期刊
IEEE SENSORS JOURNAL
卷 21, 期 18, 页码 21069-21079出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2021.3093473
关键词
Target tracking; Sensors; Wireless sensor networks; Collaboration; Vehicle dynamics; Task analysis; Aerodynamics; Energy efficiency; target tracking; wireless sensor network
资金
- National Natural Science Foundation of China [61771393]
- Fundamental Research Funds for the Central Universities [JB211307]
The paper proposes a dynamic chain-based collaboration (DCBC) approach for efficient target tracking and data gathering in WSNs, which forms a dynamic tracking chain around the target and adapts to target location changes without reconstructing the structure. The nodes in the tracking chain send their own data in turn, improving energy utilization and data transmission efficiency. Experimental results show that DCBC reduces and balances network energy consumption compared to existing approaches, prolonging network lifetime.
In target tracking WSNs, the tree-based and cluster-based network should be rebuilt when the location of target changes, which involves energy and time overheads. Although the chain structure solves this problem, it involves lots of unnecessary nodes and unacceptable transmitting delay. Therefore, we propose a dynamic chain-based collaboration (DCBC) approach for efficient target tracking and data gathering in this paper. DCBC completes the target tracking task by forming a dynamic tracking chain around the target. With the target moving, the dynamic tracking chain is adjusted by pruning some nodes as long as they are out of the range of the target and adding the nodes which entered the range of the target, so that the chain can adapt to the changing location of the target. The structure of the dynamic tracking chain does not need to be re-established when the target position changes. Furthermore, the sensing data are fused at every step when they are transmitted along the dynamic tracking chain. Using this way, the sensing data can be collected by tracking nodes and fused locally. In addition, we propose a dynamic and energy-efficient sleep scheduling for data transmission in the target tracking chain. The nodes in the tracking chain send their own data in turn. Thus it improves the energy utilization and data transmission efficiency of the network. The experimental results verify that DCBC can reduce and balance the average energy consumption of the network compared with the state-of-the-art approaches, and prolong the lifetime of networks.
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