Location information is a crucial enabler for many applications of Wireless Sensor Networks (WSN) such as oil and gas explorations, intrusion detection, and road traffic tracking. For instance, seismic explorations for oil and gas require a large number of sensor nodes deployed outdoors over large areas, which need to be accurately localized for precise depth imaging. These applications require the deployment of sensor nodes in irregular fields where the communication of sensor nodes is affected by obstructions. This disrupted communication alters the distance estimations between nodes resulting in erroneous location estimations. To improve the localization accuracy, this paper presents a range free localization method called Harris Hawks Optimization based localization with Area Minimization (HHO-AM). This algorithm uses different coverage ranges of nodes in a heterogeneous network to classify neighbor nodes into two sets: incoming neighbor, and outgoing neighbor. Area minimization is done to reduce the search area. The localization problem is solved using Harris Hawks Optimization (HHO) technique within the minimized search area using neighbor sets. In this research, the performance of the reported algorithm is tested by considering sensor nodes in 2D square, 2D C-shaped, 3D cube, 3D C-shaped, and 3D mountain terrain fields. The simulation results show that the reported method, HHO-AM shows an improvement of 39% to 62% in terms of localization error in comparison with the recent state-of-the-art methods, i.e., Enhanced Weighted Centroid DV-Hop (EWCL), DV-maxHop and the traditional method DV-Hop. Also, the HHO-AM algorithm shows a more stable performance in both isotropic and anisotropic fields.

位置信息对于无线传感器网络（WSN）的许多应用（例如石油和天然气勘探，入侵检测和道路交通跟踪）至关重要。例如，对石油和天然气的地震勘探需要将大量传感器节点部署在大面积的室外区域，而这些节点需要精确定位以进行精确的深度成像。这些应用要求将传感器节点部署在不规则的区域中，在这些区域中，传感器节点的通信受障碍的影响。这种中断的通信会更改节点之间的距离估计，从而导致错误的位置估计。为了提高定位精度，本文提出了一种基于区域最小化的基于哈里斯霍克斯优化的无范围定位方法（HHO-AM）。该算法使用异构网络中节点的不同覆盖范围将邻居节点分为两组：传入邻居和传出邻居。进行面积最小化以减小搜索面积。在使用邻居集的最小化搜索区域内，使用Harris Hawks优化（HHO）技术解决了定位问题。在这项研究中，通过考虑2D正方形，2D C形，3D立方体，3D C形和3D山地地形中的传感器节点来测试所报告算法的性能。仿真结果表明，与最近的最新方法，即增强加权质心DV-Hop（EWCL）相比，所报告的方法HHO-AM的定位误差提高了39％至62％ ），DV-maxHop和传统方法DV-Hop。也，