CHEF: Cluster Head Election mechanism using Fuzzy logic in Wireless Sensor Networks

Jong-Myoung Kim, Seon-Ho Park, Young-Ju Han, Tai M. Chung
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引用次数: 555

Abstract

In designing the wireless sensor networks, the energy is the most important consideration because the lifetime of the sensor node is limited by the battery of it. To overcome this demerit many research have been done. The clustering is the one of the representative approaches. In the clustering, the cluster heads gather data from nodes, aggregate it and send the information to the base station. In this way, the sensor nodes can reduce communication overheads that may be generated if each sensor node reports sensed data to the base station independently. LEACH is one of the most famous clustering mechanisms. It elects a cluster head based on probability model. This approach may reduce the network lifetime because LEACH does not consider the distribution of sensor nodes and the energy remains of each node. However, using the location and the energy information in the clustering can generate big overheads. In this paper we introduce CHEF - cluster head election mechanism using fuzzy logic. By using fuzzy logic, collecting and calculating overheads can be reduced and finally the lifetime of the sensor networks can be prolonged. To prove efficiency of CHEF, we simulated CHEF compared with LEACH using the matlab. Our simulation results show that CHEF is about 22.7% more efficient than LEACH.
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无线传感器网络中使用模糊逻辑的簇头选举机制
在设计无线传感器网络时,由于传感器节点的寿命受到其电池的限制,因此能量是最重要的考虑因素。为了克服这一缺点,已经进行了许多研究。聚类是一种具有代表性的方法。在集群中,簇头从节点收集数据,汇总并将信息发送到基站。这样,传感器节点可以减少如果每个传感器节点独立地向基站报告感测数据可能产生的通信开销。LEACH是最著名的聚类机制之一。它基于概率模型选择簇头。由于LEACH不考虑传感器节点的分布和每个节点的剩余能量,这种方法可以减少网络的生存期。然而,在聚类中使用位置和能量信息会产生很大的开销。本文引入了基于模糊逻辑的CHEF -簇头选举机制。利用模糊逻辑可以减少传感器网络的采集和计算开销,从而延长传感器网络的寿命。为了证明CHEF的有效性,我们利用matlab对CHEF和LEACH进行了仿真比较。仿真结果表明,CHEF的效率比LEACH的效率高22.7%。
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