IEEE ACCESS 1 Context-aware Computing in the : A Survey on Internet of Things From Industrial Market Perspective

Note: IEEE ACCESS Journal Official Title for Citations: ’A Survey on Internet of Things From Industrial Market Perspective’

Charith Perera, Member, IEEE, Chi Harold Liu Member, IEEE, Srimal Jayawardena, Member, IEEE, Min Chen, Senior Member, IEEE

Abstract—The Internet of Things (IoT) is a dynamic global which the IoT paradigm envisions. Let us briefly introduce information network consisting of Internet-connected objects, some of the terms in this domain which will help to better such as RFIDs, sensors, and actuators, as well as other in- understand the remaining sections. Context can be defined as struments and smart appliances that are becoming an integral component of the Internet. Over the last few years, we have seen any information that can be used to characterize the situation a plethora of IoT solutions making their way into the industry of an entity. An entity is a person, place, piece of software, marketplace. Context-aware communication and computing has software service or object that is considered relevant to the played a critical role throughout the last few years of ubiquitous interaction between a user and an application, including the computing and is expected to play a significant role in the user and application themselves [5]. Context-awareness can IoT paradigm as well. In this article, we examine a variety of popular and innovative IoT solutions in terms of context-aware be defined as the ability of a system to provide relevant technology perspectives. More importantly, we evaluate these IoT information or services to users using context information solutions using a framework that we built around well-known where relevance depends on the user’s task [5]. Context- context-aware computing theories. This survey is intended to aware communication and computing has been researched serve as a guideline and a conceptual framework for context- extensively since the early 2000s and several surveys have aware product development and research in the IoT paradigm. It also provides a systematic exploration of existing IoT products in been conducted in this field. The latest survey on context- the marketplace and highlights a number of potentially significant aware computing focusing on the IoT was conducted by Perera research directions and trends. et al. [2]. Several other important surveys are analysed and Index Terms—Internet of things, industry solutions, context- listed in [2]. However, all these surveys focus on academic awareness, product review, IoT marketplace research. To the best of our knowledge, however, no survey has I.INTRODUCTION focused on industrial IoT solutions. All the above-mentioned Over the last few years the Internet of Things (IoT) [1] has surveys have reviewed the solutions proposed by the academic gained significant attention from both industry and academia. and research community and refer to scholarly publications Since the term was introduced in the late 1990s many solutions produced by the respective researchers. In this paper, we have been introduced to the IoT marketplace by different types review IoT solutions that have been proposed, designed, de- of organization ranging from start-ups, academic institutions, veloped, and brought into the market by industrial organiza- government organizations and large enterprises [2]. IoT’s tions. These organizations range from start-ups and small and medium enterprises to large corporations. Because of their in- arXiv:1502.00164v1 [cs.CY] 31 Jan 2015 popularity is governed by both the value that it promises to create and market growth and predictions [3]. The IoT allows dustrial and market-driven nature, most of the IoT solutions in ’people and things to be connected Anytime, Anyplace, with the market are not published as academic work. Therefore, we Anything and Anyone, ideally using Any path/network and Any collected information about the solutions from their respective service’ [4]. Such technology will help to create ’a better websites, demo videos, technical specifications, and consumer world for human beings’, where objects around us know what reviews. Understanding how context-aware technologies are we like, what we want, and what we need and act accordingly used in the IoT solutions in the industry’s marketplace is vital without explicit instructions [2]. for academics, researchers, and industrialists so they can iden- Context-aware communication and computing is a key tify trends, industry requirements, demands, and innovation technology that enables intelligent interactions such as those opportunities. The rest of the article is organized as follows. In Section Charith Perera, and Srimal Jayawardena, are with the Research School of II, we briefly analyse IoT marketplace trends and growth. The Computer Science, The Australian National University, Canberra, ACT 0200, Australia. (e-mail: fi[email protected]) evolution of context-aware technologies and applications are Chi Harold Liu is with Beijing Institute of Technology, China. (e-mail: presented in Section III. Then, we introduce the theoretical [email protected]) foundation and our evaluation framework used in this paper in Min Chen is with Huazhong University of Science and Technology, China. (e-mail: [email protected]) Section IV. Subsequently, in Section V, we review a selected Manuscript received xxx xx, xxxx; revised xxx xx, xxxx. number of IoT solutions from context-aware perspective. IEEE ACCESS 2

Later, we present lessons learned and innovation opportunities based on the evaluation results in Section VI. Finally, we 5% Education present the conclusion remarks. 24% 23% Transportation Industry II.INTERNETOF THINGS MARKETPLACE Healthcare 15% The vision of the IoT has been heavily energised by Retail Trade 26% 8% statistics and predictions. In this section, we discuss some of Other the statistics and facts related to the IoT which allows us to Global share of turnover in 2016 understand how the IoT has grown over the years and how it [Source: Frost & Sullivan (2011)] is expected to grow in the future. Further, these statistics and facts highlight the future trends in the industry marketplace. Fig. 2. RFID Sales by Major Market Segments. It is estimated that there about 1.5 billion Internet-enabled PCs and over 1 billion Internet-enabled mobile phones today. These two categories will be joined by Internet-enabled smart “” [10] is a concept aimed at providing a set objects [6], [7] in the future. By 2020, there will be 50 to of new generation services and infrastructure with the help 100 billion devices connected to the Internet, ranging from of information and communication technologies (ICT). Smart , PCs, and ATMs (Automated Teller Machine) to cities are expected to be composed of many different smart manufacturing equipment in factories and products in shipping domains. Smart transportation, smart security and smart energy containers [8]. As depicted in Figure 1, the number of things management are some of the most important components for connected to the Internet exceeded the number of people on building smart cities [11]. However, in term of market, smart Earth in 2008. According to CISCO, each individual on earth homes, smart grid, smart healthcare, and smart transportation will have more than six devices connected to the Internet by solutions are expected to generate the majority of sales. Ac- 2020. cording to MarketsandMarkets report on Smart Cities Market (2011 - 2016), the global smart city market is expected to 60 6.58 7 [Source: Cisco Internet Business Solutions Group (IBSG)] cross $1 trillion by 2016, growing at a CAGR of 14.2% as 50 50 6 World population (in billions) illustrated in Figure 3. 5 40 Internet-connected devices (in billions) Internet-connected devices per person 4 3.47 1400 30 25 Smart Homes Smart Buildings 3 1200 Smart Energy (Grid) Smart Industrrie Automation 20 1.84 2 Smart Healthcare Smart Eduction 12.5 1000 Smart Transportation Smart Security 7.6 10 6.3 6.8 7.2 1

0.5 0.08 ) 800 $ ( 0 0 s

2003 2010 2015 2020 n 600 o i l l i

B 400 Fig. 1. Growth in Internet-Connected Devices / Objects by 2020. 200 According to BCC Research 2011 market report on sensors, 0 the global market for sensors was around $56.3 billion in 2011 2012 2013 2014 2015 2016 2010. In 2011, it was around $62.8 billion. The global market Fig. 3. Smart Product Sales by Market in 2016. for sensors is expected to increase to $91.5 billion by 2016, at a compound annual growth rate of 7.8%. One of the techniques for connecting everyday objects into networks is The interconnection and communication between everyday radio frequency identification RFID technology [9]. In this objects, in the IoT paradigm, enables many applications in technology, the data carried by the chip attached to an object many domains. Asin and Gascon [12] have listed 54 applica- is transmitted via wireless links. RFID has the capability tion domains under 12 categories: smart cities, smart environ- to convert dump devices into comparatively smart objects. ment, smart water, smart metering, security and emergencies, RFID systems can be used wherever automated labelling, retail, logistics, industrial control, smart agriculture, smart identification, registration, storage, monitoring, or transport is animal farming, domestic and , and eHealth. required to increase efficiency and effectiveness. According After analysing the industry marketplace and careful consid- to Frost & Sullivan (2011), the global RFID market was eration, we classified the popular existing IoT solutions in valued at from $3 billion to $4 billion in 2009. The RFID the marketplace into five different categories: smart wearable, market will grow by 20% per year through 2016 and reach smart home, smart city, smart environment, smart enterprise. In a volume of approximately from $6.5 billion to almost $9 this paper, we review over 100 different IoT solutions in total. billion. According to Figure 2, it is expected that five main It is important to note that not all the solutions we examined sectors, education, transportation, industry, healthcare, and are listed in the technology review in Table II. For the review, retails, will generate 76% of the total RFID market demand we selected a wide range of IoT products which demonstrate by 2016. different context-aware functionalities. IEEE ACCESS 3

Network The Internet Mobile-Internet Mobiles + People + PCs Internet of Things

People Mobile Mobile Mobile Host Device Device Device

Host Host Host Host Host Host Host Host Host Web

Host

Host Host Host

Mobile Mobile Device Device People Interconnected Objects

Fig. 4. Evolution of the Internet in five phases. The evolution of Internet begins with connecting two computers together and then moved towards creating World Wide Web by connecting large number of computers together. The mobile-Internet emerged by connecting mobile devices to the Internet. Then, peoples’ identities joined the Internet via social networks. Finally, it is moving towards Internet of Things by connecting every day objects to the Internet

