基于基因表达式编程的无线传感器网络多汇聚节点最优部署

Shucheng Dai, Changjie Tang, Shaojie Qiao, Kaikuo Xu, Hongjun Li, Jun Zhu
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引用次数: 28

摘要

在无线传感器网络(WSN)中,数据传输通常由传感器以多跳转发的方式向中央静态控制中心(sink)传输。在监控区域部署了许多廉价、低功率和能量有限的传感器,其中一些靠近汇聚节点的节点比其他节点更快地消耗能量,因为它们中继更多的数据包。虽然大多数传感器节点有足够的能量工作,但能量消耗不平衡导致连接漏洞和覆盖漏洞,最终导致整个网络故障。本文的主要贡献包括:(a)提出了一种基于多个汇聚节点的新方案,以延长网络生存期和缩短响应时间。(b)探索了网格传感器网络中给定数量的多个汇聚节点的部署策略,(c)提出了基于基因表达编程的多汇聚节点部署算法(GEP-MSN),在监测区域内最优部署多个汇聚节点,(d)引入数据传输成本模型(TCM)来衡量传输阶段的优化成本,特别是在目标跟踪应用中。(e)大量的仿真结果表明,与基于随机分布式汇聚节点的两种朴素方法相比,该方案可显著延长网络生存期,平均可延长约16.6%和36.3%。
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Optimal Multiple Sink Nodes Deployment in Wireless Sensor Networks Based on Gene Expression Programming
In wireless sensor networks (WSN) data transmission is usually performed by sensors in manner of multi-hop forwarding towards a central static control center (sink). A lot of cheap, low-powered and energy-limited sensors are deployed in the monitored area and some of these nodes closer to the sink node use up their energy more quickly than other nodes because they relay more packets. Although most of the sensor nodes have enough energy left to work, the energy consumption imbalance leads to connectivity holes and coverage holes, and finally the whole network failure. The main contributions of this paper include: (a) a new scheme based on multiple sink nodes is proposed to prolong the network lifetime and to reduce the response time. It is effective, especially in the target tracking applications, (b) the deployment strategy with given number of multiple sink nodes is explored in the grid sensor network, (c) Gene Expression Programming based Multiple Sink Nodes deployment algorithm (GEP-MSN) is proposed to optimally deploy multiple sink nodes over the monitored region, (d) a data transmission cost model (TCM) is introduced to measure the cost for optimizing during the transmission phase, (e) extensive simulations are conducted to show that the scheme can greatly extend the network lifetime by around 16.6% and 36.3% on average compared with two naive methods based on random distributed sink nodes.
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