An energy efficient target tracking scheme for distributed wireless sensor networks

Authors:
Hadi Jamali Rad;Bahman Abolhassani;Mohammad Abdizadeh
Affiliations:
School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran;School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran;School of Electrical Engineering, Sharif University of Technology, Tehran, Iran
Venue:
ISWCS'09 Proceedings of the 6th international conference on Symposium on Wireless Communication Systems
Year:
2009

Citing 2
Cited 1

Adaptive Tracking in Distributed Wireless Sensor Networks

ECBS '06 Proceedings of the 13th Annual IEEE International Symposium and Workshop on Engineering of Computer Based Systems
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CNSR '09 Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference

Energy-efficient collaborative tracking in wireless sensor networks

International Journal of Sensor Networks

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Abstract

We study the problem of power optimization for object tracking using distributed Wireless Sensor Networks (WSNs). The accuracy of the object tracking is dependent on the tracking time interval. Smaller tracking time interval increases the accuracy of tracking a moving object. However, this increases the power consumption significantly. This paper proposes a modified adaptive sleep time management scheme called Modified Predict and Mesh (MPaM) to adapt tracking time interval such that it minimizes power consumption while keeping an acceptable tracking accuracy. Also a quantitative analysis to compare the performances of the conventional PaM and proposed Modified PaM (MPaM) schemes is developed. Simulation results show that using the proposed modified scheme, the tracking network has a very good performance with the added advantage of reducing the power consumption significantly (up to 22% and up to 9%) when compared with two existing adaptive methods (PaM and AEC, respectively). Moreover, the calculated quantitative results and the simulation results coincide very well.