Abstract
We propose to use mobile phones carried by people in their everyday lives as mobile sensors to track mobile events. We argue that sensor-enabled mobile phones are best suited to deliver sensing services (e.g., tracking in urban areas) than more traditional solutions, such as static sensor networks, which are limited in scale, performance, and cost. There are a number of challenges in developing a mobile event tracking system using mobile phones. First, mobile sensors need to be tasked before sensing can begin, and only those mobile sensors near the target event should be tasked for the system to scale effectively. Second, there is no guarantee of a sufficient density of mobile sensors around any given event of interest because the mobility of people is uncontrolled. This results in time-varying sensor coverage and disruptive tracking of events, i.e., targets will be lost and must be efficiently recovered. To address these challenges, we propose MetroTrack, a mobile-event tracking system based on off-the-shelf mobile phones. MetroTrack is capable of tracking mobile targets through collaboration among local sensing devices that track and predict the future location of a target using a distributed Kalman-Consensus filtering algorithm. We present a proof-of-concept implementation of MetroTrack using Nokia N80 and N95 phones. Large scale simulation results indicate that MetroTrack prolongs the tracking duration in the presence of varying mobile sensor density.
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References
Abdelzaher, T., Anokwa, Y., Boda, P., Burke, J., Estrin, D., Guibas, L., Kansal, A., Madden, S., Reich, J.: Mobiscopes for human spaces. IEEE Pervasive Computing 6(2), 20–29 (2007)
Bai, F., Sadagopan, N., Helmy, A.: IMPORTANT: A framework to systematically analyze the Impact of Mobility on Performance of RouTing protocols for Adhoc NeTworks. In: INFOCOM 2003, San Francisco, CA, USA (April 2003)
Camp, T., Boleng, J., Davies, V.: A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing. Special issue on Mobile Ad Hoc Networking 2(5), 483–502 (2002)
Cerpa, A., Elson, J., Estrin, D., Girod, L., Hamilton, M., Zhao, J.: Habitat Monitoring: Application Driver for Wireless Communications Technology. In: Workshop on Data Communications in Latin America and the Caribbean (April 2001)
Cuff, D., Hansen, M., Kang, J.: Urban sensing: Out of the woods. Communications of the ACM 51(3), 24–33 (2008)
Girod, L., Lukac, M., Trifa, V., Estrin, D.: The design and implementation of a self-calibrating distributed acoustic sensing platform. In: 4th International Conference on Embedded Networked Sensor Systems (SenSys 2006), pp. 71–84 (2006)
He, T., Krishnamurthy, S., Stankovic, J.A., Abdelzaher, T., Luo, L., Stoleru, R., Yan, T., Gu, L., Zhou, G., Hui, J., Krogh, B.: VigilNet: An Integrated Sensor Network System for Energy-Efficent Surveillance. ACM Transactions on Sensor Networks (2004)
Huang, Q., Lu, C., Roman, G.-C.: Spatiotemporal Multicast in Sensor Network. In: First ACM Conference on Embedded Networked Sensor Systems, SenSys 2003 (2003)
Johnson, D., Maltz, D.: Dynamic Source Routing in Ad Hoc Wireless Networks. In: Mobile Computing, pp. 153–181 (1996)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Transactions of the ASME Journal of Basic Engineering (March 1960)
Kansal, A., Somasundara, A.A., Jea, D.D., Srivastava, M.B., Estrin, D.: Intelligent fluid infrastructure for embedded networks. In: MobiSys 2004, pp. 111–124. ACM, New York (2004)
Ko, Y.-B., Vaidya, N.: Geocasting in Mobile Ad Hoc Networks: Location-based Multicast Algorithms. In: Workshop on Mobile Computer Systems and Applications (WMCSA 1999) (February 1999)
Lai, K., Feldman, M., Stoica, Chuang, J.: Incentives for cooperation in peer-to-peer networks. In: Workshop on Economics of Peer-to-Peer Systems (2003)
Nokia. Python for s60, http://wiki.opensource.nokia.com/projects/PyS60
Olfati-Saber, R.: Distributed Kalman Filtering for Sensor Networks. In: 46th IEEE Conference on Decision and Control (December 2007)
Purdue University. Cell phone sensors detect radiation to thwart nuclear terrorism, http://news.uns.purdue.edu/x/2008a/080122FischbachNuclear.html
Shneidman, J., Parkes, D.C.: Rationality and self-interest in peer to peer networks. In: Kaashoek, M.F., Stoica, I. (eds.) IPTPS 2003. LNCS, vol. 2735, pp. 139–148. Springer, Heidelberg (2003)
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Ahn, GS., Musolesi, M., Lu, H., Olfati-Saber, R., Campbell, A.T. (2010). MetroTrack: Predictive Tracking of Mobile Events Using Mobile Phones. In: Rajaraman, R., Moscibroda, T., Dunkels, A., Scaglione, A. (eds) Distributed Computing in Sensor Systems. DCOSS 2010. Lecture Notes in Computer Science, vol 6131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13651-1_17
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DOI: https://doi.org/10.1007/978-3-642-13651-1_17
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