1 Mobile Phone Computing and the Internet of Things: A Survey Andreas Kamilaris∗ and Andreas Pitsillides† Centre for Data Analytics, National University of Ireland, Galway, Ireland Email: andreas.kamilaris@insight-centre.org † Department of Computer Science, University of Cyprus, Nicosia, Cyprus Email: andreas.pitsillides@cs.ucy.ac.cy ∗ Insight Abstract—As the Internet of Things and the Web of Things are becoming a reality, their interconnection with mobile phone computing is increasing. Mobile phone integrated sensors offer advanced services, which when combined with web-enabled realworld devices located near the mobile user (e.g. body area networks, RFID tags, energy monitors, environmental sensors etc.), have the potential of enhancing the overall user knowledge, perception and experience, encouraging more informed choices and better decisions. This paper serves as a survey of the most significant work performed in the area of mobile phone computing combined with the Internet/Web of Things. A selection of over 100 papers is presented, which constitute the most significant work in the field up to date, categorizing these papers into ten different domains, according to the area of application (i.e. health, sports, gaming, transportation, agriculture), the nature of interaction (i.e. participatory sensing, eco-feedback, actuation and control) or the communicating actors involved (i.e. things, people). Open issues and research challenges are identified, analyzed and discussed. Index Terms—Mobile Computing, Mobile Phone Applications, Internet of Things, Web of Things, Sensors, Survey. I. I NTRODUCTION Technologies such as wireless sensor networks, short-range wireless communications and radio-frequency identification (RFID) have allowed the Internet to penetrate in embedded computing [1], [2]. The Internet of Things (IoT) [3] is becoming reality, as everyday physical objects are becoming equipped with sensors and actuators, being (uniquely) addressable and interconnected, allowing the interaction with them through the Internet. Porting the IP stack on embedded devices was a successful effort [4], [5] and, together with the introduction of IPv6 (which provides extremely large addressing capabilities), facilitate the merging of the physical and the digital world, enabling the IoT to grow faster. Building upon the notion of the IoT, the Web of Things (WoT) [6], [7], [8] reuses well-defined Web techniques to interconnect this new generation of Internet-enabled physical devices. While the IoT focuses on interconnecting heterogeneous devices at the network layer, the WoT can be seen as a promising practice to achieve interoperability at the application layer. It is about taking the Web as we know it and extending it so that anyone can plug devices into it. The rise of multi-sensory mobile phones with Internet connectivity has helped to reduce the barriers for associating mobile computing with the IoT/WoT. Mobile phone integrated sensors offer advanced capabilities such as measuring proximity, acceleration and location or record audio/noise, sense electromagnetism or capture images and videos [9]. These sensing services, when combined with web-enabled physical entities located near the user, have the potential of enhancing the overall user knowledge and experience, helping the user to take more informed choices [10], [11]. Although there exist several survey papers focusing on advances related to the IoT/WoT [12], [13], [14], [15], not any papers -as far as we can ascertain- have yet surveyed the growing interconnection between mobile computing and the IoT/WoT. This paper aims to address this gap, presenting the most significant work in this area. Section II describes the methodology followed to develop this survey, while Section III comments on the important findings of our study. Then, Section IV discusses the overall observations during this study and identifies open issues in this research domain, and finally Section V concludes the paper. II. M ETHODOLOGY Before describing the important work in the field, the methodology in collecting this information is explained, which involved three steps: a) collection of the state of the art work, b) clustering of related work, and c) analysis of related work. In the first step, a keyword-based search for conference papers and articles in well-known scientific databases (e.g. IEEE Xplore, ACM, DBLP, ScienceDirect, CiteSeerX) and search engines was performed. Various keywords were used such as ”mobile computing”, ”internet”, ”web”, ”web of things” and combinations of them. Existing surveys on the IoT/WoT and mobile sensing [12], [13], [14], [16], [15], [9] were also studied for relevant efforts. The focus was to pick only papers which followed the main concepts and design principles of the IoT/WoT [6], [7], excluding papers that used proprietary protocols and in which vendor lock-in was evident. This means that some popular papers in mobile pervasive computing might have been excluded from our survey. Also, some cutting-edge research papers related to the exciting domain of feature- and depth-based identification, tracking of and interaction with the physical world have also been excluded, as these works have not yet become (widely) applied on mobile devices, due to their large processing requirements. Thus, 68 papers in total were firstly identified, performing then a depth search on their most relevant references, increasing the list of papers to 102. 2 In the second step, related work was categorized in clusters1 according to their topic/area of application. Ten clusters were created, in which the papers were placed with respect to their relevance to each cluster. These clusters are explained in detail in the next section. It is noted that these clusters were aimed to cover more or less the whole spectrum of combining mobile computing with the IoT/WoT. Some clusters had more studies performed than others while some other clusters involved also business cases (not only research efforts). In the final step, each cluster was examined carefully, studying and analyzing each paper separately, recording its summary, contribution and impact to the community, and its overall novelty and importance. After this procedure, at most 8-12 more significant works per cluster were selected, which are described in the next section. Hence, the rest of this survey is based on the analysis performed on the selected papers, as organized in the ten clusters. III. M OBILE C OMPUTING AND I NTERNET /W EB OF T HINGS We define the domain of mobile computing and IoT/WoT as the research area that involves case studies, prototypes, demonstrations, applications and business cases of the IoT/WoT, through mobile phones, where the user of the mobile phone interacts with cyber-physical things that are enabled to the internet/web, through his/her phone device, exploiting at the same time the sensing capabilities of the phone. As mentioned in Section II, related work is divided in different clusters or categories, which represent either the area of application (e.g. health, sports, gaming, agriculture, transportation), the nature of interaction/communication (e.g. participatory sensing, eco-feedback) or more general the type of interaction (e.g. with things, social interactions with other users). The ten different categories identified are graphically presented in Figure 1, together with the most common technologies used at each category (see also Table I), and are listed below: A. Participatory Sensing. Involves mobile systems encouraging users to record and share information, towards the co-creation of advanced knowledge. B. Eco-Feedback. Involves mobile applications that provide feedback to the users about various environmental phenomena or events, or about their personal consumption (water, electricity, energy etc.). C. Actuation and Control. Related to mobile applications that control some physical devices or actuate some events related to these devices. A typical example is home automation applications. D. Health. The mobile phone acts as an intermediary between body sensors and the web. It receives information about the health status of the user, measured by various sensor devices installed on the body of the user and then uploads/shares this information to the web for better analysis, comparisons and feedback. 1 The representation in clusters is suggested by the authors in order to provide a means to ease the readability and comprehension of the large amount of papers, and to group them in what seems to us a sensible classification. Some papers could have been included in more than one cluster. We selected the most relevant one at each case. Fig. 1. Mobile computing and IoT/WoT: Application categories. E. Sports. Includes a combination of physical sensors and mobile applications which are used during sport activities to record various metrics and help to improve the performance of the athlete user. F. Agriculture. Related to smart farming practices to improve productivity, management of livestock and increase consumer satisfaction and transparency. G. Gaming. This category is about virtual games which consider the physical presence or status of the mobile user to enhance the gaming experience. H. Transportation. A growing domain in which the sensing features of the mobile phone are harnessed for better driving experience and convenience of parking. I. Interaction with Things. This is the broader category as it relates to efforts which focus on interacting with web-enabled physical entities available in the nearby environment, as for example tagging technologies. J. Social Interactions with People. If we broaden the WoT to involve humans, we can identify various mobile apps that combine information from online social networking sites with information from the mobile phones of nearby users, to offer an augmented social experience. In the following subsections, each category is presented, listing the most significant work performed. General observations are discussed in Section IV. A. Participatory Sensing Participatory sensing involves the tasking of mobile devices to form interactive, participatory systems that enable individuals in the general public to gather, analyze and share local information, towards the co-creation of knowledge or addressing environmental challenges [17]. The MetroSense project [18] aims at transforming the mobile device into a social sensing platform. It is based on 3 Fig. 3. The eMeter system (source: [30]). Fig. 2. Sound exposure through the NoiseTube project (source: [22]). a three stage framework which involves sensing, learning and sharing. In the sense stage, MetroSense leverages mobilityenabled interactions between human-carried mobile sensors, static sensors embedded in the civic infrastructure and wireless access nodes providing a gateway to the Internet, to support the delivery of application requests to the mobile devices and the delivery of sampled data back to the application. Goldman et al. [19] show the significance of participatory sensing for our daily lives, namely its impact on climatic change. GPS-equipped mobile phones are used to photograph diesel trucks, in order to understand community exposure to air pollution. Odourmap [20] is a communication platform on which citizens and authorities can be involved into the odour management process. Portolan [21] builds signal coverage maps performing network monitoring based on crowd sourcing. In NoiseTube [22], mobile phones are used as noise sensors, to measure the personal exposure to noise of citizens, in their everyday environment. Figure 2 presents the sound exposure of a user during a walk in Paris through the NoiseTube project. The Common Sense project [23] derives design principles for describing data collection and knowledge generation from remote air quality data. In [24], a case study is performed, involving sensors deployed in public areas, shared by different communities. Users get informed of environmental conditions directly from these sensors. The findings indicate sensitivity to environmental factors from the people involved. Furthermore, participatory sensing can be extended to the health domain. A very nice example is about smartphone applications for melanoma detection [25]. Users use their phones’ camera to record unusual spots on their body, and then share with the online community, patients and generalist clinician users who provide feedback and informal diagnosis. Related work indicates that mobile sensing, both participatory and remote, is beneficial to the users and engages them in sustainable actions that improve their community [19], [24]. Mobile users become active members of their communities and raise their awareness towards their surroundings, while barriers for delivering large-scale environmental campaigns are reduced [17]. It constitutes an excellent approach for co-creating advanced knowledge based on a participatory scheme, and it could be used even for medical advice [25]. We need to comment however that these positive effects are often transient and only persist as long as users engage actively with the application. The evolution of participatory sensing is mobile crowd sensing for large-scale sensing [26], [27], which enables a broad range of applications including urban dynamics mining, public safety, traffic planning and environment monitoring. B. Eco-Feedback Eco-feedback mobile applications provide feedback to the users about their impact on the physical environment, such as their personal energy footprint (water, electricity, driving habits etc.) or useful information and knowledge about the existing local environmental conditions. Regarding eco-feedback on personal consumption, EnergyLife [28] developed a mobile application to provide electricity consumption feedback about domestic electrical appliances and conservation tips. In UbiLense [10], users can utilize their mobile phones as magic lenses to view the energy consumption of their home devices just by pointing on them with the phone’s camera. Ambient meter [29] is a mobile device that displays the level of energy consumption of the place