论文标题
通过Voronoi图的杂交和遗传算法的杂交无线连接对象的室内部署分布式方法
Distributed approach for the indoor deployment of wireless connected objects by the hybridization of the Voronoi diagram and the Genetic Algorithm
论文作者
论文摘要
物联网数据收集网络最近已成为重要的研究领域之一，因为它们的基本作用和在许多领域中的广泛应用。对象网络的建立基本上基于连接的对象的部署来处理收集的数据并将其传输到各个位置。随后，必须充分部署大量节点以实现完整的覆盖范围。该手稿引入了一种分布式方法，该方法结合了Voronoi图和遗传算法（VD-GA），以最大程度地提高感兴趣区域的覆盖范围。 Voronoi图用于将区域划分为单元格，并生成呈现已部署的IoT对象位置的初始解决方案。然后，在几个节点中并联执行遗传算法以改善这些位置。在实验环境上评估了开发的VD-GA方法，该方法是通过使用配备ESP32处理器的M5STICKC节点进行原型进行的。实验表明，与原始算法相比，根据建议的分布式遗传性voronoi算法，在计算速度方面，分布式方法提供了比原始算法所给出的更好的覆盖率，RSSI，寿命和相邻对象数量。 。
IoT data collection networks have recently become one of the important research areas due to their fundamental role and wide application in many domains. The establishment of networks of objects is based essentially on the deployment of connected objects to process the collected data and transmit them to the various locations. Subsequently, a large number of nodes must be adequately deployed to achieve complete coverage. This manuscript introduces a distributed approach, which combines the Voronoi Diagram and the Genetic algorithm(VD-GA), to maximize the coverage of a region of interest. The Voronoi diagram is used to divide region into cells and generate initial solutions that present the positions of the deployed IoT objects. Then, a genetic algorithm is executed in parallel in several nodes to improve these positions. The developed VD-GA approach was evaluated on an experimental environment by prototyping on a real testbed utilizing M5StickC nodes equipped with ESP32 processor. The experiments show that the distributed approach provided better degree of coverage, RSSI, lifetime and number of neighboring objects than those given by the original algorithms in terms of the suggested distributed Genetic-Voronoi algorithm outperforms the centralized one in terms of speed of computing. .
