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Mobile Crowdsensing: Current State and Future Challenges 1 Mobile Crowdsensing: Current State and Future Challenges Raghu K. Ganti, Fan Ye, and Hui Lei IBM T. J. Watson Research Center, Hawthorne, NY rganti,fanye,[email protected] ✦ Abstract—An emerging category of devices at the edge of the of mobile devices and their corresponding sens- Internet are consumer centric mobile sensing and computing ing capabilities are provided in Table 1. Future devices, such as smartphones, music players, and in-vehicle sen- sensing capabilities on smartphones include ECG sors. These devices will fuel the evolution of the Internet of Things (for medical purposes, e.g. ithlete, H’andy Sana), as they feed sensor data to the Internet at a societal scale. In this paper, we will examine a category of applications that we term mo- poisonous chemical detection (e.g. cell-all), and air- bile crowdsensing, where individuals with sensing and computing quality sensors (e.g. Intel’s EPIC, Figure 1). devices collectively share data and extract information to measure Different from the ”typical” everyday IoT ob- and map phenomena of common interest. We will present a brief jects (e.g., coffee machines) that traditionally lack overview of existing mobile crowdsensing applications, explain computing capabilities, these mobile devices have a their unique characteristics, illustrate various research challenges variety of sensing, computing and communication and discuss possible solutions. Finally we argue the need for a unified architecture and envision the requirements it must satisfy. faculty. They can serve either as a bridge to other everyday objects, or generate information about the environment themselves. We believe they will drive a plethora of IoT applications that elaborate our 1 INTRODUCTION knowledge of the physical world. The integration of sensing and embedded everyday These applications can be broadly classified into computing devices at the edge of the Internet will two categories, personal and community sensing, result in the evolution of an embedded Internet or based on the type of phenomena being monitored. the Internet of Things. Typical IoT devices include In personal sensing applications, the phenomena physical items tagged/embedded with sensors (e.g. is pertaining to an individual. For example, the chemical containers with temperature sensors), scis- monitoring of movement patterns (e.g. running, sors with IC-tags, and smart meters to remotely walking, exercising) of an individual for personal monitor energy consumption. An emerging cate- record-keeping or healthcare reasons. Another ex- gory of edge devices that we believe will result ample of personal sensing is one that monitors the in the evolution of Internet of Things are con- transportation modes of an individual to determine sumer centric mobile sensing and computing de- his or her carbon footprint. vices, which are connected to the Internet. These in- On the other hand, community sensing pertains clude smartphones (iPhone, Google Nexus), music to the monitoring of large-scale phenomena that players (iPods), sensor embedded gaming systems cannot be easily measured by a single individ- (Wii, XboX Kinect), and in-vehicle sensing devices ual. For example, intelligent transportation systems (GPS, OBD-II). They have become extremely pop- may require traffic congestion monitoring and air ular recently and are potentially important sources pollution level monitoring. These phenomena can of sensor data. They are typically equipped with be measured accurately only when many individu- various sensing faculty and wireless capabilities als provide speed and air quality information from that allow them to produce data and upload the their daily commutes, which are then aggregated data to the Internet. As an example, a sample list spatio-temporally to determine congestion and pol- Device Inertial Compass GPS Microphone Camera Proximity Light iPhone 4 X X X X X X X Nexus S X X X X X X X Galaxy S II X X X X X X X HTC Sensation X X X X X X X Garmin X X X ForeRunner 410 TABLE 1 Sensors on various mobile sensing devices (a) Sensors on Apple’s iPhone 4 (b) Intel’s air quality sensor (c) ECG sensor enabled mobile phone, that communicates with Blue- H’andy Sana tooth enabled mobile phones Fig. 1. Sensors on current iPhone 4 and future sensors that will possibly be integrated with mobile phones lution levels in cities. tions and tradeoffs. The research challenges that we Community sensing is also popularly called par- discuss include: (i) Localized analytics, (ii) Resource ticipatory sensing [1] or opportunistic sensing [2]. limitations, (iii) Privacy, security, and data integrity, Participatory sensing requires the active involve- (iv) Aggregate analytics, and (v) Architecture. ment of individuals to contribute sensor data (e.g. taking a picture, reporting a road closure) related 2 MOBILE CROWDSENSING APPLICA- to a large-scale phenomena. Whereas opportunistic sensing is more autonomous and user involvement TIONS is minimal (e.g. continuous location sampling with- In this section, we will briefly discuss existing out the explicit action from the user). We take mobile crowdsensing applications, which provide a the position that community sensing spans a wide basis for illustrating various research challenges in spectrum of user involvement, with participatory the rest of this article. We classify MCS applications sensing and opportunistic sensing at the two ends. into three different categories based on the type We therefore coin the term mobile crowdsensing of phenomenon being measured or mapped. These (MCS) to refer to a broad range of community include (i) Environmental, (ii) Infrastructure, and (iii) 1 sensing paradigms . Social. In the rest of this article, we will survey exist- In environmental MCS applications, the phenom- ing crowdsensing (both participatory and oppor- ena are those of the natural environment. Exam- tunistic) applications (Section 2), identify unique ples include measuring pollution levels in a city, characteristics of MCS applications (Section 3), and water levels in creeks, and monitoring wildlife discuss the research challenges they face, their solu- habitats. Such applications enable the mapping of various large scale environmental phenomena by 1. The notion that crowdsensing spans a spectrum from partic- ipatory sensing to opportunistic was suggested by our colleague involving the common man. An example prototype Thomas Erickson deployment for pollution monitoring is Common 2 Sense [3]. Common Sense uses specialized hand- individuals take pictures of what they eat and share held air quality sensing devices that communicate it within a community to compare their eating with mobile phones (using Bluetooth) to measure habits. A typical use case for this is for a community various air pollutants (e.g. CO2, NOx). These de- of diabetics to watch what other diabetics eat and vices when deployed across a large population, col- control their diet or provide suggestions to others. lectively measure the air quality of a community or To summarize, the functioning of typical MCS a large area. Similarly, one can utilize microphones applications is illustrated in Figure 2, which depicts on mobile phones to monitor noise levels in com- a number of research challenges as functional com- munities. Another example is CreekWatch devel- ponents. oped by IBM Almaden Research Center. It monitors water levels and quality in creeks by aggregating 3 MCS:UNIQUE CHARACTERISTICS reports from individuals, such as pictures taken at various locations along the creek, or text messages We will first illustrate the unique characteristics about the amount of trash. Such information can be of MCS applications that differentiate them from used by the water control boards to track pollution traditional mote-class sensor networks. This will levels in water resources. provide the reader with an idea about the research challenges faced by MCS applications. Infrastructure applications involve the measure- Compared to traditional mote-class sensor net- ment of large scale phenomena related to public works, mobile crowdsensing has a number of infrastructure. Examples include measuring traffic unique characteristics that bring both new oppor- congestion, road conditions, parking availability, tunities and problems. First, today’s mobile devices outages of public works (e.g. malfunctioning fire have significantly more computing, communica- hydrants, broken traffic lights), and real-time transit tion and storage resources than mote-class sen- tracking. Early MCS deployments measured traf- sors, and they are usually equipped with multi- fic congestion levels in cities, examples of which modality sensing capabilities. These will enable include MIT’s CarTel [4] and Microsoft Research’s many applications that require resources and sens- Nericell [5]. CarTel utilizes specialized devices in- ing modalities beyond current mote-class sensors stalled in cars to measure the location and speed of possess. Second, millions of mobile devices are cars and transmit the measured values using public already “deployed in the field”: people carry these WiFi hotspots to a central server. This central server devices wherever they go and whatever they do. can then be queried to provide information such as By leveraging these devices, we could potentially least delay routes or traffic hotspots. On the other build large scale sensing applications efficiently hand, Nericell utilizes individuals’ mobile phones (cost and time). For example, instead of installing to not only determine average speed or traffic road-side
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