it is currently located in, by changing its color from green (i.e. the amount of energy consumed in the room is low) to red (i.e. a lot of energy is consumed). Moreover, the eMeter system [30] allows users to interactively monitor, measure, and compare their energy consumption at a household and device level, by making use of a smart electricity meter and getting consumers ”into the loop” of energy monitoring, as displayed in Figure 3. Disaggregation of the electrical consumption of individual appliances within a household by means of a mobile application is discussed in [31], with accuracy rates of 87%. Social Electricity [32] is a mobile application that allows people to compare their energy consumption with their online friends, neighbors, similar peers etc., in order to perceive whether their footprint is high. In this case, the application assists the user to reduce his/her consumption through personalized tips and educational material. OPOWER is a US company that offers similar services through mobile phones [33]. PowerPedia [34] enables users to identify and compare the consumption of their residential appliances to those of others through a mobile application that uploads the measured data to a community platform. It thus helps users to better assess their electricity consumption and draw effective measures to save electricity. Furthermore, products such as CubeSensors [35] combine sensors and a mobile application to help people understand how their home or office is affecting their health, comfort and productivity. LoseIt [36] is an example of a nutrition tracking tool, which offers a barcode scanner for providing 4 Fig. 4. An example of an urban mashup (source: [11], [38]). information on various processed food options available at the supermarkets. Eco-feedback through mobile phones can also help to mitigate the negative effects of the increasing urbanism, to the people and the city as a whole. Urbanism implies that traffic is increased, levels of pollution are rising, some areas become dirty and polluted while health and security are compromised. To address some of the challenges implied by increasing urbanism, UrbanRadar [11] is a location-based application that discovers and interacts with environmental services offered by Web-enabled urban sensors. UrbanRadar allows the user to create urban mashups, defined as opportunistic web mashups that integrate real-world services, validated only when the local environmental conditions support the sensor-based Web services defined by these mashups. An example urban mashup is displayed in Figure 4. In this example, a sensor measuring levels of noise, a pollution sensor and a street camera combine their services to infer the traffic conditions existing at a city center. Air quality and pollution is studied in [37], using specialized sensors embedded in prototype mobile phones. By means of UrbanRadar, the work in [38] investigates and discusses the acceptance, influence, usefulness and potential of these services to mobile users, towards the vision of a real-time digital city. This case study suggested eleven design principles, which the authors consider important for future mobile applications that deal with remote sensing of the physical and urban environment. Timely eco-feedback can influence residents to reduce their consumption by a fraction of 5-15%, through more informed choices and better energy management [39]. It is important however to consider the fact that the positive effect might not be persistent for long time [40]. Mobile applications make the eco-feedback experience more personal, effective and convenient. Mobile applications on urban remote sensing can contribute toward the active involvement of citizens with their urban landscape. C. Actuation and Control In this category, papers are identified that use mobile devices as the tools to control physical devices such as electrical appliances. The most popular application domain is home automation, but other areas such as offices, factories and common spaces could be controlled through mobile applications. Regarding home automation and control, it is speculated that Internet technology, in line with IoT, will become the future standard [41], [42], offering advanced interoperability between heterogeneous home devices and appliances. The Aware Living Interface System (ALIS) [43] provides a mobile application for feedback and control of a smart home, such as allowing the resident to adjust the lights or shades for a house facade, with a single control. A mobile phone interface that allows users to monitor, control, and measure the consumption of single appliances at their home is presented in [44], where the mobile phones are connected to smart electricity meters through web protocols. Companies offering home automation services such as EnOcean [45] are producing solutions for home and building automation, based on Internet and web principles, enabling the control of the house/building environment through mobile phone applications. An interesting start-up company is n.thing [46], which has developed Planty, a system that assists plants growing through embedded sensors (soil humidity, temperature and light) and mobile applications. Planty’s status can be monitored in real-time remotely through the mobile phone and control its watering through a water pump whenever needed. Finally and more generally, a mobile phone application for creating physical mashups is introduced in [47]. Physical mashups [29] are defined as web mashups that combine sensory services using the classic techniques of Web mashups. This category offers mobile applications that control the real world by actuating physical devices such as home appliances. A strong focus of related work is on home automation as well as automation in offices/buildings towards more convenience and energy savings. D. Health In health-related mobile applications, the mobile phone acts either as an intermediary between body area sensors and the web or the sensors of the phone are used to identify abnormal behavior or indication of some disorder. In the first case, the mobile device receives information about the health status of the user, monitored by body sensors, and then shares this information to the web for further analysis and feedback by experts. In general, a wireless body area network is a wireless networking technology that interconnects tiny nodes with sensor or actuator capabilities in, on, or around a human body [16]. A system architecture of a wireless body area sensor network for ubiquitous health monitoring through mobile phones is presented in [48]. Zhong et al. [49] describe a Bluetooth-based body sensor network platform for physiological diary applications, using a wrist-worn device as the user interface, which displays information received from a mobile phone. The system described in [50] monitors the physiological signs of patients (electrocardiogram diagnosis) and records this information on a mobile application, which then analyzes the data and forwards it if needed to the medical center