Relay nodes placement for optimal coverage, connectivity, and communication of wireless sensor networks: a PSO-based multi-objective optimization research idea

Kasyful Amron, W. M. Kusumawinahyu, S. Anam, W. Mahmudy
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引用次数: 1

Abstract

Designing a Wireless Sensor Networks (WSN) mostly was a great challenge. Shown in previous results, some design approaches lead to problems in its implementation. Deterministic methods face the NP-Hard complex problem. On the other side, heuristic methods sometimes produce a flawed result. With those situations, this research concern with exploring the possibility of a multi-objective optimization (MOO) method. As with the MOO method, some conflicted WSN aspects consider simultaneously. Started with the PSO algorithm, this developing method tries to find the best position of the WSN's relays. Closed neighbor sensor nodes are then will be connected. It is combined with the graph to constructs the best communication link. These steps will be done in a certain number of iterations to enhance fault-tolerance ability. This MOO approached method was implemented to different WSN topologies, with several sensors placed in a simulation area. Used as controls are Steiner Point and Triangular Grid algorithms. The most significant finding is this developing method gave some early potential results that could form future solutions in the multi-objective optimization approach for the WSN designing.
无线传感器网络覆盖、连接和通信的中继节点布局:基于pso的多目标优化研究思路
设计无线传感器网络(WSN)是一个很大的挑战。在之前的结果中显示,一些设计方法在其实现中会导致问题。确定性方法面临NP-Hard复杂问题。另一方面,启发式方法有时会产生有缺陷的结果。在这种情况下,本研究关注于探索多目标优化方法的可能性。与MOO方法一样,同时考虑WSN的一些冲突方面。该开发方法从粒子群算法入手,试图找到WSN中继的最佳位置。然后将封闭邻居传感器节点连接起来。它与图形相结合,构建最佳通信链路。这些步骤将在一定数量的迭代中完成,以增强容错能力。在不同的WSN拓扑中实现了这种MOO逼近方法,在模拟区域中放置了多个传感器。用作控件的是斯坦纳点和三角网格算法。最重要的发现是,该方法给出了一些早期的潜在结果,这些结果可以形成未来无线传感器网络多目标优化设计方法的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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