III.EVOLUTIONOF CONTEXT-AWARE TECHNOLOGY other context information retrieved through mobile devices It is important to understand the evolution of the Internet to build novel context-aware applications such as activity before discussing the evolution of context-aware technologies. predictions, recommendations, and personal assistance [17]. The Internet broadly evolved in five phases as illustrated in For example, a mobile application may offer context-aware Figure 4. The evolution of Internet begins with connecting functionalities by fusing location information retrieved from two computers together and then moved towards creating the mobile phones and recent ‘likes’ retrieved from social media World Wide Web by connecting large number of computers sites to recommend nearby restaurants that a user might like. together. Mobile-Internet emerged when mobile devices were In the next phase, ‘things’ were connected to the Internet by connected to the Internet. People’s identities were added to creating the IoT paradigm. An example of context-aware func- the Internet via social networks [13]. Finally, the Internet of tionality provided in the IoT paradigm would be an Internet- Things emerged, comprised of everyday objects added to the connected refrigerator telling users what is inside it, what Internet. During the course of these phases, the application of needs to be purchased or what kind of recipes can be prepared context-aware communication and computing changed signif- for dinner. When the user leaves the office, the application icantly [2]. autonomously does the shopping and guides the user to a In the early phase of computer networking when comput- particular shopping market so s/he can collect the goods it ers were connected to each other in point-to-point fashion, has purchased. In order to perform such tasks, the application context-aware functionalities were not widely used. Providing must fuse location data, user preferences, activity prediction, help to users based on the context (of the application currently user schedules, information retrieved through the refrigerator open) was one of the fundamental context-aware interactions (i.e. shopping list) and many more. In the light of the above provided in early computer applications and operating systems. examples, it is evident that the complexity of collecting, Another popular use of context is context-aware menus that processing and fusing information has increased over time. help users to perform tasks tailored to each situation in a given The amount of information collected to aid decision-making application. When the Internet came into being, location infor- has also increased significantly. mation started to become critical context information. Location information (retrieved through IP addresses) were used by IV. THEORETICAL FOUNDATION AND EVALUATION services offered over the Internet in order to provide location- FRAMEWORK aware customization to users. Once the mobile devices (phones This section discusses context-aware theories and related and tablets) became a popular and integral part of everyday historic developments over time. The evaluation framework life, context information collected from sensors built-in to the which we used to review IoT products in the marketplace devices (e.g. accelerometer, gravity, gyroscope, GPS, linear are built upon the theoretical foundation presented in this accelerometer, and rotation vector, orientation, geomagnetic section. First, we lay the theoretical foundation and secondly field, and proximity, and light, pressure, humidity and temper- we discuss the evaluation framework. ature) were used to provide context-aware functionality. For example, built-in sensors are used to determine user activities, environmental monitoring, health and well-being, location and A. Context-aware Computing Theories so on [14]. The term context has been defined by many researchers. Dey Over the last few years social networking [15] has be- et al. [18] have evaluated and highlighted the weaknesses of come popular and widely used. Context information gath- these definitions. Dey claimed that the definition provided by ered through social networking services [16] (e.g. Facebook, Schilit and Theimer [19] was based on examples and cannot Myspace, Twitter, and Foursquare) has been fused with the be used to identify new context. Further, Dey claimed that IEEE ACCESS 4 definitions provided by Brown [20], Franklin and Flachsbart on context such as location, time, etc. By definition, IoT [21], Rodden et al. [22], Hull et al. [23], and Ward et al. promises to provide any service anytime, anyplace, with [24] used synonyms to refer to context, such as ‘environment’ anything and anyone, ideally using any path/network. and ‘situation’. Therefore, these definitions also cannot be • Execution: Automatic execution of services is also a used to identify new context. Abowd and Mynatt [25] have critical feature in the IoT paradigm. Let us consider a identified the five W’s (Who, What, Where, When, Why) as the smart home [29] environment. When a user starts driving minimum information that is necessary to understand context. home from their office, the IoT application employed in Schilit et al. [26] and Pascoe [27] have also defined the term the house should switch on the air condition system and context. switch on the coffee machine to be ready to use by the We accept the definition of context provided by Abowd et time the user steps into their house. These actions need to al. [5] to be used in this research work, because their definition be taken automatically based on the context. Machine-to- can be used to identify context from data in general. We machine communication is a significant part of the IoT. presented the definition of context in Section I. • Tagging: In the IoT paradigm, there will be a large The term context awareness, also called sentient, was first number of sensors attached to everyday objects. These introduced by Schilit and Theimer [19] in 1994. Later, it was objects will produce large volumes of sensor data that defined by Ryan et al. [28]. In both cases, the focus was on has to be collected, analysed, fused and interpreted [31]. computer applications and systems. As stated by Abowd et Sensor data produced by a single sensor will not provide al. [5], those definitions are too specific and cannot be used the necessary information that can be used to fully under- to identify whether a given system is a context-aware system stand the situation [32]. Therefore, sensor data collected or not. We presented the definition provided by Abowd et through multiple sensors needs to be fused together [33]. al. [5] in Section I. After analysing and comparing the two In order to accomplish the sensor data fusion task, context previous efforts conducted by Schilit et al. [26] and Pascoe needs to be collected. Context needs to be tagged together [27], Abowd et al. [5] identified three features that a context- with the sensor data to be processed and understood later. aware application can support: presentation, execution, and Context annotation plays a significant role in context- tagging. Even though, the IoT vision was not known at the aware computing research. The tagging operation also time these features are identified, they are highly applicable identified as annotation. to the IoT paradigm as well. We elaborate these features from In Figure 5, we summarise three different context-aware an IoT perspective. features presented by researchers. It is clear that all these clas- • Presentation: Context can be used to decide what in- sification methods have similarities. We have considered all formation and services need to be presented to the user. these feature sets when developing our evaluation framework. Let us consider a smart [29] environment scenario. When a user enters a supermarket and takes their smart phone B. Evaluation Framework out, what they want to see is their shopping list. Context- aware mobile applications need to connect to kitchen This section presents the evaluation framework we used appliances such as a [30] in the home to review the IoT products in context-aware perspective. We to retrieve the shopping list and present it to the user. developed this evaluation framework based on the widely This provides the idea of presenting information based recognized and cited research done by Abowd et al. [5]. In this evaluation, we apply one and half decade old context aware theories into IoT era. Our evaluation is mainly based Presentation on three context-aware features in high-level: 1) context-aware selection and presentation, 2) context-aware execution, and 3) Proximity Selection context-aware-tagging. However, we have also enriched the Contextual Command evaluation framework by identifying sub-features under above Contextual Sensing and Presentation mentioned three features. Our evaluation framework consists Contextual Resource Discovery of nine (9) features. The Figure 6 visualizes how data is being collected trans- Automatic Execution ferred, processed, context discovered and annotated in typical Context Triggered Actions IoT solutions. It is important to note that not all solutions may Automatic Reconfiguration use the exact same data flow. Each solution may use part of the architecture in their solution. We will refer to this common Contextual Adaptation data flow architecture during this paper to demonstrate how Tagging each solution may design their data flows. Our objective is to identify major strategies that are used by IoT products to offer Contextual Augmentation context-aware functionalities. From here onwards, we explain the taxonomy, the evaluation framework, used to evaluate the Fig. 5. Context-aware features identified by different researchers: Abowd et al. [5] (Blue), Schilit et al. [26] (Yellow), Pascoe [27] (Green). Ccontext- IoT products. The results of the evaluation are presented in awareness as been defined using these features (can also be called character- Table II. Summary of the evaluation framework is presented istics of a given system) in Table I. IEEE ACCESS 5

Cloud Smart Wearable Data Archive Real Time Processing User

Sensors

Intermediary Communication Visualisation / Presentation / Smart and Processing Recommendation Things Devices =Context Discovery

Fig. 6. Data Flow in IoT Solutions in High-level. Context can be discovers in different stages / phases in the data flow. A typical IoT solution may use some part of the data flow architecture depending on the their intended functionalities.

First we introduce the name of the IoT solution in the collects and processes context information such as location, column (1) in Table II. We also provide the web page link temperature, light, relative humidity and biometric pressure of the each product / solution. It is important to note that, in order to enhance the visibility and transparency of the these products does not have any related academic publication. supply chain. SenseAware uses both hardware and software Therefore, we believe that web page links are the most reliable components in their sensor-based logistic solution. such data reference to a given IoT solution. Such links allow readers to collection allows different parties engage in supply chain to follow further reading by using the product name along with monitor the movement of goods in real-time and accurately web link. know the quality of the transported goods and plan their pro- In column (2), we classify each product into five categories. cesses effectively and efficiently. We list commonly acquired Each category is denoted by a different colour: red (smart primary context information in column (3) in Table II. city), yellow (smart environment), blue (smart enterprise), Secondary context is any information that can be computed green (smart wearable), and purple (smart home). Some using primary context. The secondary context can be com- solutions may belong to multiple categories. We divide the rest puted by using sensor data fusion operations or data retrieval of the columns into three section : Context-aware Tagging, operations such as web service calls (e.g. identify the distance Context Selection and Presentation, and Context execution. between two sensors by applying sensor data fusion operations 1) Context-aware Tagging Section: Context-aware tagging, on two raw GPS sensor values). Further, retrieved context such which is also called context augmentation and annotation as phone numbers, addresses, email addresses, birthdays, list represent the idea of sensing the environment and collecting of friends from a contact information provider based on a primary context information. We also believe that secondary personal identity as the primary context can also be identified context generation is also a part of context-aware tagging as secondary context. For example, Mimo (mimobaby.com) feature. Primary context is any information retrieved without has built a smart nursery system, where parents learn new using existing context and without performing any kind of insights about their baby through connected products like the sensor data fusion operations [2]. For example, SenseAware Mimo Smart Baby Monitor. In this product, turtle is the device (senseaware.com) is a solution developed to support real-time that collects all primary context information. Then the data shipment tracking. As illustrated in Figure 7, SenseAware is transferred to an intermediary devices called lilypad. Such responsibility offloading strategy allows to reduce the turtle’s weight at minimum level and to increase the battery life. the communication and processing capabilities are offloaded to the Location Temperature Light Relative Barometric lilypad device which can be easiy recharged when necessary. Humidity Pressure We can see Mimo Smart Baby Monitor usees some parts of the data flow architecture we presented in Figure II. provided by Mimo and the data flow within the solution is presented in Figure 8. Cloud services [34] performs the additional processing and summarised data is pushed to the mobile devices for context presentation. In the user Fig. 7. SenseAware (senseaware.com) uses small smart devices that comprises interface, parents are presented mostly the secondary context five different built-in sensors with limited computational and communication information such as baby movement or baby’s sleeping status. capabilities. It reports the status of the packages in real time to the cloud. These smart devices comes in different sizes and form factors, as illustrated Accelerometer sensors are used to discover such secondary here, in order to support different types of packaging methods (Two types of context information using pattern recognition techniques. We smart devices are shown in the figure) list secondary context information generated by IoT solutions IEEE ACCESS 6 in column (4) in Table II. commonly used devices to present the context to the users. 2) Context Selection and Presentation Section: There are Typically, an IoT solution offers context presentation and number of commonly used strategies, by most of the IoT selection via some kind of software application. Some of solutions in the marketplace, to present context to the users. the commonly used presentation channels are web-based Most of the IoT products use some kind of visualization (W), mobile-based (M), desktop-based (D), and objects-based techniques to present context information the users. We call (O). First, three mediums describes themselves. Object-based this visual presentation. For example, Fitbit (fitbit.com) is a means that context selection and presentation is done through device that can be worn on multiple body parts in order to a custom IoT device itself. Sample IoT solutions that use tracks steps taken, stairs climbed, calories burned, and hours object-base presentation strategy are presented in Figure 10. slept, distance travelled, quality of sleep. This device collects We identify the presence of different presentation channels data and present it to the users through mobile devices and related to each IoT product in column (6) in Table II. web interfaces. Figure 9 illustrates the context presentation In addition to the context presentation channels, IoT solu- of Fitbit. Variety of different charts, graphs, icons and other tions use number of user interaction mechanisms such as voice types of graphical elements are heavily used to summarise and (V), gesture (G), touch (T). Over last few years, we have seen present analysed meaningful actionable data to the users. such more and more voice activated IoT solutions are coming to the visualization strategies are commonly encouraged in human marketplace. For example, latest technological development computer interaction domain specially due to the fact that ’a such as natural language processing and semantic technologies picture is worth a thousand words’. We denote the presence have enabled the wide use of voice activated IoT solutions. of virtual presentation related to each IoT product using (X) Amazon Echo (amazon.com/oc/echo), Ubi (theubi.com) are in column (5) in Table II. two voice activated personnel assistant solutions. Typically, IoT solutions in the market place also employ different they are capable of answering user queries related to weather, maps, traffic and so on (i.e. commonly asked questions). They are designed to learn from user interactions and customize their services and predictive models based on the user be- haviour and preferences. These solutions have gone beyond what typical smart phone assistants such as Google One, Microsoft Cortana, Apple Siri has to offer. For example, Ubi has the cabability to interact with other smart objects in the smart house environment. More importantly products such as Ivee (helloivee.com), a voice controlled hub for smart homes, facilitates interop- erability over the other IoT products in the markets. This means that consumers can use Ivee to control other IoT prod- ucts Iris (irissmarthome.com), Nest (nest.com), Philips Hue (meethue.com), SmartThings (smartthings.com), and Belkin WeMo (belkin.com). We discuss interoperability matters in

