Adaptive Sensor Scheduling Algorithm for Target Tracking in Wireless Sensor Networks

Bo HU; Qiyao WANG; Hui FENG; Lingbing LUO

doi:10.11999/JEIT171154

Volume 40 Issue 9

Aug. 2018

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Bo HU, Qiyao WANG, Hui FENG, Lingbing LUO. Adaptive Sensor Scheduling Algorithm for Target Tracking in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2033-2041. doi: 10.11999/JEIT171154

Citation:

Bo HU, Qiyao WANG, Hui FENG, Lingbing LUO. Adaptive Sensor Scheduling Algorithm for Target Tracking in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2033-2041. doi: 10.11999/JEIT171154

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Adaptive Sensor Scheduling Algorithm for Target Tracking in Wireless Sensor Networks

doi: 10.11999/JEIT171154

1.
School of Information Science and Technology, Fudan University, Shanghai 200433
2.
Research Center of Smart Networks and Systems, Fudan University, Shanghai 200433

Funds: The National Natural Science Foundation of China (61501124), The Public Security Bureau Science and Technology Development Foundation of Shanghai (2017012)

Received Date: 2017-12-06
Rev Recd Date: 2018-05-04

Available Online: 2018-07-12

Publish Date: 2018-09-01

Abstract

Abstract

In the process of target tracking, the sensor scheduling algorithm can achieve the tradeoff between the tracking error and the energy consumption so as to extend the service life of the sensor network. The issue can be modeled as a Partially Observable Markov Decision Process (POMDP), which takes both short- and long- term losses of sensor scheduling into account and makes a better decision. A C-QMDP approximation algorithm suitable for continuous state space is proposed. The Markov Chain Monte Carlo (MCMC) method is used to derive the transfer function of belief state and calculate the instantaneous cost. The state discretization method is used to solve the approximation of future cost based on Markov Decision Process (MDP) iteration. Simulation results show that compared to the existing POMDP approximation algorithms, the proposed algorithm can reduce the cumulative losses and computation load in the tracking process by offline computation.
- Wireless Sensor Networks (WSN),
- Target tracking,
- Sensor scheduling,
- Partially Observable Markov Decision Process (POMDP)

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References(27)

References

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