Energy‐Efficient Adaptive Dynamic Sensor Scheduling for Target Monitoring in Wireless Sensor Networks
Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy‐efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless s...
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Published in | ETRI journal Vol. 33; no. 6; pp. 857 - 863 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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한국전자통신연구원
01.12.2011
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ISSN | 1225-6463 2233-7326 |
DOI | 10.4218/etrij.11.0111.0027 |
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Abstract | Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy‐efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption. |
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AbstractList | Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy‐efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption. Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption. Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption. KCI Citation Count: 17 |
Author | Zhang, Yun‐zhou Ji, Peng Wu, Cheng‐dong Zhang, Jian |
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Cites_doi | 10.1109/MSP.2005.1458289 10.1109/TSP.2003.814469 10.1109/SSP.2003.1289505 10.1109/TMC.2009.81 10.1109/89.554268 10.1088/0957-0233/19/2/025104 10.1109/TPDS.2007.70773 10.1109/TSP.2006.882082 10.1049/iet-spr.2008.0205 10.1016/S1389-1286(01)00302-4 10.1109/TMC.2004.22 10.1109/HICSS.2000.926982 10.1109/TSP.2004.838930 10.1109/TIM.2008.2005822 |
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SubjectTerms | energy balance node scheduling particle filter target monitoring Wireless sensor networks 전자/정보통신공학 |
Title | Energy‐Efficient Adaptive Dynamic Sensor Scheduling for Target Monitoring in Wireless Sensor Networks |
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