(a)

(b)

Fig. 8. (a) User interface provided to the users, in this case parents by Mimo Smart Baby Monitor (mimobaby.com). All the raw information collected are presented to the users, using graphs, figures and icons, after generating secondary context information. (b) Illustrates how primary context has been collected and transferred through the infrastructure to discover secondary Fig. 9. The Fitbit web based dashboard displays recent activity level and context information. lots of other statistics using graphics, charts, and icons. IEEE ACCESS 7

storing data. We identify the usage of real-time and archival techniques in column (8) in Table II. IoT solutions mainly use three different reaction mecha- nisms. Most commonly used mechanism is notification (N). This means that when a certain condition is met, IoT solution (a) (b) (c) will release a notification to the users explaining the context. For example, Mimo (mimobaby.com), the baby monitoring Fig. 10. (a) Smart Oven (maidoven.com), (b) Smart Fridge product we mentioned earlier, notifies the parents when the (lg.com/us/discover/smartthinq/refrigerator), (c) Smart Washing machine (lg.com/us/discover/smartthinq/laundry). Some of the commonly used baby shows any abnormal movements or breathing patterns. objects in households are not enriched with presentation capabilities such Parent will receive the notification through their smart phone. as touch screens. In such circumstances context selection and presentation Some IoT solutions may react by performing actuations (A). responsibilities can be offloaded to commonly used devices such as smart-phones and tablets. For example, Blossom (myblossom.com) ia a smart watering products that can be self-programmed based on real-time weather data and gives the user control over the phone, details in Section VI. In addition to centralizes home hubs lowering the water bill up to 30%. In this kind of scenario, the based IoT systems, more and more standalone IoT products product may autonomously perform the actuations (i.e. open also support voice-activated interaction such as executing and close sprinklers) based on the context information. An- commands. For example, VOCCA (voccalight.com) is a plug other reaction mechanism used by IoT solutions is providing & play voice activated light bulb adapter requires no WiFi, no recommendations (R). For example, MAID (maidoven.com) set-up, no installation. has a personalization engine that continuously learns about the Gesture has also been used to enable the interac- users. MAID learns what users cook regularly, tracks users tions between IoT products and users. For example Myo activity using data from smart phones and smart watches. (thalmic.com/en/myo/) is a wearable armband that can be Then, it will provide recommendations for a healthy balanced used to issues gesture base commands. Myo reads gestures diet. MAID also recommends users to workout or to go for a and motion and let hte users to seamlessly control smart run based on the calories they consume each day. We identify phones, presentations, and so on. Nod (hellonod.com) is the the usage of reaction mechanisms related to each IoT product a advanced gesture control ring. It allows users to engage in column (9) in Table II. objects with user movements. Nod can be considered as a Another important factor we identified during the prod- universal controller, allowing effortless communication with uct review is the learn-ability. Some products are capable all of the smart devices in users connected life, including of recording user provided inputs and other autonomously phones, tablets, Google Glass, watches, home appliances, TVs, gathered information to predict future behaviours. In computer computers and more. We identify the presence of different user science, such behaviour is identified as machine learning interaction mechanisms related to each IoT product in column (ML). For example, Nest (nest.com) thermostat is capable of (7) in Table II. learning users’ schedules and the temperatures users prefer. It IoT solutions process data in different locations in their keeps users comfortable and saves energy when they are away. data communication flow as shown in Figure 6. Sometimes In contrast, products such as Fibaro (fibaro.com) requires users data is processed within the sensors or the local processing to explicitly defines (UD) event thresholds and triggers as devices. In other circumstances, data is sent to the cloud shown in Figure 11. We review the learn-ability of each IoT for processing. Deepening the applications and functionalities product in column (10) in Table II. each IoT solution tries to provide, data may be processed in There are number of different ways that an IoT product real-time (RT) or later (A). Specially, event detection based would trigger a certain reaction. It is important to note that IoT systems need to act in real-time which requires real-time a single IoT solution may combine multiple triggers together processing. For example, IoT solutions such as Mimo smart in order to facilitate complex requirements. Some rigger may baby monitor performs data processing in real-time as their be spacial (S), temporal (T), or event based (E). Event based mission is to increase the health and safety of the toddlers. It triggers are the most commonly used mechanism. For example, is also important to note that not every solution requires data the IoT products such as SmartThings (smartthings.com), archival. For example, health and fitness related IoT products Ninja Blocks (ninjablocks.com), Fibaro (fibaro.com), Twine can be benefited from archiving historic data. Such archives (supermechanical.com) allow users to define contextual trig- data will allow to produce graphs and charts over time and gers using sensors, actuators and parameters. Figure 11 and provide more insights and recommendations t the consumers. Figure 12 shows how two different products define events. More data also facilitates more accurate prediction. However. Low powered bluetooth beacons are commonly used in IoT storing more data cost more and not every solution requires products, specially in commercial and retail sector for both such storage. ShutterEaze (shuttereaze.com) makes it easy for localization and location-based advertising [35]. For example, anyone to add remote control functionality and automate their XY (xyfindit.com) and Estimote (estimote.com) are two similar existing interior plantation shutters. For example, IoT product products in the IoT marketplace that provide small beacons like this will not necessarily be benefited by archiving historic that can be attached to any location or object. The beacons will data. Still it can learn user behaviour over time (based on broadcast tiny radio signals which smart phones can receive how users use the product), and automate the task without and interpret, unlocking micro-location and contextual aware- IEEE ACCESS 8

TABLE I SUMMARY OF THE EVALUATION FRAMEWORK USEDIN TABLE II Taxonomy / Feature Description The name of the IoT product or the solution sorted by ‘Category’ and then by ‘Project Name’ within each category 1 Product and Web link in ascending order. Category that the solution belongs to. Each category is denoted by a different colour: red (smart city), yellow 2 Category (smart environment), blue (smart enterprise), green (smart wearable), and purple (smart home). Some solutions belongs to multiple categories. 3 Primary Context Major context data captured by IoT solutions. 4 Secondary Context Major secondary context generated by the IoT solution. 5 Visual Presentation We denote the presence of visual context presentation using a (X). We identify a number of commonly used presentation channels as follows: Web-based (W), Mobile-based (M), Desktop-based (D), Object-based (O). Please note that web based channels can be accessed through both mobile 6 Presentation Channels and desktop devices. However, we consider web-based as a separate category while native mobile apps considered as mobile based and native desktop apps consider as desktop-based. We identify Touch (T), Gesture (G), and Voice (V) as three commonly used user interaction mechanism. 7 User Interaction Mechanism Interactions done through a PC or a smart phone is denoted by (M). Touch (T) refers to the ‘user touching a physical product’. It does not refer to the user interaction using touch enabled devices such as smart phones. 8 Real-Time or Archival Some IoT solutions processes data in real-time (RT) and other process archival data (A). IoT products use different reaction mechanisms. Some of them release notifications (N). Some solutions provides 9 Reaction Mechanism recommendation (R) to the users on how to react to a certain situation. Some IoT products perform physical actuations (A). Some solutions are capable of learning by analysing user behaviours and other inputs over time. such machine 10 Learning Ability leaning ability is denoted by (ML). Other solutions require specific instruction from users typically using IF- ELSE-THEN mechanism. Such user defined approach is denoted using (UD). In IoT products, notifications are released based in different conditions as follows: Temporal (T), Spatial (S), 11 Notification Execution Event (E). Notification could be in any form such as SMS, email, sound, vibration and so on.

