An Accurate Hop-Based Localization Algorithm Under Random Deployment of Nodes for WSN

Abstract

In the last decade, the research community has devoted significant attention to Wireless Sensor Network (WSN) due to its huge participation in 5G, Internet Of Things (IoT) and Smart City. Besides that, WSN has shown to respond to some critical issues encountered in nature and even in the industry field. For example, the necessity of pertinent measurements presented in high-risk places where humans cannot seriously be there to take a manual measure or even to diagnose the status of the scattered sensors. On the other hand, localization is one of the most important parts of maintaining the functioning of WSN so range-based schemes don't present a convenient solution to the aforementioned issues because those techniques consume a lot of energy and don't guarantee a long lifetime of sensors. Hence, the conception of an efficient method of localization has become a hot research topic. Recently, the development of low energy-consumption positioning methods has effectively addressed our needs. However, a set of low energy-consumption positioning methods have been developed. In our contribution, we shall study Amorphous localization technique deeply and explain the reasons behind its robustness in non-uniform fields. Indeed, the method of resolution adopted by Amorphous in its last step causes imprecision in localization. As a consequence, our work was to intelligently replace this step, aiming to reach satisfactory precision. For that purpose, we have exploited Fmincon solver that existed in MATLAB 2015 designed for optimization. Specifically, we have integrated this function to minimize the cumulated errors done in multilateration instead of adopting the least squares method, which denotes one cause of the imprecision of Amorphous. The results obtained from the simulation prove the efficiency of the improved Amorphous iAmorphous in terms of accuracy. Furthermore, the enhanced algorithm outperforms its opponents by varying the percentage of anchors. Also, it keeps outperforming in the network with a high and low density of nodes.