not only in reducing the to satisfy delay-sensitive applications since com- end-to-end delay, but also can alleviate the burdens puting tasks are usually processed at the distant on cloud-servers and backhaul links. Furthermore, cloud-servers. To this end, edge/fog computing due to the physical proximity of edge devices with has recently emerged as a new computing par- end-users, edge computing can support distributed adigm that helps to extend cloud functionalities IoT applications that require location awareness and to the network edge. Despite several benefits of higher Quality of Service (QoS) [2, 3]. edge computing including geo-distribution, mobil- In contrast to the conventional IoT architec- ity support and location awareness, various com- ture, where storage and computing operations munication and computing related challenges are mostly performed in the cloud-center, distrib- need to be addressed for future IoT systems. In uted IoT systems incorporate nodes/gateways/ this regard, this article provides a comprehensive servers at the network edge to fulfill heterogenous view of the current issues encountered in distrib- IoT requirements with less delay and energy con- uted IoT systems and effective solutions by clas- sumption. However, edge nodes might not always sifying them into three main categories, namely, have sufficient computing and storage resources radio and computing resource management, intel- to process the massive amount of IoT data; there- ligent edge-IoT systems, and flexible infrastruc- fore, the cooperation between edge and cloud ture management. Furthermore, an optimization entities is indispensable to take the best of both framework for edge-IoT systems is proposed by computing paradigms [2]. In addition to providing considering the key performance metrics includ- computing capabilities at the vicinity of IoT users, ing throughput, delay, resource utilization and edge devices can perform various pre-process- energy consumption. Finally, an ML based case ing tasks such as data classification and filtration, study is presented along with some numerical service-level agreement ranking, and parameter results to illustrate the significance of ML in edge- measurements before involving the central-cloud. IoT computing. One of the crucial challenges in IoT systems is the limited radio resources required to provide reli- INTRODUCTION able connectivity to the massive number of devic- The next generation of Information and Com- es. Herein, one of the envisioned solutions to cope munication Technology is characterized by the with the scarcity of radio resources is to exploit and ubiquity of smart devices and machines that integrate all available communications, caching perform intelligent functions by autonomously and computing resources and radio access tech- sensing, analyzing, and exchanging information nologies (RATs) such as 5G, LTE and WiFi by using via the Internet. From E-health, smart homes and efficient resource allocation schemes. In addition, intelligent transportation to industrial manufac- harnessing large numbers of low-power small-cell turing and supply chain, Internet of Things (IoT) base stations (SBSs) can improve the cellular net- is intended to provide humanity with an easier, work capacity by allowing spatial frequency reuse safer and more intelligent lifestyle. However, the over small geographical areas. However, dealing rapid growth of IoT applications has increased the with systems characterized by such resource het- number of connected “Things” to unprecedented erogeneity requires sophisticated management levels. The number of connected devices is fore- and control schemes. To this end, implementing casted to reach about 125 billion (IHS Markit) by softwarized and virtualized platforms such as Soft- 2030, and Machine-to-Machine (M2M) commu- ware-Defined Networking (SDN) and Network nications, which constitutes a large proportion of Function Virtualization (NFV) technologies can sig- IoT applications, is expected to occupy almost 45 nificantly ease and automatize the entire network percent of the entire network traffic by 2022 [1]. control [4]. Digital Object Identifier: Ali Alnoman and Alaga Anpalgan are with Ryerson University; Shree Krishna. Sharma (corresponding author) is with the University of Luxembourg; 10.1109/MNET.2019.1800543 Waleed Ejaz is with Thomson River University. 140 0890-8044/19/$25.00 © 2019 IEEE IEEE Network • November/December 2019 Authorized licensed use limited to: Ryerson University Library. Downloaded on March 03,2020 at 17:48:05 UTC from IEEE Xplore. Restrictions apply. Most existing research works have focused on Domain Challenges Potential solutions centralized IoT systems without providing a high-lev- el coordination among the distributed communi- Delay Intelligent task offloading mechanisms cation and computing entities. Furthermore, SDN, BBU pool, distributed fog nodes, and NFV, big data analytics and artificial intelligence are Mobility usually introduced as application-specific technolo- Heterogeneity of IoT ad-hoc fogs gies/platforms rather than being adopted within a systems fundamental optimization framework. In this work, IoT authentication, access control, ML-based we aim to present comprehensive insights on dis- Security and privacy malware detection, pseudonymization tributed IoT systems by taking into account the chal- techniques, and secured task offloading lenges that encounter both radio and computing Insufficient computing Bi-directional resource sharing between elements. Moreover, effective potential solutions resources edge and cloud servers that foster adaptivity, elasticity, and self-learning capabilities are also introduced. The main contribu- High demand for In-network caching tions of this article are highlighted below: particular content • Introduce the main practical challenges fac- Resource management ing distributed IoT computing systems and Redundant data Data aggregation and analysis highlight the potential solutions. transmission • Provide a classification of the emerging tech- More demand on the nologies in distributed IoT systems into differ- Scheduling techniques ent sub-categories along with their relevant uplink discussions. • Propose an optimization framework to tackle Various computing Cooperative hierarchical architectures various system-level aspects such as comput- entities (e.g., fog-to-cloud and cloud-to-fog) ing, delay, scheduling, and energy consump- Performance Standardization and utilization of tion. Multiple service providers coordination compatible infrastructures and platforms • Present a Machine Learning (ML)-based case study for efficient IoT device clustering in the Network slicing, cross-layer optimization, Interoperability context of edge-IoT system optimization. and load sharing COMPUTING IN DISTRIBUTED IOT SYSTEMS: TABLE 1. Challenges and potential solutions for Edge-IoT systems. CHALLENGES AND POTENTiaL SOLUTIONS The ever-increasing number of IoT devices and the context of cellular IoT networks, the baseband the heterogeneous nature of their demands pose unit (BBU) pool that supports the architecture of many practical challenges, especially in regard to Heterogeneous Cloud Radio Access Networks system management and resource provision. Edge (H-CRANs) can provide significant assistance since computing can help resolve these challenges by all network resources are virtualized and managed exploiting the physical proximity with IoT devices by a unified controller. toward supporting context-awareness, data filtra- From the information security perspective, tion, and on-demand resource provision at the the limited computing resources of IoT devices network edge. In this section, we categorize the may lead to serious security challenges, especially main challenges in edge-IoT systems into three when tasks are offloaded to remote servers. Thus, main domains, and present potential enabling it is essential to harness the powerful edge com- solutions as listed in Table 1. puting resources and support IoT devices with ML and self-organizing capabilities to reduce mali- HETEROGENEITY OF IOT SYSTEMS cious attacks. Also, it is important for IoT devices The ubiquity of IoT devices in a variety of applica- to consider the potential delay and energy con- tions diversifies the Quality of Experience (QoE) sumption when making an offloading decision [6]. requirements. One of the important QoE param- eters is the end-to-end delay experienced by IoT RESOURCE MANAGEMENT devices. While some IoT applications such
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