Note: Cases where sufficient information were not available are denoted by (-). Further, (×) denote the unavailability of a certain feature.

be sent to the parent’s smart phone when FiLIP detects that the child has entered or left a safe zone. In temporal mechanism, trigger is release based on a time schedule. Temporal triggers may refer to time as time of the day (e.g. exactly: 10.30 am or approximately: morning), day of the week (e.g. Monday or weekend), week of the month (e.g. If raining: second week), month of the year (e.g January), season (e.g. - close roof windows, - turn garden sprinklers OFF, winter). Figure 11 show how Fibaro system allows to define a - set "it rained" variable to 1. trigger by incorporating temporal triggers. IoT products such as Nest thermostat also use temporal triggers to efficiently learn and manager energy consumption.

Each day at 6:00 am check if "it rained" (user defined variable). If not ("it rained" variable = 0) - turn the sprinklers ON.

Fig. 11. Two scenarios defined using Fibaro (fibaro.com) platforms. The screen-shots show how different types of context triggered can be defined by combining sensors, actuators and predefined parameters.

ness. Therefore, IoT products may trigger a reaction when either users entering into or going out from a certain area. There are some other products such as FiLIP (myfilip.com) which users location-aware triggers to make sure children are staying within safe area. FiLIP uses a unique blend of GPS, GSM, and WiFi to allow parents to locate their child using the most accurate location information, both indoors and outdoors. Fig. 12. Twine (supermechanical.com) provides a user interface to define Parents can create a virtual radius around a location, such as scenarios by combining sensors and actuators in a WHEN-THEN fashion home, school or a friend’s house. Further, parents can set up which is also similar to the IF-THEN mechanism. Twine will trigger the to five such safe zones using the FiLIP app. A notification will actuation accordingly when conditions are met. IEEE ACCESS 9

V. REVIEWOF IOTSOLUTIONS processing API for the IoT which allows developers to easily In this section we evaluated variety of different IoT solutions and quickly add natural language processing functionality to in the marketplace based on the evaluation framework pre- their IoT solutions. sented in the earlier section. Table I, summarises the evaluation It is important to note that most of the IoT solutions consider framework used and Table II presents the IoT product review families or group of people as a whole, not as individuals. results. Therefore, most of the IoT solutions are unable to individually and separately identify father, mother or child living in a given house. For example, the temperature that individual family VI.LESSONS LEARNED,OPPORTUNITIES AND members would like to have can be different. However, most CHALLENGES of the modern thermostat only consider context information This section presents some major lessons we learnt during such as past behaviour, time of the day, presence of a user, and the IoT product review. so on. However, it cannot handle individual preferences of the family members. Therefore, embedding such capabilities to the A. Trends and Opportunities IoT products would be a critical requirement to be successful According to our survey on the IoT product marketplace, in future IoT marketplace. it is evident that the types of primary context information In order to support and encourage the adoption of IoT collected through sensors are mostly limited. However, the solution among consumers, it is important to make sure that ways such collected data is been processed varied significantly the usage of products allows to recover the cost of product based on the application and the required functionalities that purchase within a reasonable time period. For example, the the IoT product plan to offer. Therefore, it is important to Nest thermostat promises that consumers can recover its costs understand that, in IoT, same data can be used to derive through reducing the energy bill. Auto-Schedule feature in different insights in different domain. In combine, the IoT Nest makes it easy to create an energy efficient schedule that solutions have used around 30-40 different types of sensors to help the users to save up to 20% on heating and cooling bills. measure different parameters. The ability to derive different insights using same set of data validates the importance of B. Product Prototyping sensing as a service model [8], which envisions to create a data market that buys and sells data. There are number of do-it-yourself (DIY) prototyping plat- Most of the IoT solutions have used some kind of context forms available that allows to create IoT prototypes quickly presentation technique that summarizes and converts the data and easily. Specially these platforms are cheaper and mod- into a easily understandable format. It is also important to note ular in nature. They allow anyone with a new idea to test that, despite the advances in human computer interaction, most their initial thoughts with very limited budget, resources, and of the IoT solutions have only employed traditional computer more importantly less time. (arduino.cc) (including screen-based technique. Only few IoT solutions really allow variations such as Libelium (libelium.com)), .NET Gargeteer voice or object-based direct communications. However, most (netmf.com/gadgeteer), LittleBits (littlebits.cc) are some well of the wearable solutions use touch as a common interaction known prototyping platforms. Most of these products are open technique. We also see a trend of smart home products also source in nature. More importantly over the last few years, increasingly use touch-based interactions. Hands free voice or they have become more interoperable which allows product gesture based user interaction will help consumers to seam- designers to combine different prototyping platforms together. lessly integrate IoT products into their lives. At least, smart The programming mechanisms use to program these modules watches and glasses may help to reduce the distraction that can be varied (e.g. C, C++, C#, Java, Javascript, etc.). Some smart phones may create when interacting with IoT products. platforms provide easy and intuitive ways to write program Most of the IoT products ends their services after releasing such as mashing-ups and wirings as shown in Figure 13. notification to the consumers. Users will need to perform the There are small computer systems been developed appropriate actuation tasks manually. Lack of standards in to support IoT prototyping. For example (M2M) communication seems to play a (www.raspberrypi.org) is a such product. Raspberry Pi is significant role in this matter. We will discuss this issue in a credit card-sized single-board computer developed in the Section VI-C. Finally, it is important to note that increasing UK by the Raspberry Pi Foundation with the intention of number of IoT products use data analytic and reasoning in promoting the teaching of basic computer science in schools. order to embed more intelligence to their products. As a result, However, more recently, Raspberry Pis are heavily used in IoT there is a need for domain independent, easy to use (e.g. drag product prototype development. For example, IoT products and drop configuration without any program coding) analytical such as NinjaBlocks (ninjablocks.com) has used Raspberry Pis frameworks with different characteristics where some may in their production officially. Further, most of the platforms effectively perform on the cloud and the others may work such as Ardunio can successfully work with Raspberry Pi efficiently in resource constrained devices. One solution in Computers. Recently, Intel has also produced a small computer this space is Microsoft Azure Machine. Learning1. Another (e.g. Intel Galileo and Intel Edison boards) competitive to generic framework is Wit. Wit (Wit.ai) is a natural language Raspberry Pi which runs both windows and Linux. The Intel Edison is a tiny computer offered by Intel as a development 1http://azure.microsoft.com/en-us/services/machine-learning/ system for wearable devices. IEEE ACCESS 10

TABLE II EVALUATION OF SURVEYED RESEARCH PROTOTYPES,SYSTEMS, AND APPROACHES

[Project Type] Project Name

(Web Link) Category Primary Context Secondary Context Visual Presentation Presentation Channel User Interaction Real-Time Archival Notification Mechanism Learning Ability Notification Execution (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) Efficient routes to pick- [Waste Management] Waste fill-level W M RT, A N, R ML, UD E up waste, schedules X Enevo (enevo.com) Bluetooth signal [Indoor Localization] Location, Distance M M RT N, R UD T, S, E strength, Beacon ID X Estimote (estimote.com) Sound level, Road sur- Route for free parking [Parking Slot M, W M RT, A N, R ML, UD T, S, E face temperature slot X Management] ParkSight (streetline.com) Light, Presence, Local information such as Energy consumption, [Street Lighting] Tvi- weather changes, special Energy usage patterns, X W M RT, A N, A ML, UD T, S, E light (tvilight.com) events, emergency Lamp failure detection situations Crowd profiling at a [Crowed Movement GPS, Video M, W, D M RT N, A ML T, S given location X Analysis] SceneTap (scenetap.com) Heat maps to understand [Foot Traffic Moni- Floor level W T, M RT, A N ML, UD S, E customer movements X toring] Scanalyticsinc (scanalyticsinc.com) Social dynamics, struc- Foursquare check-ins [Crowed Analysis] ture, and character of W M RT, A - ML E cloud service X Livehoods cities on large scale (livehoods.org) [Crowed Analysis] Location, Video Crowed movement X M M RT - ML E Placemeter (placemeter.com) Fire extinguisher usage [Fire Safety] Fire Pressure gauge, Motion W M RT, A N UD S, E patterns, Storage quality X Extinguishers (engaugeinc.net) Location, Movement di- [Foot Traffic Monitor- Video, Motion rection, Predict pedes- X W M RT, A N ML, UD E ing] Motionloft (mo- trian and vehicle traffic tionloft.com) Bluetooth signal [Indoor Localization] Location, Distance W M RT N UD S, E strength, Beacon ID X Museum Analytics (artprocessors.net) GPS, Temperature, Hu- [Supply Chain Man- Shipment quality W M RT, A N UD S, E midity, Light, Pressure X agement] SenseAware (senseaware.com) Quality and efficiency Video, Mechanical [Manufacturing of manufacturing opera- W M RT, A N ML, UD E movements of Robots X Process Management] tions Sight Machine (sightmachine.com) Accelerometers, Strain Real-time load capacity, [Concrete Structure D M RT, A N UD E gages, Temperature construction quality X Health Monitoring] Smart Structures (smart-structures- inc.us) Identify item using [Smart Pallet] RFID, Barcode Global Returnable Asset - W M RT N - S, E (igps.net) Identifier (GRAI) Identify products, Iden- [Order Picking Video, Barcode tify the tasks to perform X O T, G RT N, R - S, E Glass] SmartPick related to each object (smartpick.be) Sound levels, [Environmental Temperature, Humidity, Air quality maps X O, M, W T, M RT, A N, R UD, ML E Monitoring] CO, NO2 AirCasting (aircasting.org) IEEE ACCESS 11

