An optimization based localization with area minimization for heterogeneous wireless sensor networks in anisotropic fields

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.