The integration of the Internet of Things (IoT) with Wireless Sensor Networks (WSNs) typically involves multihop relaying combined with sophisticated signal processing to serve as an information provider for several applications such as smart grids, industrial, and search-and-rescue operations. These applications entail deploying many sensors in environments that are often random which motivated the study of beamforming using random geometric topologies. This paper introduces a new algorithm for the synthesis of several geometries of Collaborative Beamforming (CB) of virtual sensor antenna arrays with maximum mainlobe and minimum sidelobe levels (SLL) as well as null control using Canonical Swarm Optimization (CPSO) algorithm. The optimal beampattern is achieved by optimizing the current excitation weights for uniform and non-uniform interelement spacings based on the network connectivity of the virtual antenna arrays using a node selection scheme. As compared to conventional beamforming, convex optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), the proposed CPSO achieves significant reduction in SLL, control of nulls, and increased gain in mainlobe directed towards the desired base station when the node selection technique is implemented with CB.

中文翻译：

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结合可连接和协作传感器使用鲁棒群算法在物联网应用中进行波束成形优化。

物联网（IoT）与无线传感器网络（WSN）的集成通常涉及多跳中继与复杂的信号处理相结合，以充当智能电网，工业和搜索救援等多种应用的信息提供者。这些应用需要在通常是随机的环境中部署许多传感器，这促使人们研究使用随机几何拓扑的波束成形。本文介绍了一种新算法，用于合成具有最大主瓣和最小旁瓣电平（SLL）的虚拟传感器天线阵列的协作波束成形（CB）的几种几何形状，以及使用规范化群优化（CPSO）算法的空控制。通过使用节点选择方案基于虚拟天线阵列的网络连接性，针对均匀和不均匀的元素间距优化当前的激励权重，可以实现最佳的波束方向图。与传统的波束成形，凸优化，遗传算法（GA）和粒子群优化（PSO）相比，建议的CPSO显着降低了SLL，控制了零点并增加了当节点指向目标基站时主瓣的增益选择技术是用CB实现的。