Herd-Based Target Tracking Protocol in Wireless Sensor Networks

Xing, Xiaofei; Wang, Guojun; Wu, Jie

doi:10.1007/978-3-642-03417-6_14

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…(3) (4) where P(Q[) and P(Q ll) denote the probabilities when the sensor is located in Region I and Region II, respectively; E [C nl] and E [C nlI] denote the corresponding expected coverage of the node. As the nodes are deployed randomly, we get:…”

Section: B Definitionsmentioning

confidence: 99%

Square region-based coverage and connectivity probability model in wireless sensor networks

Xing¹,

Wang²,

Wu³

et al. 2009

Proceedings of the 5th International ICST Conference on Collaborative Computing: Networking, Applications, Worksharing

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Sensing coverage and network connectivity are two fundamental issues in wireless sensor networks (WSNs). Due to resource constraints of sensor nodes, it may not be possible, or necessary, to provide full coverage and/or connectivity in WSNs. Under a certain coverage and connectivity requirement, the node deployment strategy becomes a challenging issue in randomly deployed networks. In this paper, we propose a square regionbased coverage and connectivity probability model (SCCP), which reflects the relations among the coverage and connectivity rates, the number of sensor nodes, the sensing and communication ranges of sensor nodes, and the network size. This model can calculate the number of sensor nodes that need to be deployed for maintaining a certain coverage and/or connectivity rate. The simulation results have shown that the error-rate of node deployment is less than 5%, which is defined as the absolute difference between the number of sensor nodes obtained from the theoretical analysis and the number obtained from the simulation, divided by the number of sensor nodes obtained from the theoretical analysis. The proposed model is very useful in estimating the monitoring coverage and connectivity capacity when sensor nodes are distributed randomly and uniformly.

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Section: B Definitionsmentioning

confidence: 99%

Square region-based coverage and connectivity probability model in wireless sensor networks

Xing¹,

Wang²,

Wu³

et al. 2009

Proceedings of the 5th International ICST Conference on Collaborative Computing: Networking, Applications, Worksharing

Self Cite

View full text Add to dashboard Cite

show abstract

“…LIU Qin [5] utilized binary particle swarm optimization algorithm for tracking multi hiding targets in radar networks. X. Xing [7] depended on three status transport module and put forward a moving target tracking method based on drove, experiment results shown that the solution play a good performance in detection, awakening mechanism and logical networks architecture. The existing solutions have merit and demerit considering detection tracking accuracy and energy consumption roundly.…”

Section: Introductionmentioning

confidence: 99%

A Human Abnormal BehaviorRecognition and Collaborative Tracking Algorithm

Xiong¹,

Liu²,

Xue³

et al. 2015

IJSIP

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“…Another protocol called Herd-Based Target Tracking Protocol (HTTP) is proposed in [53]. It is based on a three state-transition model, namely: sensing, sleeping and tracking.…”

Section: Dynamic Clusteringmentioning

confidence: 99%

On Energy Efficiency in Collaborative Target Tracking in Wireless Sensor Network: A Review

Demigha

Hidouci

Ahmed

2013

IEEE Commun. Surv. Tutorials

151

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Abstract-Energy-efficiency in target tracking applications has been extensively studied in the literature of Wireless Sensor Networks (WSN). However, there is little work which has been done to survey and summarize this effort. In this paper, we address the lack of these studies by giving an up-to-date Stateof-the-Art of the most important energy-efficient target tracking schemes. We propose a novel classification of schemes that are based on the interaction between the communication subsystem and the sensing subsystem on a single sensor node. We are interested in collaborative target tracking instead of singlenode tracking. In fact, WSNs are often of a dense nature, and redundant data that can be received from multiple sensors help at improving tracking accuracy and reducing energy consumption by using limited sensing and communication ranges. We show that energy-efficiency in a collaborative WSN-based target tracking scheme can be achieved via two classes of methods: sensing-related methods and communication-related methods. We illustrate both of them with several examples. We show also that these two classes can be related to each other via a prediction algorithm to optimize communication and sensing operations. By self-organizing the WSN in trees and/or clusters, and selecting for activation the most appropriate nodes that handle the tracking task, the tracking algorithm can reduce the energy consumption at the communication and the sensing layers. Thereby, network parameters (sampling rate, wakeup period, cluster size, tree depth, etc.) are adapted to the dynamic of the target (position, velocity, direction, etc.). In addition to this general classification, we discuss also a special classification of some protocols that put specific assumptions on the target nature and/or use a "non-standard" hardware to do sensing. At the end, we conduct a theoretic comparison between all these schemes in terms of objectives and mechanisms. Finally, we give some recommendations that help at designing a WSN-based energy efficient target tracking scheme.

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Herd-Based Target Tracking Protocol in Wireless Sensor Networks

Cited by 7 publications

References 13 publications

Square region-based coverage and connectivity probability model in wireless sensor networks

Square region-based coverage and connectivity probability model in wireless sensor networks

A Human Abnormal BehaviorRecognition and Collaborative Tracking Algorithm

On Energy Efficiency in Collaborative Target Tracking in Wireless Sensor Network: A Review

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