through the web. myHealthAssistant [51] is a phone-based body sensor network that captures the wearer’s exercises throughout the day, both daily activities and specific gym exercises, and then allows 5 comparisons and competitions with other people through the web. Rodrigues et al. [52] present an application that acquires, processes, stores, and displays medical data, thus enabling a full body sensor networking interface. A smart device for alerting a person’s critical health condition is presented in [53]. In the case when the sensors of the phone device are used to identify health issues, Madan et al. [54] use mobile phonebased co-location and communication sensing to measure characteristic behavior changes in symptomatic individuals, reflected in their total communication, interactions with respect to time of day, diversity and entropy of face-to-face interactions and movement. Using these extracted mobile features, it is possible to predict the health status of an individual, without having actual health measurements from the subject. More examples include identifying depressive symptoms based on user’s daily life behavior [55], therapy of insomnia [56] and stress recognition [57]. HealthyOffice exploits mood recognition to improve employees’ health and productivity [58]. Concerning nutrition, FoodCam [59] is a real-time mobile food recognition system recording a user’s eating habits. BodyBeat [60] is a mobile sensing system for capturing a diverse range of non-speech body sounds in real-life scenarios, such as sounds of food intake and breath, which contain invaluable information about our dietary behavior and respiratory physiology. Finally, the CrossCheck study [61] includes 150 schizophrenic patients who use a smartphone application that monitors their typical locations, when they are walking, running or sedentary, and detects and records the duration and frequency of conversations as well as sleep patterns. It then sends this data to health care providers who determine the improvement on the patients’ health. Mobile applications provide an unobtrusive way of managing the monitoring of the user’s health status through body sensors and have the potential of taking locally some decisions about critical situations. In such cases, their communication capabilities are used to immediately alert caregivers and medical centers remotely. Applications such as [54], [55], [57], [61] offer unique monitoring opportunities that allow the patients to have a normal life, being at the same time monitored for abnormal behavior that could indicate risks to themselves or to other people. It is a much promising research domain. Fig. 5. Snapshot of the BikeNet application (source: [63]). etc.) but also by sharing this information within the online cycling community. A snapshot of the web portal of BikeNet is illustrated in Figure 5. Also, measuring physical activity and promoting an active way of life is a subject of research. Mobile Teen [64] uses the mobile phone’s built-in motion sensor to automatically detect likely sedentary behavior. Fit Buddy [65] helps users track their personal fitness statistics focusing on step counting from both walking and running using a smartphone. Commercial solutions also exist, such as the Nike+ Sports Sensor [66], which puts a super smart sensor in shoes and uses pressure data in combination with an accelerometer to calculate movement. This information can then be recorded by smart watches and mobile phones and then shared through the web, to challenge online friends and other online users, set personal goals, train smarter, improve performance etc. Apple Watch [67] has an excellent design and co-exists with the user’s mobile device to track daily activity, encouraging the user to keep moving for health and fitness. Similar products and services are offered by other large companies such as Garmin [68] and Samsung [69] (smartwatches), as well as Suunto [70] (sports watches, dive computers and precision instruments). The aforementioned mobile applications make the sports experience more entertaining and fun for the user, helping him/her to improve performance through competitions and comparisons with friends, similar peers and the online community, encouraging a more active way of living. This community can create new useful knowledge, as for example the sharing of nice cycling pathways [63] recently discovered. F. Agriculture E. Sports Sports and recreational activities constitute one of the most rapidly growing areas of personal and consumer-oriented IoT and WoT technologies [62]. WoT-enabled mobile applications focusing on sports include various physical sensors, installed usually on the body or the clothes of the user, and which are used during sport activities to record various metrics and help to improve the user’s performance. The mobile phone in these cases records the measurements of the sensors and shares this information to the web. A typical example is the BikeNet application [63], which aims to give a holistic picture of the cyclist experience, not only by measuring various metrics (speed, distance traveled, calories burned, heart rate Smart farming [71] is about real-time data gathering, processing and analysis, as well as automation technologies on the farming procedures, allowing improvement of the overall farming operations and management and more informed decisionmaking by the farmers. Mobile applications interacting with the agricultural environment can facilitate management of cultivation and/or livestock, as well as consider the whole value chain from farm to fork, by considering consumer transparency over the product he/she buys. Examples include Herdwatch [72], a herd management system which allows cattle farmers manage their beef or dairy herds via a smartphone or tablet. MooMonitor+ [73] monitors each individual cow’s health and fertility by means of wearable 6 collars installed on the animals, allowing farmers to monitor their entire herd, from their phone. Map your meal [74] is an agriculture-related smartphone application which aims at enhancing the public awareness understanding of global interdependence via exploring the global food system. With this smartphone application, farmers and consumers can scan the products to see their fairness and how much green they are. Finally, FoodLoop [75] ties grocer inventory system and smart tagging to consumer-facing mobile applications to provide real-time deals on ”nearly expired” products, supporting food waste reduction. As agriculture is a highly unpredictable and risk-prone domain, smart sensing and real-time monitoring is important to prevent pests and animals diseases, react fast to changing environmental conditions, optimizing operations and productivity. Hence, combining IoT sensing technology with mobile applications, involving online information and web services [74], [75], permits faster reaction to unpredictable events and better decision making. G. Gaming This category is about virtual games which take into account the physical presence and characteristics of the