Recently, localization accuracy of unknown nodes has become a critical and challenging issue for many Wireless Sensor Networks (WSNs) and Internet of Things (IoT) applications. Without associating the detected event with its precise geographic location will be surely considered meaningless for these applications. Among all localization algorithms, we observe that the DV-Hop localization algorithm is highly recommended to use in many fields of application due to its simplicity, feasibility, low cost, and no extra hardware requirements, but the localization error caused by the DV-Hop algorithm is relatively large. In this current work, based on both the DV-Hop algorithm and the Particle Swarm Optimization algorithm, we proposed four new localization algorithms to overcome the shortcomings of low accuracy that the basic DV-Hop based algorithms produce. The simulation results showed that the proposed localization algorithms can achieve a better localization performance in terms of accuracy in comparison with other existing algorithms such as basic DV-Hop, MDV-Hop and DV-HopPSO under different random network topologies. We also observed that a significant localization accuracy is achieved by the proposed algorithm HWDV-HopPSO.

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Wireless sensor networks have attracted great interest in the last decade, and have provided solutions in a growing number of applications. The location of sensors is a major issue in these networks. Many localization algorithms have been proposed and can be categorized into two families: range-based and range-free algorithms. In this paper, the localization algorithm DV-Hop is improved in order to minimize the estimation error and thus obtain better localization accuracy of sensor nodes. Simulations have been conducted and results are reported to show the efficiency of the enhanced version of DV-Hop.

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