[Project Type] Project Name

(Web Link) Category Primary Context Secondary Context Visual Presentation Presentation Channel User Interaction Real-Time Archival Notification Mechanism Learning Ability Notification Execution (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) NO2, CO, O3, Volatile Organic Compounds, [Air Quality Moni- Air quality maps W M RT, A N UD E Radiation, Dust X tor] Air Quality Egg particulars (airqualityegg.com) Temperature, Humidity, Light, CO ,NO2, vibration, Volatile organic compounds, Climate trends, Air qual- [Public Sensor O , CO , SO, Dust - M M RT, A - - E 3 2 ity Infrastructure] Array particulars, Sound, of Things (array- infra-red images, ofthings.github.io) Precipitation and wind measurements Video, Audio, Tempera- Model bees’ life styles [Smart Farming] D-AN-- ture, Sunlight, Weather and behaviour X Bumblebee project (niksar- gent.com/bumblebee) Maps of water GPS, Temperature, movement, [Smart River W M RT, A N ML E Salinity Hydrodynamic X Management] modelling. Floating Sensor Network (float.berkeley.edu) Temperature, Ultrasonic, Flood detection and pre- [Floot Detection] Ox- W M RT, A N ML, UD E Wet sensor diction X ford Flood Network (oxfloodnet.co.uk) [Weather Monitor] Barometer, GPS Weather Forecast X M M RT, A N ML, UD E PressureNet (pres- surenet.cumulonimbus.ca) Efficient routes to pick- [Waste Management] Waste fill-level M, W M RT, A N, R ML, UD S, E up waste X Smart Belly (bigbelly.com) Air Quality, UV, Air Quality in streets, [Environment Temperature, Humidity, O, M M RT, A N, R UD E Indoor air quality maps X Monitor] Tzoa Light (mytzoa.com) Retrieve weather infor- [Weather Monitor] - mation from Web a ser- × O T RT, A N, A ML E (uniform.net) vice Force sensor, Heart rate Heart rate, Respiration, [Sleep Monitor] Bed- O, M T, M A N, R ML, UD T, E sensor Sleep cycles, Sleep time X dit (beddit.com) Breathing rate, Posture, Activity level, Peak Ac- [Health Monitor] Bio- GPS, ECG, Heart rate O, M, W, D T, M RT, A N, R ML, UD E celeration, Speed, Dis- X Harness (zephyrany- tance where.com) [Remote Health ECG, Biometric Sensors Health report X O, M, W T, M RT, A N, R ML, UD T, E Monitor] BodyGuardian (preventice.com) 3-Axis accelerometer, Heart condition, Calo- [Smart Ring] O M T, M RT N ML, UD T, S, E Heart rate, GPS ries Burned X Electricfoxy (electricfoxy.com) Steps, Distance, Calories Burned, [Health-Fitness 3-Axis accelerometer O, M, W T, M RT, A N, R ML, UD T, S, E Floors Climbed, Sleep X Tracker] Fitbit Tracking (fitbit.com) Users’ medication, [Emergency Helmet] Location M M RT, A N, A UD E Users’ personal allergies X ICEdot (icedot.org) Accelerometers, Gravity, Gyroscopes, , [Fitness Tracker] Rotational vector, M M RT, A N, R UD T, E Calories burned X Lark (lark.com) Orientation, Magnetometers Distance, Calories burned, Speed, Average [Sport Watch] Leikr GPS, Heart Rate O T RT, A N, R UD T, S, E pace per lap, Lap X (leikr.com) distance, Lap calories IEEE ACCESS 12

[Project Type] Project Name

(Web Link) Category Primary Context Secondary Context Visual Presentation Presentation Channel User Interaction Real-Time Archival Notification Mechanism Learning Ability Notification Execution (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) Posture steps, Distance travelled, Activity [Activity Tracker] 3-Axis Accelerometer O, M, D T, M RT, A N, R UD T, E recognition, Calories X Lumoback burned (lumobodytech.com) Baby sleep status, Res- 3-Axis Accelerometer, [Baby Monitor] piration patterns, Baby’s M, W T, M RT, A N UD E Audio, Respiration X Mimo (mimob- body position aby.com) Heart rate, Galvanic skin response, Skin tempera- Activity, Health, Calo- [Health Monitor] My- O, M T, M RT, A N, R ML E ture, 3-Axis Accelerom- ries X Basis (mybasis.com) eter [Medical Jacket] Pressure Activity level X M, W T, M RT, A N, A ML, UD E MyTJacket (mytjacket.com) [Security Authentica- Heart activity Personal Identity X O T, M - N - E tor] Nymi (nymi.com) GPS, 3-Axis Accelerometer 3- Speed, Track friends, Axis Gyroscope, 3- [Sport Goggles] Oak- Navigation maps, Jump O, M, W T, M RT, A N, R, A UD, ML T, S, E Axis Magnetometer, X ley (oakley.com) Analytic Temperature, Barometric Pressure Accelerometer, Hit direction, Force esti- [Sports Helmet] M, W T, M RT, A N, R UD E Rotation, Pressure mation, Hit count X TheShockBox (theshockbox.com) 3D swing, Club speed, Dual accelerometers 3- Swing plane, Tempo, [Sport Assistant] M T, M RT, A N,R ML,UD E Axis Gyroscope Backswing position, Hip X Zepp (zepp.com) rotation Volatile organic com- pounds, CO , CO, Tem- Indoor air quality pre- [Indoor Air Quality 2 O, M, W M RT, A N ML E perature, Humidity, Ac- diction X Monitor] Alima celerometer, (getalima.com) [Smart Locator] Beacon signal strength Distance, Geo-fencing X O,M T, M RT, A N UD S BiKN (bikn.com) [Family Connections] × × × O T RT, A A × × Good Night Lamp (goodnightlamp.com) [Light Bulb] Hue × × X MM × A UD × Bulb (meethue.com) Identify family and [Door Lock] Lock- GPS, Person ID O, M M - A - T, S, E friends X itron (lockitron.com) Efficient heating sched- [Smart Thermostat] Temperature ule, Heat up and cool X O, M T, M RT A ML E Nest (nest.com) down time calculation Motion, Moisture, Temperature, Light, Energy usage, Indoor lo- [Smart Home] M V, M RT N, R, A UD T, S, E Humidity, Presence calization X Ninja Blocks [extendible] (ninjablocks.com) Temperature, Humidity, [Weather Station] Ne- Air quality, CO2, Sound, Weather prediction X M, W M RT N UD E tatmo (netatmo.com) Pressure Weight, Body composi- Body Mass Index, Air [Smart Scale] With- tion, Heart rate, Temper- quality, Automatic user X M, W M RT, A N, R, A UD × ings (withings.com) ature, CO2 recognition Motion, Moisture, Temperature, Light, Energy usage, Indoor lo- [Smart Home] M V, M RT N, R, A UD T, S, E Humidity, Presence calization X SmartThings [extendible] (smartthings.com) Efficient heating sched- Temperature, GPS, [Thermostat] Tado ule , User location pre- M M RT, A N, A ML, UD T, S Weather forecast X (tado.com) diction Moisture, Magnetism, Recommendation to [Smart Cooking] Temperature, Vibration, M, W M RT, A N UD T, S, E cook meat X Twine (supermechan- Orientation ical.com) IEEE ACCESS 13

[Project Type] Project Name

(Web Link) Category Primary Context Secondary Context Visual Presentation Presentation Channel User Interaction Real-Time Archival Notification Mechanism Learning Ability Notification Execution (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) Temperature, light, hu- [Personal Assistant] - O, M, W V RT, A N, R, A ML, UD T, S, E midity, pressure X Ubi (theubi.com) Temperature, energy [Power Plug] WeMo Estimate Cost M T, M RT, A N, A UD T,E consumption X Switch (belkin.com) location (e.g. pub, [Family Connections] GPS × O T RT N × E work, home) WhereDial (wheredial.com) Daily Activity Re- port (play time, rest Accelerometer, location, [Dog Activity Moni- time), Medical Rec- M M RT, A N, R ML S person X toring] Whistle (whis- ommendations, Ex- tle.com) cers

Programming IDE tools such Microsoft Visual Studio pro- issue. These modules need to be defined in a standard form vides significant support for IoT program development by despite their differences in real implementations. Further, such facilitating visual wiring, mash ups and automated code gen- context discovery modules should be able to combine together eration. Such ease of programming and prototyping abilities to discover more advance context information [36]. We further have attracted significant attention from hobbyist, researcher, explain how such framework should work in real world in and even from school children. Section VI-D. These modular based prototyping tools allow to build and test context-aware functionalities efficiently and effectively. C. Interoperability on Product and Services Most of these platforms offer large number of sensing mod- Interoperability is a critical factor to be successful in IoT ules that allow to collect data from different types sensors. domain. Consumers typically do not want to stick into one As we mentioned earlier such data can be considered as single manufacturer or service provider. They always go for primary context. Therefore, such primary context can be their preferences and for the factor which are more important combined together to generate secondary context information. to them such as cost, look and feel, customer service, function- However, in most of the prototyping platforms, secondary ality and so on. Interoperability among different IoT products context discovery needs to be done manually or using IF-ELSE and solutions allows consumers to move from one product to statements. However, it would be much useful to develop a another or combine multiple products and services to build standard framework with modularity in mind to address this their smart environments as they like in a customize fashion. Further, interoperability [37] is also important to eliminate market domination of large companies that increase the entry barriers for the small IoT product and service providers. In IoT market place, interoperability is mainly achieved using three methods: 1) partnerships among product and ser- vice developers, 2) open and close standards, and 3) adaptors and mediator services. We have seen that major industrial players in the IoT marketplace stablish strategic partnerships with each other in order to enable interoperability among (a) (b) their product and services. However, this is not a scalable strategy to widely enable interoperability among IoT devices. Similarly, large corporations such as Apple (e.g. HomeKit2, (d) HealthKit3) and Google (e.g. Fit4) are also attempting to build their own standards and interoperability certifications. This kind of interoperability may lead to corporate domination of IoT marketplace which could also hinder the innovation by small, medium, and start-up companies. (c) (e) To address the interoperability, there are some alliance have been initiated. For example AllSeen Alliance (allseenal- Fig. 13. (a) Microsoft Visual Studio IDE that allows to visually wire .NET liance.org) has been created to promote some kind of interop- Gadgeteer hardware components. The IDE automatically generated the code erability among IoT consumer brands. AllSeen has developed a skeletons to make the prototyping much easier and faster, (b) Hardware sen- LittleBits sors and actuators of (littlebits.cc) platform, (c) Wyliodrin web-based 2 IDE that allows to program variety of different platforms including Arduino developer.apple.com/homekit 3 (arduino.cc) and Raspberry Pi (www.raspberrypi.org) by visually drag and developer.apple.com/healthkit drop programming components, (d) a Raspberry Pi (www.raspberrypi.org), 4developers.google.com/fit (e) Intel Edison board. IEEE ACCESS 14

location, time, family members around, IoT products located near by and so on. Context-aware recommendation [39] can also be done by analysing similar users with similar smart environments.

