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

Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks
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用于无线传感器网络中目标跟踪的基于拍卖的自适应传感器激活算法

DOI:
10.1016/j.future.2013.12.014
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发表时间:
2014-10
期刊:
Future Generation Computer Systems
影响因子:
--
通讯作者:
Guojun Wang
Guojun Wang
中科院分区:
其他
文献类型:
--
作者:
Jin Zheng;Md Zakirul Alam Bhuiyan;Shaohua Liang;Xiaofei Xing;Guojun Wang

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由于无线传感器网络(WSNs)资源的限制,设计一种能量有效且跟踪质量高的目标跟踪算法成为一个具有挑战性的问题。无线传感器网络通常提供集中的信息,例如,目标的位置和方向,选择目标周围的传感器等。然而,由于从中央服务器获得跟踪任务的响应的高通信成本和低跟踪质量,一些现成的策略可能不被直接使用。在本文中,我们提出了一种用于无线传感器网络目标跟踪的完全分布式算法,即基于拍卖的自适应传感器激活算法(AASA)。集群形成的目标运动之前,在一个有趣的方式,集群形成的过程是由于预测区域(PR)和集群成员通过拍卖机制从PR中选择。在PR计算的基础上,只有PR中的节点被激活,其余节点保持睡眠状态。为了在能量效率和跟踪质量之间取得平衡,根据当前跟踪质量自适应地调整PR的半径和节点数量。而不是固定的时间间隔(通常在现有的工作中使用),跟踪间隔也是动态适应。大量的仿真结果表明,与现有的工作相比,AASA实现高性能的跟踪质量,能源效率和网络寿命。
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.
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