Tracking and Predicting Moving Targets in Hierarchical Sensor Networks

Wang, Zhen; Li, Hongbin; Shen, Xingfa; Sun, Xice; Wang, Zhi

doi:10.1109/icnsc.2008.4525393

Cited by 36 publications

(19 citation statements)

References 13 publications

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“…Examples include distributed predictive tracking (DPT) (Yang & Sikdor, 2003), DCAT , and Hierarchical prediction strategy (HPS) (Wang et al, 2008). The DPT adopts a clustering based approach for scalability and a prediction based tracking mechanism to provide a distributed and energy efficient solution (Yang & Sikdor, 2003).…”

Section: Hybrid Methodsmentioning

confidence: 99%

Target Tracking in Wireless Sensor Networks

Li¹,

Zhou²

2010

Wireless Sensor Networks: Application-Centric Design

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Section: Hybrid Methodsmentioning

confidence: 99%

Target Tracking in Wireless Sensor Networks

Li¹,

Zhou²

2010

Wireless Sensor Networks: Application-Centric Design

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“…In contrast, the target tracking in [11][12][13] uses the static cluster for the network scalability and energy efficiency (the term of "static cluster" does not mean that the cluster will not change during the network's entire lifetime. It means that the cluster structure will keep unchanged for a relatively longer period of time compared to a temporally formed dynamic cluster, until the next round of clustering process begins.…”

Section: Introductionmentioning

confidence: 99%

“…For example, it facilitates spatial reuse of resources to increase the system capacity [2]; it also benefits local collaboration and routing [3,4]. Recently, the cluster structure is gradually adopted for solving the target tracking problem [5][6][7][8][9][10][11][12][13]. Normally, nodes surrounding the target collaborate with each other to estimate the location of the target.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Cluster-Based Target Tracking Protocol for Wireless Sensor Networks

Wang

Lou

Wang

et al. 2013

International Journal of Distributed Sensor Networks

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Target tracking is a typical and important application of wireless sensor networks (WSNs). In consideration of the network scalability and energy efficiency for target tracking in large-scale WSNs, it has been employed as an effective solution by organizing the WSNs into clusters. However, tracking a moving target in cluster-based WSNs suffers a boundary problem when the target moves across or along the boundaries of clusters, as the static cluster membership prevents sensors in different clusters from sharing information. In this paper, we propose a novel mobility management protocol, called hybrid cluster-based target tracking (HCTT), which integrates on-demand dynamic clustering into a cluster-based WSN for target tracking. By constructing on-demand dynamic clusters at boundary regions, nodes from different static clusters that detect the target can temporarily share information, and the tracking task can be handed over smoothly from one static cluster to another. As the target moves, static clusters and on-demand dynamic clusters alternately manage the target tracking task. Simulation results show that the proposed protocol performs better in tracking the moving target when compared with other typical target tracking protocols.

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“…WSNs have wide application bandwidth emerging the areas of agricultural, medical, military, environmental, industrial control, monitoring, civil and mechanical, etc. Target tracking and continuous monitoring in WSNs are important energy hungry problems with a large spectrum of applications, such as surveillance [1], natural disaster relief [2], traffic monitoring [3] and pursuit evasion games, and so forth. A wide range of wireless sensor network applications helps in transforming human lives in various aspects of intelligent living technology.…”

Section: Introductionmentioning

confidence: 99%

A review on stochastic approach for dynamic power management in wireless sensor networks

Pughat

Sharma

2015

Hum. Cent. Comput. Inf. Sci.

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Wireless sensor networks (WSNs) demand low power and energy efficient hardware and software. Dynamic Power Management (DPM) technique reduces the maximum possible active states of a wireless sensor node by controlling the switching of the low power manageable components in power down or off states. During DPM, it is also required that the deadline of task execution and performance are not compromised. It is seen that operational level change can improve the energy efficiency of a system drastically (up to 90%). Hence, DPM policies have drawn considerable attention. This review paper classifies different dynamic power management techniques and focuses on stochastic modeling scheme which dynamically manage wireless sensor node operations in order to minimize its power consumption. This survey paper is expected to trigger ideas for future research projects in power aware wireless sensor network arenas.

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Tracking and Predicting Moving Targets in Hierarchical Sensor Networks

Cited by 36 publications

References 13 publications

Target Tracking in Wireless Sensor Networks

Target Tracking in Wireless Sensor Networks

A Hybrid Cluster-Based Target Tracking Protocol for Wireless Sensor Networks

A review on stochastic approach for dynamic power management in wireless sensor networks

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