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Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks

delete2014-10-01
delete55
PRE
AI
J
Jin Zheng
M
Md Zakirul Alam Bhuiyan
S
Shaohua Liang
X
Xiaofei Xing
G
Guojun Wang *
DOI:10.1016/j.future.2013.12.014delete
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摘要

摘要

En 中文
Due to the severe resource constraints in wireless sensor networks (WSNs), designing an efficient target tracking algorithm for WSNs in terms of energy efficiency and high tracking quality becomes a challenging issue. WSNs usually provide centralized information, e.g., the locations and directions of a target, choosing sensors around the target, etc. However, some ready strategies may not be used directly because of high communication costs to get the responses for tracking tasks from a central server and low quality of tracking. In this paper, we propose a fully distributed algorithm, an auction-based adaptive sensor activation algorithm (AASA), for target tracking in WSNs. Clusters are formed ahead of the target movements in an interesting way where the process of cluster formation is due to a predicted region (PR) and cluster members are chosen from the PR via an auction mechanism. On the basis of PR calculation, only the nodes in the PR are activated and the rest of the nodes remain in the sleeping state. To make a trade-off between energy efficiency and tracking quality, the radius of PR and the number of nodes are adaptively adjusted according to current tracking quality. Instead of fixed interval (usually used in existing work), tracking interval is also dynamically adapted. Extensive simulation results, compared to existing work, show that AASA achieves high performance in terms of quality of tracking, energy efficiency, and network lifetime. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Wireless sensor networks
Target tracking
Energy efficiency
Auction mechanism
Adaptive sensor activation
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期刊

F
Future Generation Computer Systems-The International Journal of eScience
IF:
6.1
论文数:
6.9K
被引数:
2.3W

机构

C
Central South University
学者数:
10.0W
论文数: 7.2W
被引数: 10.9W
G
Guangzhou University
学者数:
1.8W
论文数: 1.3W
被引数: 1.8W
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