An improved DV-Hop algorithm for wireless sensor networks based on neural dynamics

Among the localization algorithms of wireless sensor networks (WSNs), the distance vector-hop (DV-Hop) algorithm has been widely concerned thanks to its simplicity, low hardware requirements, and easy implementation. However, the localization accuracy of the DV-Hop algorithm declines greatly when the sensor nodes are unevenly distributed. To improve the accuracy of the DV-Hop algorithm, we propose an improved DV-Hop algorithm based on neural dynamics (ND-DV-Hop). First, the fluctuant range of distance errors between the unknown nodes and the anchor nodes is computed via error analysis. Then, the traditional localization model is transformed into an algebraic equation in which the distances and coordinates change with time. Besides, a neural dynamics (ND) algorithm is used to solve the equation and obtain the solution with the residual errors eliminated. Theoretical analyses are provided to verify the convergence and anti-noise performance of the ND-DV-Hop algorithm. Finally, numerical simulations are carried out to confirm the superiority, efficiency, robustness, and accuracy of the proposed algorithm for dealing with WSNs localization problems.