mobile user to enhance his/her gaming experience. The use of personal sensor streams in virtual worlds is demonstrated in Second Life [76], which is a virtual world simulator where people lead virtual lives by using personal avatars. Accelerometer data is collected from a user mobile phone and classified into various activity states (sitting, standing and running). These activities are then injected into Second Life and interpreted by the user’s avatar [77]. Greenet [78] is an augmented reality mobile game dedicated to recycling. The camera of the mobile phone is used to detect whether the user is recycling various items. Object markers and bin markers are used to augment the physical world with this digital, mobile gaming experience. Users earn points by recycling, and compete with each other for more points through the web. Fish’n’Steps [79] is a social game which links a player’s daily foot step count, measured by a pedometer, to the growth and activity of an animated virtual character, a fish in a fish tank. As further encouragement, some of the players fish tanks included other players fish, thereby creating an environment of both cooperation and competition. Augmented reality is expected to be the future in gaming, and early examples include Monopoly Zapped and Game of Life2 [80]. The board games play out like the original versions but in the center of the board, an iPhone running the respective online application is placed to spice up the game. In Monopoly Zapped, the iPhone application turns the iOS device into a banking unit, which makes counting a lot less of a hassle. In general, online gaming integrations with the real world augment the user’s gaming experience and offer educational value, targeting in some cases good causes such as environmental protection or maintaining good health and wellbeing. Augmented reality would offer in the near future novel, 2 These augmented reality games do not constitute WoT applications. Their examples are mentioned by the authors only to show the potential of cyberphysical games. unique gaming experiences blending digital and physical world. Examples include mimicking with high accuracy people’s postures and movements in real life, transforming them into digital actions in the game [76], or harnessing the nearby physical environment as part of the digital game [78], [80]. A more futuristic example could be distributed gaming, where the players’ avatars are teleported at another player’s place or in a common place. H. Transportation Smart WoT-based mobile applications targeting transportation relate to more informed driving, easier parking and more convenient city transit by means of public transport. VTrack [81] uses position samples from drivers’ phones to monitor traffic delays. Similarly, Mobile Millennium [82] is a research project that includes a pilot traffic-monitoring system that uses the GPS in mobile phones to gather traffic information, process it, and distribute it back to the phones in real time. A road condition monitoring and alert application is presented in [83], harnessing the compass and connectivity on user’s mobile device to provide road condition based alerts to the user. Park Here! [84] is a smart parking system based on smartphones’ embedded sensors and short range communication technologies, towards the automatic detection of parking actions. Find My Car [85] makes use of GPS services and internet connectivity to help drivers find their way back to their parked car. Waze [86] is perhaps the most popular mobile app for car drivers, with a community of 50 million users. It provides up-to-date traffic conditions, live-routing and maps, comprehensive voice-assisted navigation, alerts about road hazards, and even notifications when a Facebook friend is heading towards the same destination. INRIX Traffic [87] offers similar services, automatically learning a user’s driving habits, personalizes routes to avoid traffic, recommends trips and departure times, providing also parking services, based on location, vehicle type and parking duration. Finally, examples of mobile applications that combine user’s location with public transportation information for convenient city transit involve GoMetro [88] (Los Angeles), Muni+ [89] (San Francisco) and Gothere [90] (Singapore). Transportation is an area where the authors expect more research efforts to rise, exploiting the sensors of the mobile device for providing better and safer driving experience. Commercial mobile apps focusing on driving assistance, such as Waze and INRIX Traffic are gaining popularity, especially in the US. I. Interaction with Things This category is the broader one, and relates to efforts targeting interaction in general with web-enabled things, located in the nearby environment. At first, a framework for supporting mobile interactions with real-world objects is presented in [91]. In [92], Frank et al. present the architecture, design and evaluation of a search system that relies on sensor-enabled mobile phones to discover lost objects. MobileIoT Toolkit [93] connects the Electronic 7 Product Code (EPC) network to mobile phones for reading and interpreting various tags (RFID, NFC, barcode). Furthermore, a toolkit for barcode recognition and resolving on mobile phones equipped with cameras is presented in [94]. Similarly, BIT [95] is a unified system architecture for providing mobile phone-based digital services related to tagging technologies. Tales of Things [96] is a tagging service that uses QR codes and RFID tags to enable people to attach stories and memories to any object. The scanning of readable and writable tags through mobile phones allows stories to be replayed and added. Scandit [97] is a company that claims to provide the highest quality in mobile barcode scanning solutions for smartphones, tablets and wearable devices. Other approaches target the sales market, to improve consumer awareness and optimize the sales process. The Mobile Sales Assistant [98] aims at improving sales in retail stores, by enabling shop assistants to check the availability and stock information of products directly with an NFC-enabled mobile phone. My2cents [99] allows consumers to have access to comments about a product via their mobile phone and share their own product experience with other consumers and within their social networks. APriori [100] makes product recommendation available for mobile users, who utilize their phones to identify tagged consumer products. Also, companies such as Square [101] and PayPal [102] offer credit card payment systems connected directly to mobile phones. Interacting with physical things through mobile applications includes a variety of technologies such as RFID, NFC, barcodes, QR codes, which can be either identified and sensed by the phone’s camera (barcodes, QR codes), or sensed by special equipment (RFID readers, credit card readers). Local information is combined with information from the web to create advanced knowledge or to implement payment transactions. J. Social Interactions with People If we broaden the notion