Hasan Mohammed Zaki, Al-Rizzo Hussain
College of Computer Science and Mathematics, University of Mosul, Mosul 41002, Iraq.
Systems Engineering Department, Donaghey College of Engineering & Information Technology, University of Arkansas, Little Rock, AR 72701, USA.
Sensors (Basel). 2020 Apr 6;20(7):2048. doi: 10.3390/s20072048.
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.
物联网（IoT）与无线传感器网络（WSN）的集成通常涉及多跳中继，并结合复杂的信号处理，以为智能电网、工业和搜索救援行动等多种应用提供信息。这些应用需要在通常随机的环境中部署许多传感器，这激发了对使用随机几何拓扑进行波束成形的研究。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协作波束成形（CB）的多种几何结构，以及使用规范群优化（CPSO）算法进行零陷控制。通过基于虚拟天线阵列的网络连接性，使用节点选择方案优化均匀和非均匀单元间距的当前激励权重，实现了最优波束方向图。与传统波束成形、凸优化、遗传算法（GA）和粒子群优化（PSO）相比，当与CB一起实施节点选择技术时，所提出的CPSO在SLL方面显著降低，实现了零陷控制，并提高了指向所需基站的主瓣增益。
