Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization

Abstract

The localization of Wireless sensor networks (WSNs) has been recognized as one of the most challenging problems to overcome. Thus, much work has been given to solving this difficult problem. In emergency services, navigational systems, civil/military surveillance etc., locating the signal source in a WSN is essential. A novel approach for sensor node localization using range-based localization methodology has been proposed to overcome this issue. The problem is expressed in the form of a maximum probability distribution function. The use of an RSSI-based Time Difference of Arrival (TDOA) measurement model, along with the Chan algorithm, is used to find the coordinates of unknown nodes has been proposed. With the help of ultra-wideband, this research aims to develop new and precise localization algorithms for wireless sensor networks (WSNs). This work offers localization using two-hybrid localization algorithms, i.e., ELPSO (Ensemble learning particle swarm optimization) and PSO-BPNN (Back-propagation neural network optimized by particle swarm optimization). Further, the error optimization accuracy has been compared between those algorithms using simulations. The proposed techniques consistently offer a better localization accuracy than the conventional algorithms available in the literature. The new localization methods with optimal techniques reduce the error value to a minimal distance. The distance value of localization error is nearly2.7cms compared to other designs from the literature. It is noted as significantly less.

Optimized Range-Free Localization Scheme Using Autonomous Groups Particles Swarm Optimization for Anisotropic Wireless Sensor Networks

“…How to determine the collinearity is an essential problem. Here, we determine whether the triangle area is zero to judge collinearity, see as in Equation (26).…”

“…To address the issue of strict synchronization localization algorithm, a simplifying method based on two synchronized nodes are introduced to indoor localization system [25]. An enhanced Chan algorithm is applied in hybrid RSSI and TDOA localization model to obtain the position of target sensor nodes for its merit of stability and efficiency [26]. Two PSO-based optimized methods, ELPSO and PSO-BPNN are offered to further improve the localization accuracy.…”

Enhancing Smart Irrigation Efficiency: A New WSN-Based Localization Method for Water Conservation

“…Numerous methods and algorithms have been proposed to address the node localization problem, aiming to achieve accurate and efficient localization results. We categorize the related work into three main approaches: range-based, range-free, and hybrid localization techniques [19][20][21][22][23][24][25][26][27][28].…”

“…Range-based localization techniques rely on distance or range measurements between sensor nodes to estimate their positions [19][20][21][22]. The authors in [19] consider the RSS method for location identification.…”

Electromechanical Integration Construction of Automobile Inspection Equipment Based on Particle Swarm Optimization

“…With the continuous improvement of computer processing capacity and the rapid growth of relevant theoretical research fields such as artificial intelligence algorithms, the vehicle detection equipment is mainly composed of sensors and actuators, among which the executive components include the engine speed signal acquisition module, ECU, master control unit (STC12C4S7) and peripheral circuit boards, which are one of the most important core parts of the device operation [7][8]. The vehicle detection equipment refers to the system for real-time monitoring of vehicle driving status, which consists of three parts: hardware, software and application management system.…”