of ”things” to include also (nearby) people, then we can refer to BlueAware [103], a mobile phone-based system that uses Bluetooth hardware addresses and a database of user profiles to cue informal, face-to-face interactions between nearby users who do not know each other, but probably should, as they have similar profiles, interests and characteristics. This concept of Familiar Strangers is also demonstrated in [104], where Jabberwockies, designed for Bluetooth enabled mobile phones, investigate the constraints of our feelings and affinities with strangers in pubic places. EmotionSense [105] is a passive monitoring smartphone application that can autonomously capture emotive, behavioral, and social signals from smartphone owners. Similarly, SociableSense [105] is a smart phone-based platform for providing real-time feedback to users to foster and improve social interactions. Also, CenceMe [106] is a mobile application serving a similar goal, by combining the inference of the presence of individuals with sharing of this information through social networking applications such as Facebook and MySpace. In a user study where 22 people used CenceMe continuously over a three week period in a campus town, it was observed that participants found it as a new way to connect with people, with the presence of their friends always nearby. Finally, Kostakos et al. [107] use a Bluetooth-based infrastructure to identify characteristics of urban environments such as mobility patterns, social and spatial structures etc. Similarly, the Wireless Rope [108] is a framework to study the notion of social context and the detection of social situations using Bluetooth-based mobile devices for proximity detection, and its effects on group dynamics. Low-power short-distance wireless technology (e.g. Bluetooth, RFID) can be used to improve interactions with nearby individuals, for improving physical social experiences. IV. D ISCUSSION AND O PEN I SSUES As the previous section shows, an increasing number of works are combining mobile computing and the IoT/WoT to provide more personalized, enriched services to the users. In this section, the general findings are discussed and open challenges in this domain are identified. A. Analysis of Related Work Through this review of related efforts, the following eight interaction possibilities have been identified: 1) The built-in sensors of the mobile phone are used in a participatory manner to co-create knowledge on the internet/web (e.g. [19], [22], [23], [24], [25], [81], [82]). 2) The mobile phone is used as a tool for getting feedback about the nearby environmental conditions and/or the personal energy footprint of the user using internet/web protocols (e.g. [28], [10], [29], [30], [31], [32], [34]). 3) The mobile phone uses internet/web principles to control physical devices such as electrical appliances, which are located either nearby or remotely. (e.g. [43], [44], [45], [46], [47], [29]). 4) The mobile phone acts as an intermediary between body sensors and the internet/web, for health/sports applications (e.g. [49], [50], [51], [54], [61], [63], [66]). 5) The built-in sensors of the mobile phone are used to enhance the gaming experience of the user in web-based (e.g. [77]), mobile (e.g. [78], [79]) or cyber-physical games (e.g. [80]). 6) The mobile phone interacts with real-world objects, using knowledge available on the web to better inform the user about these physical things (e.g. [93], [100], [96], [98], [99]). 7) The mobile phone becomes a credit card payment system (e.g. [101], [102]). 8) Low-power short-distance wireless communication (e.g. Bluetooth) and information from online social networking sites is used to promote face-to-face interactions with nearby mobile users who share similar interests (e.g. [103], [104], [106]). The most common sensing technologies employed in each of the ten categories, together with the type of data analysis involved, are listed in Table I (see also Figure 1). The technologies employed are either embedded natively onto the mobile device or involve third-party sensors interacting 8 directly with the mobile phone. Data analysis is performed either on the mobile phone or on the crowd/web. The latter is needed for more computationally expensive operations and data integration from various mobile phones/sensors. The last column of Table I indicates the estimated market value per category in the next 5-10 years combining a number of sources, including [109], [110], [111], [112]. According to them, several trillion US dollars will be spent on IoT solutions (indicatively, estimates by [109] $6 trillion, [110] $4-11 trillion, [111] $14.4 trillion). Tech leaders predict the greatest potential revenue growth for IoT in the next three years is in consumer and retail markets (22%), with IoT/WoT expected to see significant revenue growth in technology industries (13%), aerospace and defense (10%), and education (9%). The share of the market for mobile phone computing/IoT has not been analysed to date by industry, at least publicly, but we expect it will be a sizable share of the IoT market. Our own assessment is that actuation and control, gaming, health and transportation are the domains with the highest market value and potential. In regard to the impact of these categories to people’s lives, mobile applications in health, sports, transportation and gaming seem to be highly impactful, especially those that constitute commercial products (i.e. Nike+, Apple Watch, Second Life, Waze, GoMetro). Participatory sensing-based applications have been successfully used only in local projects, addressing particular community problems for specific time. The engagement of the users in the long run remains questionable. Mobile applications in eco-feedback and actuation/control are gaining momentum, in domains such as home and building automation, and industrial control. Finally, the impact of mobile apps in agriculture and real-life interaction with things/people remains to be seen, being low at the moment. B. Open Challenges Some general IoT/WoT challenges are discussed in related surveys [12], [13], [14], [15]. Hence, the focus is on particular issues that appear in mobile computing when integrated to the IoT/WoT, discovered through this study, which are listed below. Heterogeneity, either because of IoT/WoT devices/services that provide proprietary communication protocols or because of heterogeneous consumers of data with different requirements and needs. In the former case, although the communication at the application layer becomes standardized through the WoT principles, the lower-level interaction remains a challenge. In the latter scenario, some users might ask for realtime information while some others might need historical data. Their needs regarding data quality, spatial resolution, and sampling rates vary, hence IoT/WoT and mobile computing need to be open to support diverse applications and requirements. Continuous sensing. Some IoT/WoT-enabled mobile applications require continuous sensing, signal processing, analysis