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Metrotrack: Predictive Tracking of Mobile Events Using Mobile Phones

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Metrotrack: Predictive Tracking of Mobile Events Using Mobile Phones MetroTrack: Predictive Tracking of Mobile Events using Mobile Phones Gahng-Seop Ahn1,MircoMusolesi2,HongLu3, Reza Olfati-Saber3,andAndrewT.Campbell3 1 The City University of New York, USA, [email protected] 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 thetarget event should be tasked for the system to scale effectively. Second, there is no guarantee of a sufficient density of mobile sensors aroundanygiven 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. Toaddress these challenges, we propose MetroTrack,amobile-eventtrackingsystem 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 prolongsthetracking duration in the presence of varying mobile sensor density. 1Introduction 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 detectionandtracking.Unless the network provides complete coverage, it must be determined in advance where the network should be deployed. However, it is challenging todeterminewhere 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. An alternative design of such a sensor system, which we propose in this paper, is to use people’s mobile phones as mobile sensors to track mobile events. Increasingly, mobile phones are becoming more computation capable and embed sensors and communication support. Therefore, making a sensor network based on mobile phones is becoming more of a reality. For example, many high-end mobile phones, such as Nokia N95 phones, include a number of different radio technologies (e.g., multiple cellular radios, WiFi, and Bluetooth), and sensors (e.g., accelerometer, microphone, camera, and GPS) that areprogrammable.We imagine that micro-electro-mechanical systems (MEMS) technology will allow for the integration of more specialized sensors (e.g., pollution/air quality sensor, bio sensor, and chemical sensor) in the future. In our design,weassumethatwe can exploit the mobile phones belonging to people going abouttheirdailylives or defined groups (e.g., federal employees, transit workers,police).Ultimately, the more people who opt in to being a part of the sensor network,thebetter the density and sensing coverage will be and the more effectiveurbansensing system will become in delivering services. There are several important challenges in building a mobile event tracking system using mobile sensors. First, mobile sensors must be tasked before sens- ing [4]. Another issue that complicates the design of the system is that the mobility of mobile phones (therefore, the mobile sensors) isuncontrolled.This work diverges from mobile sensing systems that use the controlled mobility of a device (e.g., a robot) as part of the overall sensor system design. In such cases, the system can be optimized to drive the mobility of the sensors in response to detected events [11]. Due to the uncontrolled mobility of the mobile sensors, there is no guarantee that there will always be high enough density of mobile sensors around any given event of interest. The density changes over time so that sometimes there is a sufficient number of devices around the event to be tracked, and at other times, there is limited device density.Onecanthinkof this as dynamic sensor network coverage. The event tracking process has to be designed assuming that the process of tracking will be disrupted periodically in response to dynamic density and coverage conditions. Thus, a fundamental problem is how to recover a target when the system loses track of the target due to changing coverage. In this paper, we propose MetroTrack,asystemcapableoftrackingmobile events using off-the-shelf mobile phones. MetroTrack is predicated on the fact that a target will be lost during the tracking process, and thus it takes compen- satory action to recover the target, allowing the tracking process to continue. In this sense, MetroTrack is designed to be responsive to the changing density of mobile phones and the changing sensor network coverage. The MetroTrack system is capable of tasking mobile sensors around a target event of interest and recovering lost targets by tasking other mobile sensors in close proximity of the lost target based on a prediction of its future location. 3 MetroTrack is based on two algorithms, namely information-driven tasking and prediction-based recovery.Thetaskingisinformation-drivenbecauseeach sensor node independently determines whether to forward thetrackingtaskto its neighbors or not, according to its local sensor state information. If the sen- sor readings meet the criteria of the event being tracked, then the sensor node forwards the task to its neighbors, informing them it detected the event. The recovery is based on a prediction algorithm that estimates the lost target and its margin of error. MetroTrack uses a geocast approach similar to the algorithms in [12, 8] to forward the task to the sensors in the projected area of the target. In our prior work, Olfati-Saber [15] presented the Distributed Kalman- Consensus filter (DKF) that defined the theoretical foundation of distributed tracking of mobile events. In this paper, we extend this work and importantly implement it in an experimental mobile sensing network. We adapt the DKF for the prediction of the projected area of the target. MetroTrack does not have to rely on a central entity (i.e., a tracking leader) because MetroTrack tracks events based on local state and interactions between mobile phones in the vicinity of a target. Therefore, MetroTrack is simple, flex- ible, robust, and easy to deploy. However, we do not rule out the potential help from infrastructure. Also, mobile phones occasionally interact with the back-end servers using cellular or infrastructure-based Wi-Fi connectivity for initial task- ing purposes or to inform the back-end of the targets progress. In this paper, we focus on the interaction between mobiles and reserve the issues of the interaction with the back-end servers as future work. Also, we do not discuss what would provide the incentive for more people to opt in (even if we believe mechanisms devised for peer-to-peer systems can be exploited [13, 17]), nor do we discuss the important privacy,trust,andsecurity issues that predicate the wide-scale adoption of these ideas. Rather, we leave those issues for future work and focus on the proof of concept and evaluation of asystemthatiscapableoftrackingmobileeventsusingmobile phones. To the best of our knowledge, this is the first sensor-based trackingsystemofmobile events using mobile phones. The paper is organized as follows. Section 2 describes the information-driven tasking and the prediction-based recovery of MetroTrack. InSection3,wepresent the mathematical formulation of the prediction algorithm that is the basis for the prediction-based recovery. In Section 4, we discuss the implementation and the performance evaluation of MetroTrack. A proof-of-concept prototype of Metro- Track is implemented using Nokia N80 and N95 phones to show that MetroTrack can effectively track a mobile noise source in an outdoor urbanenvironment. Following this, in Section 5, we address the large-scale design space of Metro- Track which cannot be analyzed from a small-scale testbed deployment. Section 6presentssomeconcludingremarks. 4 2MetroTrackDesign 2.1 Information-driven Tasking The tracking initiation can be done in two ways, i.e., user initiation or sentry sensor [4] initiation. A user can request to track an event described by certain attributes when the target event is encountered. Another wayistorelyonsentry nodes to detect the event to be tracked. The sentry nodes can beselectedfrom mobile nodes that have enough power to periodically turn on their sensors and start sampling. When one of the sentry nodes detects an event that matches the pre-defined event description, the node initiates the tracking procedure. The device associated with the requesting user or first sentry node that has detected the event becomes an initiator. The tasking is a distributed process. Each neighboring sensor node that re- ceives the task message performs sensing. The sensor node does not forward the task message to its neighbors unless it detects the event. Thetaskmessageis forwarded by the sensors that are tasked and have detected theevent.Hence, the nodes in close proximity to the event are tasked and the size of the tasked region is one hop wider than the event sensing range. As a result, the sensors just outside the event sensing range are already tasked and ready to detect the event wherever it moves.
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