Conference Paper

Tracking moving targets in a smart sensor network

Dept. of ECECS, Cincinnati Univ., OH, USA
DOI: 10.1109/VETECF.2003.1286181 Conference: Vehicular Technology Conference, 2003. VTC 2003-Fall. 2003 IEEE 58th, Volume: 5
Source: IEEE Xplore


Networks of small, densely distributed wireless sensor nodes are capable of solving a variety of collaborative problems such as monitoring and surveillance. We develop a simple algorithm that detects and tracks a moving target, and alerts sensor nodes along the projected path of the target. The algorithm involves only simple computation and localizes communication only to the nodes in the vicinity of the target and its projected course. The algorithm is evaluated on a small-scale testbed of Berkeley motes using a light source as the moving target. The performance results are presented emphasizing the accuracy of the technique, along with a discussion about our experience in using such a platform for target tracking experiments.

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    • "To our knowledge, the only work focused on light source tracking in sensor networks was presented by Gupta and Das [7]. Photo sensors are used as proximity sensors for detecting the light source target. "
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    ABSTRACT: We revisit the classic object tracking problem with a novel and effective, yet straightforward distributed solution for resource-lean devices. The difficulty of object tracking lies in the mismatch between the limited computational capacity of typical sensor nodes and the processing requirements of typical tracking algorithms. In this paper, we introduce an in-network system for tracking mobile objects using resource-lean sensors. The system is based on a distributed, dynamically-scoped tracking algorithm which alters the event detection region and reporting rate based on object speed. A leader node records the detected samples across the event region and estimates the object's location in situ. We study the performance of our tracking implementation on an 80-node test bed. The results show that it achieves high performance, even for very fast objects, and is readily implemented on resource-lean sensors. While the area is well-studied, the unique combination of algorithmic features represents a significant addition to the literature.
    DCOSS 2014; 05/2014
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    • "Most tracking protocols suggested up to now [1] [2] [3] [4] have not noticed the type of target mobility model sufficiently. In other words, is the efficiency of a tracking protocol the same when tracking a tank or a human? "

    International Journal of Computer Applications 05/2011; 22(9). DOI:10.5120/2612-3293
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    • "This can lead to fast energy exhaustion of nodes, and hence shortening the lifetime of the network. Many researchers try to overcome the point coverage problem by designing a suitable sleep-scheduling (or simply scheduling) mechanism for nodes in such a way that in each period of time, only nodes which can sense the target points in that period are awakened [13] [14] [15] [16] [17] [18] [19] [20]. In terms of dynamicity, this solution can deal with changes that occur in the topology of the network and position of target points using a dynamic scheduling mechanism. "
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    ABSTRACT: The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as few sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target point is in its sensing region. In this paper we propose SALA, a scheduling algorithm based on learning automata, to deal with the problem of dynamic point coverage. In SALA each node in the network is equipped with a set of learning automata. The learning automata residing in each node try to learn the maximum sleep duration for the node in such a way that the detection rate of target points by the node does not degrade dramatically. This is done using the information obtained about the movement patterns of target points while passing throughout the sensing region of the nodes. We consider two types of target points; events and moving objects. Events are assumed to occur periodically or based on a Poisson distribution and moving objects are assumed to have a static movement path which is repeated periodically with a randomly selected velocity. In order to show the performance of SALA, some experiments have been conducted. The experimental results show that SALA outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.
    Computer Networks 10/2010; 54(14-54):2410-2438. DOI:10.1016/j.comnet.2010.03.014 · 1.26 Impact Factor
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