Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks. (2nd September 2019)

• Record Type: Journal Article
• Authors: Annepu, Visalakshi; Anbazhagan, Rajesh
• Other Names: Souihi Sami guestEditor.; Bitam Salim guestEditor.; Mellouk Abdelhamid guestEditor.; Abreu Thiago guestEditor.; Hoceini Said guestEditor.; Fowler Scott guestEditor.; Medileh Saci guestEditor.; De Swades guestEditor.; Shami Abdallah guestEditor.
• Is Part Of: International journal of communication systems. Volume 33:Number 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-09-02
• Subjects: DEA -- ELM -- localizations -- MLP -- UAV -- wireless sensor networks; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.4173
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: Accurate node localization in wireless sensor networks (WSNs) is an essential for many networking protocols like clustering, routing, and network map building. The classical localization techniques such as multilateration and optimization‐based least square localization (OLSL) techniques estimate position of unknown node (UN) from the distance measured between all anchor nodes (ANs) and UNs. On the other hand, node localization using fixed terrestrial ANs suffers from poor localization accuracy because the ground to ground (GG) channel link is not reliable. By contrast, the mobile anchor deployed in unmanned aerial vehicle (UAV) provides high localization accuracy through reliable air to ground (AG) channel link. Still, the nonlinear distortion introduced in the wireless channel makes the distance measurement noisy. This noisy distance measurement also limits localization accuracy of classical localization techniques. Hence, the highly nonlinear artificial neural network (ANN) models such as multilayer perceptron (MLP) models can be applied effectively for node localization in UAV‐assisted WSNs. However, the MLP suffers from slow training speed, which limits its usage in real‐time applications. So, the extreme learning machine (ELM) is found to be a better alternative because it works on empirical error minimization theory, and its learning process requires only a single iteration. The detailed simulation analysis supports the proposed ELM localization scheme in terms of both localization accuracy and computational complexity. Abstract : This paper aimed to design extreme learning machine (ELM)‐based node localization technique using aerial anchors deployed in unmanned aerial vehicles (UAVs). UAVs provide more reliable communication through air to ground (AG) channel. Still, the nonlinear distortion introduced in the wireless channel limits localization accuracy of classical localization techniques. Hence, the highly nonlinear ELM can be applied effectively for node localization in UAV‐assisted WSNs. ELM works on empirical error minimization theory, and its learning process requires only a single iteration.