D. Resources and Energy Management Most popular approach of energy management in IoT is through smart plugs. Plugwise (shop.plugwise.com), Thinkeco- inc (shop.thinkecoinc.com), Belkin (www.belkin.com) provide similar functionalities and services where they capture energy consumption using smart plugs. These solutions analyse data in many different ways and presented the context information to the users using variety of different charts and graphs. These plugs can also be used to home automation as they can be switched ON and OFF remotely or conditionally. For example, a condition would be temporal (i.e. time-aware behaviour) or spatial (i.e. location-aware behaviour). There aren’t any IoT solutions that focus on planning or deployment stages of smart environments. Analyse energy consumption is important in both industrial large scale de- ployments (e.g. waste management solutions discussed in [8]) and in consumer based smart home and office deployments. Fig. 14. (a) shows how a recipe is structured using conditional statements and Lets consider a smart home office planing and deployment actions. (b) shows how recipes are built combining different triggers, actions, scenario. At the moment, IoT marketplace is flooded with and channels. large number of IoT smart products that offer different func- tionalities. However, there aren’t any method for consumers to measure or compare the benefits these products may offer standard software platform called AllJoyn. AllJoyn is a system and the associated costs such as cost of purchase, installation that allows devices to advertise and share their abilities with and maintains. Further, it is very hard to understand which other devices around them. A simple example would be a solutions can work together and complement each other and motion sensor letting a light bulb know no one is in the room which work standalone. it is lighting. This is the ideal approach the interoperability It is also difficult to understand where to install certain among IoT products. However, security [38] and privacy in smart products and how many products are required to cover a this framework need to be strengthen to avoid using interop- certain area. (e.g. what are the ideal locations to install micro- erability features to attack IoT products by hackers or evil climate sensors within a building which enable to accurately parties. identify the micro-climate behaviour). Another issue would Another approach to enable interoperability among different be to determine the coverage of a product. For example, how IoT solutions is through adapter services. For example, IFTTT many motion sensors are required for a given home or office. (ifttt.com), If This Then That, is a web based service that Currently, to best of our knowledge, there is no such tool that allows users to create powerful connections, chains of simple can be used to achieve above planning and installation tasks. conditional statements. One simple statement is illustrated in As we mentioned before, consumers are always eager to know Figure 14. Channels are the basic building blocks of IFTTT. the costs and benefits of a products. Therefore, it is important Each Channel has its own Triggers and Actions. Some example to facilitate some tools that can demonstrate cost benefit Channels could be Facebook, Twitter, weather, Android Wear, analysis (e.g. purchase cost, maintenance cost such as energy, and so on. Channel could be both hardware or software. energy saving and so on.). Context information will play a Service providers and product manufactures need to register significant role in this kind of tools where consumers may their services with IFTTT once. After that anyone interested ca need to input the budget, size of the building, their priorities use that product or service as a channel to compose any recipe. and expectations. The tool will need to make recommendations Example list of channels are listed here: ifttt.com/channels. to the consumers on which product to buy based on the Personal recipes are combinations of a Trigger and an Action product’s technical specification and other consumers’ reviews from active Channels. Example recipes are shown in Figure 14. and comments. For example, first recipe is defined to send a twitter message The planing and installation becomes much more critical to a family member when the user reaches home. This kind of in industrial settings. Let considers the agricultural sensing recipe can be used to offload responsibility from a child so the scenario, the Phenonet project, presented in [40]. Phenonet system automatically act on behalf of the child and sent a tweet describes the network of sensors collecting information over to their parents. Context-aware recommendation can also help a field of experimental crops. Researchers at the High Res- users to quickly configure channels in IFTTT. Context could be olution Plant Phenomics Centre [41] needs to monitor plant IEEE ACCESS 15 growth and performance information under different climate user preferences, and location information so the users will be conditions over time. offered different combinations to select from. It would be very valuable to have a tool that can help Resource management is also a critical task that need to planning large scale sensor deployments. For example, energy be done optimally in IoT domain. Previously, we discussed predictive models will help the users to decide what kind how data may transferred over the network as well as through of energy sources to be used and what kind of battery size different types of data processing devices in Figure 6. It to be used in each scenario. The amount of sensor nodes is hard to determine the optimal location5 to process data. require to cover a curtain geographical area should be able to Therefore, it is ideal to have a tool that is capable of evaluating accurately predicted based on the context information using a given software component6 against a given computational such tool. For example, in the agricultural sensing scenario, network architecture and deciding which location is optimal sensors deployments are planned by agricultural scientist who to conduct any kind of reasoning based on user preferences, have little knowledge on electronic, communication, or energy resource availability, context information availability, network consumption. Therefore, it is useful to have a user friendly tool communication availability and so on. that enables them to plot and visualise a large scale sensor deployment in virtual setting before getting into real world E. Privacy and Data Analytic deployments. Perera et al. [40] have present the agriculture scenario in detail. IoT marketplace is mainly composed with three parties, Context information plays a critical role in sensor configu- namely: device manufacturers, IoT cloud services and platform ration in large scale sensor deployments in IoT. The objective providers, and third party application developers [15]. All of collecting sensor data is to understand the environment these parities need to consider privacy as a serious requirement better by fusing and reasoning them. In order to accomplish and a challenge. In this section, we present some advice on this task, sensor data needs to be collected in a timely and preserving user privacy in IoT domain. location-sensitive manner. Each sensor needs to be configured Device Manufacturers: Device manufactures must embed by considering context information. Let us consider a scenario privacy preserving techniques into their devices. Specially, related to smart agriculture to understand why context matters manufactures must implement secure storage, data deletion, in sensor configuration. Severe frosts and heat events can have and control access mechanisms at the firmware level. Manu- a devastating effect on crops. Flowering time is critical for factures must also inform consumers about the type of data that cereal crops and a frost event could damage the flowering are collected by the devices. Moreover, they must also explain mechanism of the plant. However, the ideal sampling rate what kind of data processing will be employed and how and could vary depending on both the season of the year and the when data would be extracted out of the devices. Next, the time of day. For example, a higher sampling rate is necessary manufactures must also provide the necessary control for the during the winter and the night. In contrast, lower sampling consumers to disable any hardware components. For example, would be sufficient during summer and daytime. On the other in an IoT security solution, consumers may prefer to disable hand, some reasoning approaches may require multiple sensor the outside CCTV cameras when inside the home. However, data readings. For example, a frost event can be detected consumers will prefer to keep both inside and outside cameras by fusing air temperature, soil temperature, and humidity active when they leave the premises. Moreover, devices man- data. However, if the air temperature sensor stops sensing ufactures may also need to provide programming interface for due to a malfunction, there is no value in sensing humidity, third party developers to acquire data from the devices. because frost events cannot be detected without temperature. IoT Cloud Services and Platform Providers: It is likely that In such circumstances, configuring the humidity sensor to most of the IoT solutions will have a cloud based service that sleep is ideal until the temperature sensor is replaced and is responsible for proving advance data analysis support for starts sensing again. Such intelligent (re-)configuration can the local software platforms. It is very critical that such cloud save energy by eliminating ineffectual sensing and network providers use common standards, so that the consumers have a communication. choice to decide which provider to use. Users must be able to An ideal tool should be able to simulate different types seamlessly delete and move data from one provider to another of user scenarios virtually before the real world deployments over time. Such a possibility can only be achieved by following begin. Once deployed, another set of tools are required to a common set of interfaces and data formats. Most of the cloud advice and recommend, scientists and non-technical users, services will also use local software and hardware gateways on configuring sensor parameters. Configuring sensors in a such as mobile phones that act as intermediary controllers. optimal fashion would lead to longer operation time while Such devices can be used to encrypt data locally to improved maintaining required accuracy. It is important to develop the security and to process and filter data locally to reduce the tools in a modular and standard fashion so the manufacturers amount of data send to the cloud. Such methods will reduce of each IoT solution can add their products into a library the possibility of user privacy violation that can occur during of product which enables consumers to easily select (may the data transmission. be drag and drop and visualize) the product they prefer 5 for visualization purposes. Further, such tools will need to the device that is responsible for processing data 6A self contained algorithm that may take primary context information be able to combine different compatible products together as inputs and outputs secondary context information using any kind of data autonomously based on context information such as budget, reasoning technique [2]. IEEE ACCESS 16

