scholarly journals Improved 3-D Indoor Positioning Based on Particle Swarm Optimization and the Chan Method

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 208
Author(s):  
Shanshan Chen ◽  
Zhicai Shi ◽  
Fei Wu ◽  
Changzhi Wang ◽  
Jin Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Complexity ◽  
Large Scale ◽  
Particle Swarm ◽  
Base Station ◽  
Base Stations ◽  
Time Of Arrival ◽  
Swarm Optimization ◽  
Positioning Accuracy ◽  
Initial Location

Time of arrival (TOA) measurement is a promising method for target positioning based on a set of nodes with known positions, with high accuracy and low computational complexity. However, most positioning methods based on TOA (such as least squares estimation, maximum-likelihood, and Chan, etc.) cannot provide desirable accuracy while maintaining high computational efficiency in the case of a non-line of sight (NLOS) path between base stations and user terminals. Therefore, in this paper, we proposed a creative 3-D positioning system based on particle swarm optimization (PSO) and an improved Chan algorithm to greatly improve the positioning accuracy while decreasing the computation time. In the system, PSO is used to estimate the initial location of the target, which can effectively eliminate the NLOS error. Based on the initial location, the improved Chan algorithm performs iterative computations quickly to obtain the final exact location of the target. In addition, the proposed methods will have computational benefits in dealing with the large-scale base station positioning problems while has highly positioning accuracy and lower computational complexity. The experimental results demonstrated that our algorithm has the best time efficiency and good practicability among stat-of-the-art algorithms.

scholarly journals QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

2021 ◽  
Vol 14 (1) ◽  
pp. 270-280
Author(s):  
Abhijit Halkai ◽  
◽  
Sujatha Terdal ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Large Scale ◽  
Cluster Head ◽  
Particle Swarm ◽  
Base Station ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Localization Model

A sensor network operates wirelessly and transmits detected information to the base station. The sensor is a small sized device, it is battery-powered with some electrical components, and the protocols should operate efficiently in such least resource availability. Here, we propose a novel improved framework in large scale applications where the huge numbers of sensors are distributed over an area. The designed protocol will address the issues that arise during its communication and give a consistent seamless communication system. The process of reasoning and learning in cognitive sensors guarantees data delivery in the network. Localization in Scarce and dense sensor networks is achieved by efficient cluster head election and route selection which are indeed based on cognition, improved Particle Swarm Optimization, and improved Ant Colony Optimization algorithms. Factors such as mobility, use of sensor buffer, power management, and defects in channels have been identified and solutions are presented in this research to build an accurate path based on the network context. The achieved results in extensive simulation prove that the proposed scheme outperforms ESNA, NETCRP, and GAECH algorithms in terms of Delay, Network lifetime, Energy consumption.

Efficient Heuristics for Large-Scale Vehicle Routing Problems Using Particle Swarm Optimization

2012 ◽  
Vol 3 (2) ◽  
pp. 34-50
Author(s):  
A. Chandramouli ◽  
L. Vivek Srinivasan ◽  
T. T. Narendran
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Large Scale ◽  
Particle Swarm ◽  
Computational Effort ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Customer Base ◽  
Benchmark Datasets ◽  
Problem Instances

This paper addresses the Capacitated Vehicle Routing Problem (CVRP) with a homogenous fleet of vehicles serving a large customer base. The authors propose a multi-phase heuristic that clusters the nodes based on proximity, orients them along a route, and allots vehicles. For the final phase of determining the routes for each vehicle, they have developed a Particle Swarm Optimization (PSO) approach. Benchmark datasets as well as hypothetical datasets have been used for computational trials. The proposed heuristic is found to perform exceedingly well even for large problem instances, both in terms of quality of solutions and in terms of computational effort.

scholarly journals NLOS Error Mitigation in Cellular Positioning using PSO Optimization Algorithm

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Stevo Lukić ◽  
Mirjana Simić
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Mobile Station ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Cellular Radio ◽  
Object Function ◽  
Swarm Optimization ◽  
Station Location

Non-Line-Of-Sight conditions pose a major challenge to cellular radio positioning. Such conditions, when the direct Line-Of-Sight path is blocked, result in additional propagation delay for the signal, additional attenuation, and an angular bias. Therefore,many researchers have proposed various algorithms to mitigate the measured error caused by this phenomenon. This paper presentsthe procedure for improving accuracy of determining the mobile station location in cellular radio networks in Non-Line-of-Sightpropagation environment, based on the Time Of Arrival oriented estimator using the Particle Swarm Optimization algorithm. Incomputer science, Particle Swarm Optimization is an evolutionary computational method that optimizes a problem by iteratively tryingto improve a candidate solution with regard to a given measure of quality. The proposed algorithm uses the repeating Time-Of-Arrivaltest measurements using the four base stations and for simulation selects the measurement combination that give the smallest regionenclosed by the overlap of four circles. In this way, the smallest intersect area of the four Time-Of-Arrival circles is obtained, andtherefore the smallest positioning error. After that, we consider the complete problem as a combinatorial optimization problem withthe corresponding object function that represents the nonlinear relationship between the intersection of the four circles and the mobilestation location. The Particle Swarm Optimization finds the optimal solution of the object function and efficiently determines themobile station location. The simulation results show that the proposed method outperforms conventional algorithms such as theWeighted Least Squares and the Levenberq-Marquardt method.

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