Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

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.     

Implementation of an Efficient Artificial Bee Colony Algorithm for Node Localization in Unmanned Aerial Vehicle Assisted Wireless Sensor Networks

Node localization is a fundamental task in wireless sensor networks as it is useful for several localization based protocols and applications. Node localization using Global Poisoning System (GPS) employed fixed terrestrial anchor nodes suffers from high deployment cost and poor localization accuracy in GPS denied locations. These issues can be easily handled by deploying movable Unmanned Aerial Vehicles (UAVs). A movable UAV equipped with a single GPS module virtually increases number of anchor nodes and localizes a node at different locations. Hence, UAVs are cost effective and also provides high localization accuracy. As the flying altitude of UAV greatly influence localization accuracy, the present work firstly optimizes the flying height and then the node localization is defined as least square optimization problem using this optimal height. Since the classical received signal strength indicator based multilateration results high localization error, the least square localization using optimization techniques is found to be better alternative. The recently proposed Artificial Bee Colony (ABC) algorithm is a powerful optimization technique that can be applied for this optimization problem to achieve high accuracy. Thus, this paper aims at designing an ABC localization technique using UAV anchors to achieve minimum localization error. Further, we provide detailed simulation analysis to support the proposed ABC localization scheme.

     

Radial basis function-based node localization for unmanned aerial vehicle-assisted 5G wireless sensor networks

Neural Computing and Applications - Localization or positioning of wireless sensor nodes is an essential task for a wide range of applications in wireless sensor networks-based fifth generation...