Third Part Application Developers: Application developers have the responsibility to certify their apps to ensure that they do not contain any malware. Moreover, it is the developers’ responsibility to ensure that they present clear and accurate information to the users to acquire explicit user consent. Some (a) (b) critical information are: (1) the task that the app performs, (c) (2) the required data to accomplish the tasks, (3) hardware and software sensors employed, (4) kind of aggregation and data analysis techniques that the app will employ, (5) kind of knowledge that the app will derive by data processing. Users need to be presented with a list of features that the application provides, and the authorization that the user needs to give to activate each of those features. The control must be given to the user to decide which feature they want to activate. Central Hubs Moreover, in the IoT, acquiring user consent should be a Intermediary Nodes continuous and ongoing process. Consequently, the application (d) developers must continuously allow the users to withdraw, Sensors grant, or change their consent. Moreover, users must be given Fig. 15. Centralised Hubs are category of devices heavily used in IoT full access to the data collected by the IoT devices. solution. (a) Ninja sphere (b) ALYT Hub powered by Andorid (c) Samsung’s SmartThing Hub (d) Sensors and other components are connected to a F. Central Hubs centralised hub. These hubs are typically connected to permanent power sources and comprises comparatively high computational capabilities. Central hubs are commonly used in IoT solutions. A typical IoT solution may comprises a number of different components. For example, an IoT solution may have sensors, actuators, The problem in this approach is that each IoT solution processing and communication devices. Due to the nature, designers are eager to design their own centralized hub. sensors and actuators may need to deploy in certain location Such design approach significantly reduces the interoperability manner (e.g. door sensor must mount on the door). As a result among different products and services in the IoT marketplace. such sensors and actuators need to be small in size. Due to These hubs are tend to use custom firmware and software miniature size, it is not possible to enrich them with significant framework stacks. Unlike operating systems, they are mostly computational capacity. Similarly, most of the time these designed to run under specific hardware platforms and config- sensors and actuators would be battery powers (i.e. without urations. As a result, it makes harder for other IoT solutions having connected to permanent power sources). Therefore, to use or utilize other centralized hubs in the marketplace. energy management within those sensors and actuators is very Centralized hubs typically does not have any user interface. critical. As a result, such smaller devices cannot perform They are controlled and managed using smart phones, tablets, significant data processing tasks. On the other hand, these or computers. individual devices have only limited knowledge about a given context. For example, a door sensors may only know about the In order to stimulate the adoption of IoT solution among current status of the door. The knowledge that can be derived consumers, it is important to design a common software from such limited amount of data is very constrained. In order platform using common set of standard. The current mobile to comprehensively understand a given situation, context data app market is an ideal model for IoT domains as well where from number of sensors and actuators need to be collected, users may install different applications in order to enhance processed, and analysed. To address this issue, most of the their existing IoT products. Verification is required to check IoT solutions have been used a central hubs (sometimes called whether the required hardware devices is available to support ‘home hub’) or similar solutions as shown in Figure 15. the intended software application. This is similar to the some Typically, central hubs are larger in size compared to sensors mobile app stores validate the phone specification before and actuators. Further, they are capable of communicating pushing the each app to a smart phone. In comparison to using multiple wireless protocols such as WiFi, WiFi-direct, mobile phone domain, IoT domain is slightly complex where Bluetooth ZigBee, Z-wave, and so on. They are also capable hardware also play a significant role. A one possible solution of storing data for a significant time period. Typically, only is to use hardware adaptors. This means when a IoT product one central hub is required for a large area (e.g. house). These manufacture wants to design a product that is interoperable hubs may perform data processing and reasoning tasks (e.g. with a another hub in the IoT marketplace, they need to design triggering IF-THEN rules). Further these hubs are typically a hardware adaptor that may handle the interoperability using connected to the cloud services. Dispite the differences in , two-way conversions. in high-level, all of these hubs allows to add functionalities Finally, it is also important to highlight the necessity of over time (i.e. extend the functionalities they may offer), intermediation nodes that can perform multi-protocol commu- through installing new applications. An app could be a IF- nication, bridging short range protocols, and protocol con- ELSE procedure that explain a certain contextual behaviour versions [42]. For example, sensors that may use Bluetooth as illustrated in Figure 12. and ZigBee which can only communicate very short distance. IEEE ACCESS 17

To accommodate such sensors, intermediary nodes may be VII.CONCLUDING REMARKS required. The intermediate nodes may install throughout a In this survey, we reviewed significant number of IoT given location which may use with log range protocols to solutions in the industry marketplace from context-aware communicate with the central hub. The intermediate nodes computing perspective. We briefly highlighted the evolution may use short rage protocols to communicate with sensors of context-aware technologies and how they have become and actuators. increasingly popular and critical in today’s applications. First, we reviewed number of IoT products in order to identify context-aware features they support. At the same time, we also G. Legacy Devices categorized the IoT solutions in the market into five different Most of the IoT products in the marketplace comes with segments: smart wearable, smart home, smart city, smart own hardware components and software stacks. However, we environment, and smart enterprise. Finally, we identified and have increasingly seen that IoT solutions attempt to enrich discussed seven major lessons learned and opportunities for legacy devices with smart capabilities. One very popular solu- future research and development in context-aware computing tion is Nest (nest.com) thermostat. It has the capability to learn domain. Our ultimate goal is to build a foundation that helps from users over time about their behaviour and preferences us to understand what has happened in the IoT marketplace and control the temperature more efficiently and pro-actively. in the past so we can plan for the future more efficiently and This thermostat can be installed by replacing the existing non- effectively. smart traditional thermostats. Everything else connected to the heating systems would work seamlessly. ShutterEaze (shut- REFERENCES tereaze.com) is another example for enriching legacy devices. This example is more into home automation. ShutterEaze [1] L. Atzori, A. Iera, and G. Morabito, “The internet of things: A survey,” Comput. Netw., vol. 54, no. 15, pp. 2787–2805, Oct. 2010. [Online]. makes it easy for anyone to add remote control functionality Available: http://dx.doi.org/10.1016/j.comnet.2010.05.010 and automate their existing interior plantation shutters. No [2] C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, “Context shutters changing is required. aware computing for the internet of things: A survey,” Communications Surveys Tutorials, IEEE, vol. 16, no. 1, pp. 414–454, 2013. A slightly different example is Leeo (leeo.com). As illus- [3] A. Zaslavsky, C. Perera, and D. Georgakopoulos, “Sensing as a service trated in Figure 16, Leeo keeps track of smoke alarms, carbon and big data,” in International Conference on Advances in Cloud monoxide alarms, and the climate in home. If something is Computing (ACC-2012), Bangalore, India, July 2012. [4] H. Sundmaeker, P. Guillemin, P. Friess, and S. Woelffle, “Vi- not right, it sends notifications straight to the users phone. It is sion and challenges for realising the internet of things,” European important to note that, there is no communication between the Commission Information Society and Media, Tech. Rep., March legacy smoke detection devices / alarms and the Leeo device. 2010, http://www.internet-of-things-research.eu/pdf/IoT Clusterbook March 2010.pdf [Accessed on: 2011-10-10]. They are completely two different systems without any de- [5] G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, pendencies. Leeo get triggered by the sound that may produce and P. Steggles, “Towards a better understanding of context by other traditional alarms. This is a very good examples to and context-awareness,” in Proceedings of the 1st international symposium on Handheld and , ser. HUC ’99. demonstrate how to embed smartness to our homes without London, UK: Springer-Verlag, 1999, pp. 304–307. [Online]. Available: replacing existing legacy systems. More importantly, any kind http://dl.acm.org/citation.cfm?id=647985.743843 of replacing cost a significant amount to the consumers. This [6] G. Kortuem, F. Kawsar, D. Fitton, and V. Sundramoorthy, “Smart objects as building blocks for the internet of things,” Internet kind of solutions eliminates such unnecessary and extra costs Computing, IEEE, vol. 14, no. 1, pp. 44 –51, jan.-feb. 2010. [Online]. that may put consumers away from adopting IoT solutions. Available: http://dx.doi.org/10.1109/MIC.2009.143 The lesson we can learn is that if the legacy devices cannot [7] L. Atzori, A. Iera, and G. Morabito, “From smart objects to social objects: The next evolutionary step of the internet of things,” Commu- understand the context it operates and act intelligently, the new nications Magazine, IEEE, vol. 52, no. 1, pp. 97–105, January 2014. devices can be incorporated to embed smartness to the overall [8] C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, “Sensing system where new devices helps to mitigate the weaknesses as a service model for smart cities supported by internet of things,” Transactions on Emerging Telecommunications Technologies (ETT), in the legacy devices. vol. 25, no. 1, pp. 81–93, 2014. [9] E. Welbourne, L. Battle, G. Cole, K. Gould, K. Rector, S. Raymer, M. Balazinska, and G. Borriello, “Building the internet of things using rfid: The rfid ecosystem experience,” Internet Computing, IEEE, vol. 13, no. 3, pp. 48–55, 2009. [10] A. Caragliu, C. D. Bo, and P. Nijkamp, “Smart cities in europe,” in 3rd Central European Conference in Regional Science-CERS, October 2009, pp. 45–59. [Online]. Available: http://www.cers.tuke.sk/cers2009/ PDF/01 03 Nijkamp.pdf [11] A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of things for smart cities,” Internet of Things Journal, IEEE, vol. PP, no. 99, pp. 1–1, 2014. Fig. 16. Enriching smartness to legacy devices: Legacy devices may monitor [12] Libelium Comunicaciones Distribuidas S.L. , “WaspMote,” 2013, http: fire and smoke. Once these legacy devices detect any abnormalities, they //www.libelium.com/products/plug-sense [accessed on: 2013-08-13]. will trigger their alarms and start to make sounds. Leeo is designed to [13] A. Weaver and B. Morrison, “Social networking,” Computer, vol. 41, listen to such alarm sound. Once Leeo detects such sound, it triggers its no. 2, pp. 97–100, Feb 2008. reaction mechanisms such as sending notification to the users, neighbours, [14] N. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and and government authorities such as fire brigade in a predefined order. A. Campbell, “A survey of mobile phone sensing,” Communications Magazine, IEEE, vol. 48, no. 9, pp. 140 –150, sept. 2010. [Online]. Available: http://dx.doi.org/10.1109/MCOM.2010.5560598 IEEE ACCESS 18

