A modified Manhattan distance with application for localization algorithms in ad-hoc WSNs

Building an efficient node localization system in wireless sensor networks is facing several challenges. For example, calculating the square root consumes computational resources and utilizing flooding techniques to broadcast nodes location wastes bandwidth and energy. Reducing computational complexity and communication overhead is essential in order to reduce power consumption, extend the life time of the battery operated nodes, and improve the performance of the limited computational resources of these sensor nodes. To that end, we propose a novel modified Manhattan distance norm and employ it in a previous localization system so-called TALS (Trigonometric Ad-hoc Localization System), such that we optimize TALS. Furthermore, an analysis and an extensive simulation for the optimized TALS (OTALS) is presented showing its cost, accuracy, and efficiency, thus deducing the impact of its parameters on performance. Our novel similarity measure formula can be used in many other domains such as clustering and classification. However, we present its efficiency only for a particular problem in this work. Thus, the major contribution of this work can be summarized as follows: (1) Proposing and employing a novel modified Manhattan distance norm in the TALS localization process. (2) Analyzing and simulating of OTALS showing its computational cost and accuracy and comparing them with other related work. (3) Studying the impacts of different parameters like anchor density, node density, noisy measurements, transmission range, and non-convex network areas. (4) Extending our previous joint work, TALS, to consider base anchors to be located in positions other than the origin and analyzing this work to illustrate the possibility of selecting a wrong quadrant at the first iteration and how this problem is overcome. Through mathematical analysis and intensive simulation, OTALS proved to be iterative, distributed, and computationally simple. It presented superior performance compared to other localization techniques. (C) 2015 Elsevier B.V. All rights reserved.