Fundamental Limitations and State-of-the-art Solutions for Target Node Localization in WSNs

Abstract

<p> </p><p>With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks</p> <p>(WSNs) and their applications are proliferating in several areas of our society such as healthcare, industry, farming, and housing. Accordingly, in recent years attention on localization has increased significantly since it is one of the main facets in any WSN. In a nutshell, localization is the process in which the position of any sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracy, complexity, and hence different applicability. The localization accuracy is limited by fundamental limitations, theoretical and practical, that restrict the localization accuracy regardless of the technique employed in the localization process. In this paper, we pay special attention to such fundamental limitations from the theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. Additionally, discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives are presented.</p> <p><br></p>

Adaptation of Population Size in Differential Evolution and Its Effects on Localization of Target Nodes

“…being the arithmetic mean of the j-th gene over all the individuals at the g-th generation. Taking into account the relative diversity R (g) , the PD technique updates the PS as 1) , L (g) , otherwise.…”

“…L PD denotes the PS at the g-th generation, and it varies according to (22). K 1PD and K 2PD are binary parameters: K 1PD = 1 and 1) . K 1PD = 0 and 1) .…”

“…K 1PD and K 2PD are binary parameters: K 1PD = 1 and 1) . K 1PD = 0 and 1) . K 1PD = 0 and K 2PD = 0 in other cases.…”

“…T arget nodes (TNs) localization in wireless sensor networks (WSNs) is of pivotal importance in several realworld applications such as healthcare monitoring, surveillance, and robotics. To estimate the position of TNs, several approaches based on linear least squares, semidefinite programming, and unscented transformation are commonly employed [1]. Although such conventional approaches improve the localization accuracy over their predecessors, they usually approximate the maximum likelihood (ML) cost function, resulting in suboptimal solutions.…”