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An energy efficient cluster head selection approach for performance improvement in network-coding-based wireless sensor networks with multiple sinks
Computer Communications ( IF 6 ) Pub Date : 2020-10-28 , DOI: 10.1016/j.comcom.2020.10.014
Saeed Doostali , Seyed Morteza Babamir

The limited power source of sensors is the main constraint of Wireless Sensor Networks (WSNs); therefore, some energy management techniques are required. Network coding and topology control techniques have received extensive attention to decrease energy consumption and improve network performance. Although, combining these two techniques in WSNs can reduce energy consumption efficiently, their main drawback is the computation time, which grows exponentially by increasing nodes; this is not a convenient case for large-scale networks. In this paper, we utilize clustering method as a well-known topology control technique to overcome the mentioned drawback. The initial probability of cluster head selection is critical in distributed clustering algorithms. The results show that finding an appropriate probability provides a robust and fast alternative for the optimization approaches that are sensitive to the initial guess. Hence, we determine a near-optimal probability for cluster head selection to reach the maximum efficiency in the energy consumption. This probability is specified in terms of the number of nodes in the network and the distance of each node from its nearest neighbor provided that the neighbor lies within an angle of the source–destination axis. Our clustering method is based on learning automata and a sleep–awake mechanism to improve results. Accordingly, a routing algorithm along with an optimization problem is developed, which is called inter cluster subgraph selection. Simulation results show that the proposed approach is suitable for large-scale WSNs. Moreover, we demonstrate that the performance of the proposed approach, in terms of energy consumption and network lifetime, is more beneficial as compared to some existing algorithms.



中文翻译:

一种节能的簇头选择方法,用于在具有多个接收器的基于网络编码的无线传感器网络中提高性能

传感器电源有限是无线传感器网络(WSN)的主要限制;因此,需要一些能源管理技术。网络编码和拓扑控制技术已受到广泛关注,以减少能耗并提高网络性能。尽管在WSN中将这两种技术结合起来可以有效降低能耗,但它们的主要缺点是计算时间,而计算时间却随着节点数量的增加而呈指数增长。对于大规模网络来说,这不是一个方便的情况。在本文中,我们利用聚类方法作为一种众所周知的拓扑控制技术来克服上述缺点。在分布式聚类算法中,簇头选择的初始概率至关重要。结果表明,找到合适的概率为对初始猜测敏感的优化方法提供了强大而快速的替代方法。因此,我们确定簇头选择的最佳概率,以达到能耗的最大效率。根据网络中的节点数以及每个节点到其最近邻居的距离来指定此概率,前提是该邻居位于源-目的轴的角度之内。我们的聚类方法基于学习自动机和改善睡眠效果的睡眠觉醒机制。因此,开发了带有优化问题的路由算法,称为簇间子图选择。仿真结果表明,该方法适用于大规模无线传感器网络。此外,

更新日期:2020-11-02
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