[15] A. Ortiz, D. Hussein, S. Park, S. Han, and N. Crespi, “The cluster [34] S. Cirani, L. Davoli, G. Ferrari, R. Leone, P. Medagliani, M. Picone, between internet of things and social networks: Review and research and L. Veltri, “A scalable and self-configuring architecture for service challenges,” Internet of Things Journal, IEEE, vol. 1, no. 3, pp. 206– discovery in the internet of things,” Internet of Things Journal, IEEE, 215, June 2014. vol. 1, no. 5, pp. 508–521, Oct 2014. [16] A. Ahmed, T. Qiu, F. Xia, B. Jedari, and S. Abolfazli, “Event-based mo- [35] E. Ovaska and J. Kuusijarvi, “Piecemeal development of intelligent bile social networks: Services, technologies, and applications,” Access, applications for smart spaces,” Access, IEEE, vol. 2, pp. 199–214, 2014. IEEE, vol. 2, pp. 500–513, 2014. [36] F. Xia, N. Asabere, A. Ahmed, J. Li, and X. Kong, “Mobile multimedia [17] A. Campbell, S. Eisenman, N. Lane, E. Miluzzo, R. Peterson, H. Lu, recommendation in smart communities: A survey,” Access, IEEE, vol. 1, X. Zheng, M. Musolesi, K. Fodor, and G.-S. Ahn, “The rise of people- pp. 606–624, 2013. centric sensing,” Internet Computing, IEEE, vol. 12, no. 4, pp. 12–21, [37] J. Kiljander, A. D’elia, F. Morandi, P. Hyttinen, J. Takalo-Mattila, July 2008. A. Ylisaukko-Oja, J.-P. Soininen, and T. Cinotti, “Semantic interop- [18] A. K. Dey, G. D. Abowd, and D. Salber, “A conceptual framework erability architecture for pervasive computing and internet of things,” and a toolkit for supporting the rapid prototyping of context- Access, IEEE, vol. 2, pp. 856–873, 2014. aware applications,” Hum.-Comput. Interact., vol. 16, pp. 97– [38] S. L. Keoh, S. Kumar, and H. Tschofenig, “Securing the internet of 166, December 2001. [Online]. Available: http://dx.doi.org/10.1207/ things: A standardization perspective,” Internet of Things Journal, IEEE, S15327051HCI16234 02 vol. 1, no. 3, pp. 265–275, June 2014. [19] B. Schilit and M. Theimer, “Disseminating active map information to [39] G. Dror, N. Koenigstein, and Y. Koren, “Web-scale media recommenda- mobile hosts,” Network, IEEE, vol. 8, no. 5, pp. 22 –32, sep/oct 1994. tion systems,” Proceedings of the IEEE, vol. 100, no. 9, pp. 2722–2736, [Online]. Available: http://dx.doi.org/10.1109/65.313011 Sept 2012. [20] P. J. Brown, “The stick-e document: a framework for creating [40] C. Perera, P. P. Jayaraman, A. Zaslavsky, P. Christen, and D. Geor- context-aware applications,” Electronic Publishing, vol. 8, no. 2 & gakopoulos, “Mosden: An internet of things middleware for resource 3, pp. 259–272, 1996. [Online]. Available: http://citeseerx.ist.psu.edu/ constrained mobile devices,” in 47th Hawaii International Conference viewdoc/download?doi=10.1.1.8.7472&rep=rep1&type=pdf on System Sciences (HICSS), Kona, Hawaii, USA, January 2014, p. n/a. [21] D. Franklin and J. Flachsbart, “All gadget and no representation [41] CSIRO, “The high resolution plant phenomics centre,” June 2011, http: makes jack a dull environment,” Proceedings of the AAAI 1998 Spring //www.csiro.au/Outcomes/Food-and-Agriculture/HRPPC.aspx[Accessed Symposium on Intelligent Environments, vol. Technical Report SS-98- on: 2012-09-24]. 02, pp. 155–160, 1998. [Online]. Available: http://infolab.northwestern. [42] M. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L. Grieco, edu/media/papers/paper10072.pdf G. Boggia, and M. Dohler, “Standardized protocol stack for the internet [22] T. Rodden, K. Chervest, N. Davies, and A. Dix, “Exploiting context of (important) things,” Communications Surveys Tutorials, IEEE, vol. 15, in hci design for mobile systems,” in in Workshop on Human no. 3, pp. 1389–1406, 2013. Computer Interaction with Mobile Devices, 1998. [Online]. Available: http://eprints.lancs.ac.uk/11619/ [23] R. Hull, P. Neaves, and J. Bedford-Roberts, “Towards situated computing,” in Wearable Computers, 1997. Digest of Papers., First International Symposium on, oct 1997, pp. 146 –153. [Online]. Available: http://dx.doi.org/10.1109/ISWC.1997.629931 [24] A. Ward, A. Jones, and A. Hopper, “A new location technique for the active office,” Personal Communications, IEEE, vol. 4, no. 5, pp. 42 –47, oct 1997. [Online]. Available: http://dx.doi.org/10.1109/98.626982 [25] G. D. Abowd and E. D. Mynatt, “Charting past, present, and future research in ubiquitous computing,” ACM Trans. Comput.- Hum. Interact., vol. 7, pp. 29–58, March 2000. [Online]. Available: http://doi.acm.org/10.1145/344949.344988 [26] B. Schilit, N. Adams, and R. Want, “Context-aware computing applications,” in Mobile Computing Systems and Applications, 1994. Proceedings., Workshop on, dec 1994, pp. 85 –90. [Online]. Available: http://dx.doi.org/10.1109/MCSA.1994.512740 [27] J. Pascoe, “Adding generic contextual capabilities to wearable computers,” in Wearable Computers, 1998. Digest of Papers. Second International Symposium on, oct 1998, pp. 92 –99. [Online]. Available: http://dx.doi.org/10.1109/ISWC.1998.729534 [28] N. S. Ryan, J. Pascoe, and D. R. Morse, “Enhanced reality fieldwork: the context-aware archaeological assistant,” in Computer Applications in Archaeology 1997, ser. British Archaeological Reports, V. Gaffney, M. van Leusen, and S. Exxon, Eds. Oxford: Tempus Reparatum, October 1998. [Online]. Available: http://www.cs.kent.ac.uk/pubs/1998/ 616 [29] Carnot Institutes, “Smart networked objects and internet of things,” Carnot Institutes’ Information Communication Technologies and Micro Nano Technologies alliance, White Paper, January 2011, http://www. internet-of-things-research.eu/pdf/IoT Clusterbook March 2010.pdf [Accessed on:2011-11-28]. Charith Perera received his BSc (Hons) in Com- [30] A. Moses, “Lg smart fridge tells you what to buy, cook puter Science in 2009 from Staffordshire Univer- and eat,” The Sydney Morning Herald, January 2012, sity, Stoke-on-Trent, United Kingdom and MBA http://www.smh.com.au/digital-life/hometech/lg-smart-fridge-tells- in Business Administration in 2012 from Univer- you-what-to-buy-cook-and-eat-20120110-1ps9z.html [Accessed on: sity of Wales, Cardiff, United Kingdom and PhD 2012-04-04]. in Computer Science at The Australian National [31] M. Raskino, J. Fenn, and A. Linden, “Extracting value from University, Canberra, Australia. He is also worked the massively connected world of 2015,” Gartner Research, Tech. at Information Engineering Laboratory, ICT Centre, Rep., April 2005, http://www.gartner.com/resources/125900/125949/ CSIRO and involved in OpenIoT Project (FP7-ICT- extracting valu.pdf [Accessed on: 2011-08-20]. 2011.1.3) which is co-funded by the European Com- [32] X.-W. Chen and X. Lin, “Big data deep learning: Challenges and mission under seventh framework program. He has perspectives,” Access, IEEE, vol. 2, pp. 514–525, 2014. also contributed into several projects including EPSRC funded HAT project [33] H. Hu, Y. Wen, T.-S. Chua, and X. Li, “Toward scalable systems for (EP/K039911/1) His research interests include Internet of Things, Smart big data analytics: A technology tutorial,” Access, IEEE, vol. 2, pp. Cities, Mobile and Pervasive Computing, Context-awareness, Ubiquitous 652–687, 2014. Computing. He is a member of both IEEE and ACM. IEEE ACCESS 19

Chi Harold Liu is currently a Full Professor with the School of Software, Beijing Institute of Tech- nology, Beijing, China. He is the Director of the IBM Mainframe Excellence Center (Beijing), the Director of the IBM Big Data Technology Center, and the Director of the National Laboratory of Data Intelligence for China Light Industry. He received the Ph.D. degree from Imperial College London, London, U.K., and the B.Eng. degree from Tsinghua University, Beijing. Before moving to academia, he joined IBM Research, Beijing, as a Staff Researcher and the Project Manager, after working as a Post-Doctoral Researcher with Deutsche Telekom Laboratories, Berlin, Germany, and a Visiting Scholar with the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. His current research interests include the Internet of Things (IoT), big data analytics, mobile computing, and wireless ad hoc, sensor, and mesh networks. He was a recipient of the Distinguished Young Scholar Award in 2013, the IBM First Plateau Invention Achievement Award in 2012, and the IBM First Patent ApplicationAward in 2011, andwas interviewed byEEWeb as the Featured Engineer in 2011. He has authored over 60 prestigious conference and journal papers, and owned over 10 EU/U.S./China patents. He serves as an Editor of the KSII Transactions on Internet and Information Systems and a Book Editor of four books published by Taylor and Francis Group, USA. He served as the General Chair of the IEEE SECON’13 Workshop on IoT Networking and Control, the IEEE WCNC’12 Worksho on IoT Enabling Technologies, and the ACM UbiComp’11 Workshop on Networking and Object Memories for IoT. He served as a Consultant with Asian Development Bank, Manila, Philippines, Bain & Company, Boston, MA, USA, and KPMG, New York, NY, USA, and a peer reviewer for the Qatar National Research Foundation and the National Science Foundation in China. He is a member of the Association for Computing Machinery.

Srimal Jayawardena received his BSc (Hons) in Electrical Engineering from the University of Per- adeniya and his Bachelors in IT from the University of Colombo School of Computing, both with first class honours in 2004. He also obtained a Masters in Business Administration from the University of Moratuwa in 2009. He holds a PhD in Computer Science from The Australian National University, Canberra. He is currently working as post-doctoral research fellow at Computer Vision Laboratory (CI2CV),in CSIRO. His research interests include , object recognition for the Internet of Things, computer vision, human computer interaction, and machine learning. He is a member of the Institute of Electrical and Electronics Engineers (IEEE).

Min Chen is currently a Full Professor with the School of Computer Science and Engineering, Huazhong University of Science and Technology, Wuhan, China. He was an Assistant Professor with Seoul National University, Seoul, Korea. He has authored over 150 papers. He serves as an Editor or Associate Editor of Wireless Communications and Mobile Computing, IET Communications, IET Networks, the International Journal of Security and Communication Networks (Wiley), the Journal of In- ternet Technology, the KSII Transactions on Internet and Information Systems, and the International Journal of Sensor Networks. He is the Managing Editor of the International Journal of Autonomous and Adaptive Communication Systems. He was the Co-Chair of the IEEE ICC 2012 Communications Theory Symposium and the IEEE ICC 2013 Wireless Networks Symposium. He was the General Co-Chair of the 12th IEEE International Conference on Computer and Information Technology in 2012. His research focuses on multimedia and communications, such as multimedia transmission over wireless network, wireless sensor networks, body sensor networks, RFID, ubiquitous computing, intelligent mobile agent, pervasive computing and networks, E-healthcare, medical application, machine to ma- chine communications, and Internet of Things.