MetroTrack-DCOSS10 - MetroTrack: Predictive Tracking of...

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Unformatted text preview: MetroTrack: Predictive Tracking of Mobile Events using Mobile Phones Gahng-Seop Ahn 1 , Mirco Musolesi 2 , Hong Lu 3 , Reza Olfati-Saber 3 , and Andrew T. Campbell 3 1 The City University of New York, USA, gahn@ccny.cuny.edu 2 University of St. Andrews, United Kingdom 3 Dartmouth College, Hanover, NH, USA 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 be- fore 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 re- sults 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. 1 Introduction Urban sensing and tracking [1,5] is an emerging area of interest that presents a new set of challenges for traditional applications such as tracking noise, pollu- tants, objects (e.g., based on radio signatures using RFID tags), people, cars, or as recently discussed in the literature and popular press, weapons of mass de- struction [16]. Traditional tracking solutions [4, 7] are based on the deployment of static sensor networks. Building sensor networks for urban environments requires careful planning and deployment of possibly a very large number of sensors capa- ble of offering sufficient coverage density for event detection and tracking. Unless the network provides complete coverage, it must be determined in advance where the network should be deployed. However, it is challenging to determine where 2 the network should be deployed because events are unpredictable in time and space. We believe the use of static networks across urban areas has significant cost, scaling, coverage, and performance issues that will limit their deployment....
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This note was uploaded on 08/25/2011 for the course EEL 6788 taught by Professor Boloni,l during the Spring '08 term at University of Central Florida.

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MetroTrack-DCOSS10 - MetroTrack: Predictive Tracking of...

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