一种新的WSN节能聚类方法

P. Parwekar
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引用次数: 10

摘要

在无线传感器网络(WSNs)中,能量消耗是主要的挑战问题。如果数据直接从节点传输到基站,传输量会增加,通信距离越远,消耗的能量也会增加。在这种情况下,为了减少较长的通信距离和减少传输次数,采用了聚类技术。另一种减少能量消耗的方法是减少从节点到CH或从CH到BS的传输。减小传输距离是一个NP-Hard问题。因此,优化技术可以有效地解决这类问题。在本文中,是实现社会群体优化(SGO),以减少传输距离,并允许节点消耗更少的能量。将SGO算法的性能与遗传算法和粒子群算法进行了比较,结果表明SGO算法在适应度和能量方面都优于遗传算法。
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SGO A New Approach for Energy Efficient Clustering in WSN
In wireless sensor networks (WSNs), consumption of energy is the major challenging issue. If the data is transmitted directly from the node to the base station, it leads to more transmissions and energy consumed also increases if the communication distance is longer. In such cases, to reduce the longer communication distances and to reduce the number of transmissions, a clustering technique is employed. Another way to reduce the energy consumed is to reduce the transmission from node to CH or from CH to BS. Reducing the transmission distance is a NP-Hard problem. So, optimization techniques can be used effectively to solve such problems. In this article, is the implementation of a social group optimization (SGO) to reduce the transmission distance and to allow the nodes to consume less energy. The performance of SGO is compared with GA and PSO and the results show that SGO outperforms in terms of fitness and energy.
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