DV-Hop localization Based on parameter correction and Improved zebra optimization algorithm

The Internet of Things (IoT) makes extensive use of wireless sensor networks to monitor the physical environment. Since location data is the foundation of network applications in wireless sensor networks, precise node localization is essential to the operation of the entire system. An improved DV-Hop localization algorithm (IZDV) based on the fusion of Chaos, Flight, and Wandering strategies for zebra optimization is proposed in order to improve the localization accuracy of the widely used Distance Vector Hopping (DV-Hop) localization algorithm. The DV-Hop localization algorithm relies solely on network connectivity for localization, which results in a large error in the localization result. First, various communication radii are used to substitute the original integer hop information with fractional hop information. Second, the average hop distance information of the unknown nodes is corrected using the mean square weighted average error plus additional beacon node information. Lastly, the original least squares approach for node position estimation is replaced with the improved zebra optimization algorithm, which optimizes the classical zebra algorithm by combining chaotic, dynamic weighting and Levy. This accelerates the algorithm's convergence and strengthens its global and local search capabilities to keep the algorithm from prematurely entering the local optimum while still maintaining the algorithm's time complexity. The simulation results indicate that the average localization error of this paper's algorithm is reduced by about 60.51%, 22.65% and 19.32% in comparison with the classical DV-Hop algorithm, GDV-Hop algorithm and ISSADV-Hop algorithm in default network conditions, which verifies the better localization accuracy and stability of the proposed algorithm.