| 面向智能交通的无线传感网络分簇算法 |
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| 引用本文： | 苏航.面向智能交通的无线传感网络分簇算法[J].交通信息与安全,2017,35(3):74. |
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| 作者姓名： | 苏航 |
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| 作者单位： | 中国交通通信信息中心交通安全应急信息技术国家工程实验室 北京 100011 |
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| 摘 要： | 无线传感网络是智能交通系统的关键组成部分.合理的分簇技术有利用于提高无线传感网络能量利用率.研究了基于孤点感知能效的无线传感网络分簇算法(INEEC),该算法将时间划分等间隔的轮,每一轮进行簇头选择,平衡簇头的能耗.在簇头选择阶段,每个节点先选择一个随机数,依据各节点的能量以及该区域的局部能量信息估计门限值.若随机数小于门限值,就成为簇头.每个簇头节点广播请求加入消息,其他节点接收消息后,加入最近的簇.若没有收到任何请求加入消息,节点就为孤点.仿真结果表明,相比于HEED和LEACH算法,该算法孤点数最少,传输时延最低,簇头分布更加均匀使总体能耗降低,网络寿命比HEED算法提高近23.5%,较好解决了同类算法在应用中能量效率较低、能耗较高、网络生命周期较短的关键问题.
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| 关 键 词： | 智能交通 无线传感网络 分簇 孤点 |
A Clustering Algorithm of Wireless Sensor Networks for Intelligent Transportation |
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| Abstract: | Wireless sensor networks (WSNs) are important components of intelligent transportation systems.The energy efficiency of WSNs can benefit from a suitable clustering technique.Based on Energy Efficient Clustering (INEEC) scheme, a clustering algorithm of WSNs is studied in this paper.Operating time is divided into several rounds.Cluster head (CH) is determined in each round to balance energy consumption of CH.During a selection phase of CH, each node chooses a random number and computes the threshold value of the random number by residual energy and the average of the regional energy of all sensors in each cluster.If the random number is less than the threshold value, the node becomes a CH for the current round.Each CH broadcasts a joining request message to the rest of nodes.If a non-CH node receives many joining request messages, the node decides to join the closest cluster accordingly.Those non-CH nodes that do not receive a joining request message are considered as isolated nodes.In order to improve the energy efficiency of an isolated node, INEEC scheme determines its transmission model.Compared with the HEED and LEACH algorithms, the simulation results show that INEEC scheme has better performance in reducing energy consumption which means this scheme has the least isolated nodes, the minimum transmission delay, and a more uniform CH distribution.In detail, the network lifetime increases nearly 23.5% compared with the HEED algorithm.Key problems in the application of the INEEC algorithm, such as high energy consumption, low energy efficiency and network lifetime, are well solved in this study. |
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