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Accuracy-Stressed and Energy-Concerned Adaptive Sensor Scheduling for Target Tracking in Underwater Wireless Sensor Networks
ZHANG Senlin * #,CHEN Huayan,LIU Meiqin
College of Electrical Engineering, Zhejiang University, Hangzhou 310027
*Correspondence author
#Submitted by
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Funding: National Natural Science Foundation of China （No.No. 61374021, No. 61222310 ), Specialized Research Fund for the Doctoral Program of Higher Education of China）
Opened online:24 January 2014
Accepted by: none
Citation: ZHANG Senlin,CHEN Huayan,LIU Meiqin.Accuracy-Stressed and Energy-Concerned Adaptive Sensor Scheduling for Target Tracking in Underwater Wireless Sensor Networks[OL]. [24 January 2014] http://en.paper.edu.cn/en_releasepaper/content/4583108
 
 
Target tracking is one of the broad applications of underwater wireless sensor networks (UWSNs). Sensors of UWSNs are battery-powered and it is impracticable to replace the batteries when exhausted. That means the batteries life affecting the lifetime of whole networks. So, it is worth reducing the energy consumption on the premise of satisfactory tracking accuracy. This paper proposes an adaptive sensor scheduling scheme that implements for accurately and energy-efficiently tracking a maneuvering target detected by underwater UWSNs. This scheme employs multi-sensor to achieve the tracking task. And a priori criterion is presented to select the best sensor group and best fusion sensor from candidate sensors. The criterion is generated from the algorithm combining interacting multiple model with extended Kalman filters (IMM-EKF). For reducing the energy consumption, the sampling interval is variable at each time step, according to a pre-given tracking accuracy thresthold. Simulation demonstrates that selecting best sensor group can improve the tracking accuracy significantly; selecting best fusion sensor and appropriate sampling interval can reduce the energy consumption significantly.
Keywords:Underwater wireless sensor networks; Target tracking; Sensor scheduling; Interacting multiple model extended Kalman filter.
 
 
 

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