Energy-Efficient Randomized Switching for Maximizing Lifetime in Tree-Based Wireless Sensor Networks


In most wireless sensor network (WSN) applications, data are sometimes gathered by sensor nodes and reported to an information assortment point called sink. To support such a knowledge assortment pattern, a tree structure rooted at the sink is outlined. Depending on numerous factors, as well as the WSN topology and the supply of resources, the energy consumption of nodes in numerous ways of the information assortment tree may vary largely, so affecting the general network lifetime. This paper addresses the problem of lifetime maximization of WSNs based on information collection trees. Specifically, we have a tendency to propose a unique and efficient algorithm, referred to as Randomized Switching for Maximizing Lifetime (RaSMaLai), that aims at extending the lifetime of WSNs through load balancing. Given an initial knowledge assortment tree, RaSMaLai randomly switches some sensor nodes from their original paths to different paths with lower load. We have a tendency to prove that, below applicable settings of the operating parameters, RaSMaLai converges with an occasional time complexity. We tend to any design a distributed version of our algorithm. Through an extensive performance analysis study that features simulation of enormous-scale situations and real experiments on a WSN testbed, we tend to show that the proposed RaSMaLai algorithm and its distributed version achieve a longer network lifetime than the state-of-the-art solutions.

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