Distributed and energy-efficient target localization and tracking in wireless sensor networks

Lee, Jeongkeun; Cho, Kideok; Lee, Seungjae; Kwon, Taekyoung; Choi, Yanghee

doi:10.1016/j.comcom.2006.02.004

Cited by 59 publications

(68 citation statements)

References 11 publications

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“…4). Following discussions of acoustic, seismic, and magnetic signals in the literature [15], we assumed that the inverse-square law is a good model to calculate the signal strength for this simulation, where each signal strength is…”

Section: Simulation For Tracking By Signal Strengthmentioning

confidence: 99%

“…In this simulation, the neighborhood of a sensor included all the sensors that are one grid space away from the current one. Second, we adjust locations based on the observed ratio of signal strengths, a variation on the approach of [15]. If initially the observed signal strength is assumed to be only due to each sensor's nearest target, then, for any two sensors 1 and 2, the following holds true due to the assumed inverse-square law:…”

Section: Simulation For Tracking By Signal Strengthmentioning

confidence: 99%

See 1 more Smart Citation

Improving the localization accuracy of targets by using their spatial–temporal relationships in wireless sensor networks

Chen

Rowe

et al. 2012

Journal of Parallel and Distributed Computing

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Section: Simulation For Tracking By Signal Strengthmentioning

confidence: 99%

Section: Simulation For Tracking By Signal Strengthmentioning

confidence: 99%

Improving the localization accuracy of targets by using their spatial–temporal relationships in wireless sensor networks

Chen

Rowe

et al. 2012

Journal of Parallel and Distributed Computing

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“…Random objects are created and signals from these sources are received by the sensors in the field. Following discussions of acoustic, seismic and magnetic signals in the literature [9], the inverse-square law is a good model to calculate signal strength, where each signal strength is:…”

Section: Simulationsmentioning

confidence: 99%

“…For example, for an internal sensor on the grid, it has four neighbors one grid space away in the east, south, west, and north directions. Second, improve the localization accuracy based on observed ratio of signal strengths, a variation of the approach in [9]. If initially the observed signal strength is assumed only due to each sensor's nearest source based on the fact that the effects fall off fast with an inverse square, then for two sensors 1 and 2 nearest the same signal, the following holds true:…”

Section: Simulationsmentioning

confidence: 99%

Improving Localization Accuracy Using Spatio-Temporal Information of Objects

Chen

Rowe

Singh

2010

2010 IEEE International Conference on Communications

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Abstract-Improvised explosive devices (IEDs) are an increasingly serious military threat as is witnessed in Iraq and Afghanistan. To combat the IED emplacement, it is important to have persistent surveillance over time. Due to the low cost and capabilities of sensors, wireless sensor networks (WSNs) have tremendous potential for military and civilian surveillance. In this paper, we explore methods to improve an important aspect of surveillance: localization accuracy. Though there are many localization algorithms in the literature, all of them try to improve the accuracy from the side of sensor networks. In this paper, we tackle this problem from a new angle, that is, we look at the spatio-temporal relationships of objects we track, which, as far as we know, unprecedented in this attempt. We first develop algorithms that use spatial and temporal relationships of objects separately and then design ones that combine them. Experimental results show that all our proposed algorithms can improve localization accuracy, especially those combined ones. Moreover, since our methods use features related to objects themselves and not the underlying localization mechanism, they can be built on any localization algorithm to improve accuracy.

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“…Lee et al [12] propose a distributed energy-efficient target positioning and tracking algorithm, and study the RVI positioning issue based on distance ratio. Moreover, they propose a scheme that dynamically adjusts the lead nodes' reporting frequency, so that the amount of the status information of the target and energy consumption can be reduced.…”

Section: Related Workmentioning

confidence: 99%

Herd-Based Target Tracking Protocol in Wireless Sensor Networks

Xing

Wang

2009

Wireless Algorithms, Systems, and Applications

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Abstract. Target tracking is a killer application in wireless sensor networks (WSNs). Energy efficiency is one of the most important design goals for target tracking. In this paper, we propose a herd-based target tracking protocol (HTTP) with the notions of node state transition and herd-based node group for target tracking. A sensor node has three states, namely, sleeping state, sensing state, and tracking state. Each sensor node is associated with a weight to be used to make a state transition among the three states. When a target moves into a monitoring area, a cluster node is selected as the herd head that is responsible for reporting the target information to the sink in the network. The sensor node can adjust the frequency of data reporting according to the velocity of the target. Simulation results show that HTTP not only improves the energy efficiency, but also enhances the tracking accuracy.

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Distributed and energy-efficient target localization and tracking in wireless sensor networks

Cited by 59 publications

References 11 publications

Improving the localization accuracy of targets by using their spatial–temporal relationships in wireless sensor networks

Improving the localization accuracy of targets by using their spatial–temporal relationships in wireless sensor networks

Improving Localization Accuracy Using Spatio-Temporal Information of Objects

Herd-Based Target Tracking Protocol in Wireless Sensor Networks

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