Improved Tree Dimensional DV-Hop Protocol for Large Scale Range-Free Wireless Sensors Network

Abstract

Location plays a crucial role in many applications of Wireless Sensor Networks (WSNs), and accurate sensor localization is an important aspect of the acquired data. While connectivity algorithms are commonly used for localizing multi-hop WSNs because their simplicity and acceptable accuracy, their effectiveness can be limited in two-dimensional (2D) or three-dimensional (3D) environments. An analytic model that incorporates hop size quantization and the Recursive Least Squares (RLS) method can be advantageous for Range-Free 3D wireless sensor networks (WSNs) in localization. This approach reduces computational complexity, memory requirements, and localization errors. The third dimension significantly impacts localization accuracy, necessitating the development of effective self-localization algorithms for 3D WSNs. This article introduces a novel probabilistic quantization technique for hop sizes in 3D-WSNs, specifically designed to address the uniform distribution of sensor nodes. The RLS method is employed as an adaptive filtering algorithm to recursively estimate the positions of sensor nodes in the system by minimizing the sum of squared errors between actual measured values and predicted values. Through extensive simulations conducted in isotropic settings under various conditions, the proposed algorithms are evaluated based on their average localization error performance. The simulation data clearly demonstrate that the suggested localization algorithm outperforms previous 3D-DV-Hop heuristics in terms of accuracy. The proposed localization method for 3D-WSNs successfully decreases the average localization error of nodes and achieves superior location accuracy when utilizing predicted hop quantization for hop-size estimation and the RLS algorithm for position estimation compared to competing approaches.