Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

Gravitational Particle Swarm Optimization Localization Algorithm for Wireless Sensor Network Nodes

2014 ◽  
Vol 556-562 ◽  
pp. 4622-4627
Author(s):  
Shu Wang Zhou ◽  
Ming Lei Shu ◽  
Ming Yang ◽  
Ying Long Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Method Of Least Squares ◽  
Swarm Optimization ◽  
Network Nodes ◽  
Ranging Error ◽  
Anchor Nodes ◽  
Localization Approach

A range-based localization approach which named gravitational particle swarm optimization localization algorithm (GL) has been proposed. This algorithm considered the influence from the position of anchor nodes to the localization results, GL can directly searched out the coordinates of unknown nodes by the distance from anchor nodes to unknown nodes. As is shown in the experiment results, GL not only has high positioning accuracy, but also overcomes the defect that location error increases rapidly as the ranging error increases, compares with normal schemes (such as method of least squares, ML ) GL’s accuracy can improve 40% as the ranging error is 35%.

scholarly journals A Novel Multiobjective Quantum-Behaved Particle Swarm Optimization Based on the Ring Model

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Di Zhou ◽  
Yajun Li ◽  
Bin Jiang ◽  
Jun Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Population Based ◽  
Swarm Optimization ◽  
Ring Model ◽  
Global Optima ◽  
Searching Ability ◽  
Update Strategy

Due to its fast convergence and population-based nature, particle swarm optimization (PSO) has been widely applied to address the multiobjective optimization problems (MOPs). However, the classical PSO has been proved to be not a global search algorithm. Therefore, there may exist the problem of not being able to converge to global optima in the multiobjective PSO-based algorithms. In this paper, making full use of the global convergence property of quantum-behaved particle swarm optimization (QPSO), a novel multiobjective QPSO algorithm based on the ring model is proposed. Based on the ring model, the position-update strategy is improved to address MOPs. The employment of a novel communication mechanism between particles effectively slows down the descent speed of the swarm diversity. Moreover, the searching ability is further improved by adjusting the position of local attractor. Experiment results show that the proposed algorithm is highly competitive on both convergence and diversity in solving the MOPs. In addition, the advantage becomes even more obvious with the number of objectives increasing.

scholarly journals Using Particle Swarm Optimization as Pathfinding Strategy in a Space with Obstacles

2021 ◽  
Author(s):  
David
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Population Based ◽  
Target Point ◽  
Adaptive Optimization ◽  
Swarm Optimization ◽  
Algorithm Performance ◽  
Long Time

Particle swarm optimization (PSO) is a search algorithm based on stochastic and population-based adaptive optimization. In this paper, a pathfinding strategy is proposed to improve the efficiency of path planning for a broad range of applications. This study aims to investigate the effect of PSO parameters (numbers of particle, weight constant, particle constant, and global constant) on algorithm performance to give solution paths. Increasing the PSO parameters makes the swarm move faster to the target point but takes a long time to converge because of too many random movements, and vice versa. From a variety of simulations with different parameters, the PSO algorithm is proven to be able to provide a solution path in a space with obstacles.

scholarly journals On the Localization Algorithm of Wireless Sensor Network and Its Application

2017 ◽  
Vol 13 (03) ◽  
pp. 40 ◽  
Author(s):  
Honglei Jia ◽  
Jiaxin Zheng ◽  
Gang Wang ◽  
Yulong Chen ◽  
Dongyan Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Simulation Experiment ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Swarm Optimization

<span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">This paper carries out in-depth and meticulous analysis of the DV-Hop localization algorithm for wireless sensor network. It improves the DV-Hop algorithm into a node localization algorithm based on one-hop range, and proposes the centroid particle swarm optimization localization algorithm based on RSSI by adding the RSSI and particle swarm optimization algorithm to the traditional centroid localization algorithm. Simulation experiment proves that the two algorithms have excellent effect.</span>

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