and inference which asks for more computation, memory, storage, sensing, and communication bandwidth. Recommended approaches include sending only compressed summaries over the air (rather than raw data) or involve delay-tolerant models adaptable to the application needs. Opportunistic sensing could be another option [18], [9], in which sensing is a secondary, low-priority operation on the mobile device. Crowd sensing. Participatory sensing as well as mobile crowd sensing have various open issues, which are identified in related work [26], [27]. Some of these issues in particular include: • How can the sensing opportunities and sensing quality be measured? • How to deal with incomplete, noisy and unreliable data? • How many mobile users can provide enough sensing opportunities to achieve the required sensing quality? • How to tackle the fact that people always move around a set of popular locations, instead of purely random movements? • How to tackle the fact that each individual shows preference for some particular locations? • How to avoid using up significant battery that could prevent users from accessing their usual services? The context problem occurs because phones may be exposed to events for too short a period of time (e.g. if the user is traveling quickly, if the event is local and spontaneous (e.g. a sound) or the sensor requires more time to gather a sample (e.g. air quality sensor). Phone calls could also interfere in collecting relevant context. An idea would be to leverage colocated mobile phones for localized participatory sensing. Security is a top priority in mobile computing, and becomes more important when interacting with the IoT/WoT. One practical consideration is ensuring security of shared resources (e.g. WoT-enabled devices/services) against misuse by unauthorized mobile users. Relay infrastructures for secure access to embedded systems such as Yaler [113] could form part of the solution here. Privacy in terms of sharing personal information through the web is one of the most important research challenges. How much privacy do applications such as CrossCheck [61] and the work in [54] provide? Additionally, various privacy threats have been reported in mobile RFID applications [114]. Some approaches to address privacy include AnonySense [115], which suggests a notion of anonymity through k-anonymous tasking and Social Access Controller (SAC) [116], which is an authentication proxy between (mobile) users and smart things. Perhaps more interesting is the work in [117], preserving the anonymity of sensor reports without reducing the precision of location data. Reliability becomes important especially in health monitoring applications that involve body area networks. In these cases, measurements need to be reliable and precise and the corresponding mobile applications needs to be able to identify anomalies and faults at the data, which could create false alarms. These concerns are discussed in [52] too. Reliability relates also to how well mobile phones can interpret human behavior (e.g. sitting, sleeping, talking) from low-level multimodal sensor data, or similarly how accurately can they infer the surrounding context (e.g. pollution, weather, noise environment) from low-level measurements. UrbanRadar [11] proposed urban mashups as a way to infer various environmental conditions from more basic WoT-based services. 9 Category Participatory Sensing EcoFeedback Actuation and Control Health Sports Popular technologies used GPS, camera, microphone. Camera, energy monitors, smart meters, barcodes, environmental sensors e.g. air quality. Smart electricity meters, smart appliances, light switches, smart factory sensors/actuators. Body area sensors, GPS, microphone, accelerometer, communication and conservation sensing (e.g. phone calls, SMS). Body area sensors, motion sensors, GPS, pressure (shoe) sensors, pedometers. Wearable collars, GPS, barcodes, RFID tags, plant (soil) sensors. Accelerometer, gyrometer, camera, pedometer, barcodes, RFID tags. GPS, compass, carrier connectivity, RFID tags. Type of data analysis Location-based search, map visualizations, information sharing. Historical comparisons, information retrieval, stream data processing. Market value N/A Rule-based inference, adaptive reasoning, optimizations. $1.3-4.6 Trillions Personalization and profiling, anomaly detection, emotions detection, stream data processing, machine learning. $110-900 Billions $200-750 Billions Personalization and profiling, historical com$200-450 parisons, performance visualizations, statistics, Billions information sharing. Agriculture Historical comparisons, information retrieval, $140-200 stream data processing, optimizations. Billions Gaming Activity recognition, machine learning, infor$450-635 mation sharing, image and video processing. Billions Transportation Location-based search, big data analysis, stream $500-740 data processing, anomaly detection, machine Billions learning, information sharing, image and video processing. Interaction NFC technologies, RFID tags, barLocation-based search, information retrieval, $70-150 with Things codes, QR codes, credit card deinformation sharing, personalization and profilBillions vices. ing, recommender systems, transactions. Social Bluetooth, GPS, camera, microLocation-based search, information retrieval $170-450 Interactions phone. and sharing, personalization and profiling, emoBillions with People tions detection, recommender systems. TABLE I S ENSING TECHNOLOGIES AT EACH CATEGORY OF I OT/W OT- BASED MOBILE APPLICATIONS . Search and Discovery is crucial for locating nearby, local and/or relevant real-world devices (and/or people), exploiting their services for the creation of more advanced knowledge. Various works and applications such as [35], [11], [29], [103], [104], [106] require discovery of relevant physical devices. The WoT has not yet standardized any technique for realtime discovery of physical entities and their services, but early efforts in this direction have been proposed [118], [119]. Personalization is expected to give significance to the interaction with the IoT/WoT through mobile computing. Planty [46] could understand the user’s sense of aesthetics and help him create his personal garden. Sports apps [66], [67], [68], [70] could record the personal characteristics and achievements of the users to suggest realistic short- and longterm goals. My2cents [99] could record the buying habits of people and propose them relevant nearby offers. Imagine also walking into a pharmacy and your phone suggesting vitamins and supplements, depending on your health monitoring metrics, as recorded by body area sensors [50], [51], [52]. Mobile computing, personalized to a user’s profile, empowers him/her to make more informed decisions across a spectrum of WoTenabled services. Emotions analysis relates to personalization as it allows to offer better services to users according to their reactions in relation to the features provided by the mobile applications. The most important efforts in understanding emotions and measuring