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By Charu C. Aggarwal and Tarek Abdelzaher


A number of sensor applications in recent years collect data which can be directly associated with human interactions. Some examples of such applications include GPS applications on mobile devices, accelerometers, or location sensors designed to track human and vehicular traffic. Such data lends itself to a variety of rich applications in which one can use the sensor data in order to model the underlying relationships and interactions. This requires the development of trajectory mining techniques, which can mine the GPS data for interesting social patterns. It also leads to a number of challenges, since such data may often be private, and it is important to be able to perform the mining process without violating the privacy of the users. Given the open nature of the information contributed by users in social sensing applications, this also leads to issues of trust in making inferences from the underlying data. In this chapter, we provide a broad survey of the work in this important and rapidly emerging field. We also discuss the key problems which arise in the context of this important field and the corresponding solutions.

Topics: Sensor Networks, Social Sensors, Cyber-physical Networks C.C. Aggarwal (ed, Managing and Mining Sensor Data, DOI 10.1007/978-1-4614-6309-2_9, 237 © Springer Science+Business Media New York 2013238 MANAGING AND MINING SENSOR DATA
Publisher: 2013-09-21
Year: 2013
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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