Abstract
Internet Of Things (IOT) is an inevitable result of the evolution of communication and manufacturing of small low power and effective micro electro mechanical systems (MEMS). Wireless Sensor Network (WSN) is Self organized collected sensors that communicate to each other randomly through waves. Node localization is a major challenge for most of WSN applications due to the difficult environments through which nodes are distributed such as underwater, fires, volcanoes, animal habitat or Battlefields. Moreover, using Global Positioning System (GPS) is costly high and not feasible in indoors. Range based algorithms such as Angle Of Arrival, Time Of Arrival and Received Signal Strength Indicator are alternative for GPS with acceptable accuracy, however they require additional hardware. Range free algorithms such as centroid, approximate point-in-triangulation and distance vector-hop algorithms are economic but less accurate. Lack of accuracy is still an issue in traditional localization algorithms which guide researchers recently to propose optimization techniques with the ability to improve the accuracy of localization based on soft computing algorithms like genetic algorithms, fuzzy logic, neural network and deep learning. The objective of this paper is to give an over view of WSN applications and challenges highlighting the localization problem. The paper also, proposes taxonomy that classifies variant localization algorithms. Moreover, presents a study of different traditional algorithms as well as their improvements based on soft computing techniques. Moreover, the paper summarizes the challenges of localization and represents research points for future work. (C) 2018 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V.