feelings by monitoring the affective states of the mobile user are discussed in [120], concluding that affective sensing offers exciting research opportunities. Research in this area involves sensing unconscious emotions too [121], aiming to quantify and log aspects of our behavior we are not aware of. Mood recognition is applicable in working environments too to benefit employees’ health and productivity [58]. Persuasion in mobile computing is an open area, creating design elements and exploiting various psychological/sociological factors which affect users to change their behavior towards better quality of living, more sustainable life, environmental awareness etc. [122], [123]. The connection between mobile computing and IoT/WoT offers great new opportunities for examining which design characteristics of mobile phone applications and which motivational factors may influence users to question and change their behavior and way of living. As an early effort, the work in [38] identified 11 design principles for remote sensing mobile applications. A big challenge related to persuasion is how to address the fact that people tend to lose their interest after being exposed to the feedback for some weeks or months [40]. Big data analysis is a necessary step in most participatory sensing applications, in order to extract useful information from vast amounts of real-world data streams. Related to this, an open, standard methodology for IoT/WoT analytics problems is still non-existent. Important to researchers in this domain is the availability of relevant datasets. CrowdSignals.io [124] aims to create the largest set of rich, longitudinal mobile and sensor data recorded from smartphones and smartwatches, including geo-location, sensor, system and network logs, user interactions, social connections, and communications as well as user-provided ground truth labels and survey feedback. Business cases are required to prove the market potential of the IoT/WoT and mobile computing, showcasing that the merging of digital and physical world through web technologies and mobile computing is profitable to enterprises 10 worldwide. Evrythng [125] is a start-up company aiming to demonstrate, through various case studies [126], the economic viability of IoT/WoT-based investments. Solutions offered include brand protection, inventory management, consumer loyalty and engagement, data monitoring and analysis etc. Each of the issues listed above affect in a varying degree the ten different categories in which related work was divided in this survey. According to the authors’ point of view, based on the overall research performed in this paper, the relevance and importance of each of the aforementioned issues in relation to each category is assessed in Table II, according to the following scale: Essential (E), Very Important (VI), Important (I), Less Important (LI) and Not Applicable (NA). Observing Table II, aspects of security, privacy and reliability are essential in ”sensitive” domains such as health and transportation. Search and discovery is relevant only when interacting with things or people, while big data analysis makes sense in open systems, when large streams of data are involved. Heterogeneity and context are important in almost every category, while business cases are needed across the whole mobile IoT/WoT realm, not only to prove the value of mobile IoT/WoT but also to analyze the barriers for market uptake of various solutions especially in health, gaming and sports. Moreover, personalization, persuasion and emotions analysis are relevant in applications involving feedback, such as in eco-feedback, health, sports and transportation. Finally, other mobile phone application domains which will potentially be important in the future include smart cities [127] (i.e. more interactive and informed mobile phone assistants), tourism [128] (i.e. personalized location-based tourist guides), micro-grids of electricity [129] (i.e. users could trade electricity produced by local renewable energy sources) and smart metering [130], [131], disaster management [132] (i.e. mobile phones could form ad hoc wireless networks contributing to rescue operations), collaborative economy [133] (i.e. users share things and services and perform legal transactions through their mobile phones) and nano-networking [134] (i.e. nano-sensors inside the human body would collaboratively collect health-related data, transmitting it to the user’s mobile device for anomaly detection). V. C ONCLUSION This paper performed a survey on the most significant efforts in the area of mobile computing combined with the IoT/WoT, an exciting research domain in which mobile phone applications exploit the sensing of the real world through internet/web technologies for providing better information and more advanced knowledge to the user, helping him/her to take more informed decisions during everyday life. More than 100 papers have been identified, analyzed and divided in ten different categories, which represent either the area of application (i.e. health, sports, gaming, transportation, agriculture), the nature of interaction (i.e. participatory sensing, eco-feedback, actuation and control) or the communicating actors involved (i.e. things, people). Open issues and research challenges at this research domain have been identified and discussed. Summing up, the practice of combining mobile computing and the IoT/WoT seems to offer tremendous new opportunities in many real-life domains, enabling the seamless integration of devices, services and information that can create advanced knowledge, more informed reasoning and decision-making, as well as encouraging big data analysis, more use of persuasive and engagement techniques, emotions analysis and personalization. However, this openness comes together with various risks in privacy, security and reliability of information, and also various challenges such as search and discovery of devices, services and information on the fly, which is still an open issue in the WoT world [118]. Finally, the most critical element seems to be a need for large business cases which could prove the market value of mobile computing and the IoT/WoT, and which could eventually lead to wide-scale adoption of this practice, inspiring even more research and development in satisfying the risks and challenges at this field. VI. ACKNOWLEDGEMENTS This research has been partially supported by Science Foundation Ireland (SFI) under grant No. SFI/12/RC/2289 and EU FP7 CityPulse Project under grant No.603095. http://www.ictcitypulse.eu R EFERENCES [1] Lars Schor, Philipp Sommer, and Roger Wattenhofer. Towards a Zero-Configuration Wireless Sensor Network Architecture for Smart Buildings. In Proc. BuildSys, Berkeley, California, USA, 2009. [2] Dogan Yazar and Adam Dunkels. Efficient Application Integration in IP-based Sensor Networks. In First Workshop On Embedded Sensing Systems For Energy-Efficiency In Buildings (BuildSys), Berkeley, California, November 2009. ACM. 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