A New Location Sensing Algorithm Based on DV-Hop and Quantum-Behaved Particle Swarm Optimization in WSN

In wireless sensor network, the location sensing of the sensor nodes is practical. If there is no location information of the sensor nodes, the perceived data would have no meaning. In recent years, the range-free location sensing algorithms have got great attention. DV-Hop localization algorithm is one of the important algorithm in range-free location algorithms. It has high efficiency, convenient operation and low energy consumption. However, the localization accuracy cannot meet the requirements in some applications. In this paper, a new localization method is proposed, which is based on DV-Hop and Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, it deals with the high influence of average single jumping distance and then modifies the calculation of it in the DV-Hop algorithm. Second, in order to solve the problem of the coordinate optimization in the DV-Hop algorithm, this study chooses QPSO algorithm to optimize the unknown nodes’ coordinates. Simulation results show that the new method can improve the localization accuracy of the unknown nodes obviously in WSN.

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Improved GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network

Journal of Communications ◽

10.12720/jcm.16.6.242-249 ◽

2021 ◽

pp. 242-249

Author(s):

M.Shahkhir Mozamir ◽

◽

Rohani Binti Abu Bakar ◽

Wan Isni Soffiah Wan Din ◽

Zalili Binti Musa

Keyword(s):

Particle Swarm Optimization ◽

Indoor Localization ◽

Particle Swarm ◽

Pso Algorithm ◽

Sensor Nodes ◽

Wireless Sensor ◽

Computational Time ◽

Swarm Optimization ◽

Localization Accuracy ◽

Local Neighborhood

Localization is one of the important matters for Wireless Sensor Networks (WSN) because various applications are depending on exact sensor nodes position. The problem in localization is the gained low accuracy in estimation process. Thus, this research is intended to increase the accuracy by overcome the problem in the Global best Local Neighborhood Particle Swarm Optimization (GbLN-PSO) to gain high accuracy. To compass this problem, an Improved Global best Local Neighborhood Particle Swarm Optimization (IGbLN-PSO) algorithm has been proposed. In IGbLN-PSO algorithm, there are consists of two phases: Exploration phase and Exploitation phase. The neighbor particles population that scattered around the main particles, help in the searching process to estimate the node location more accurately and gained lesser computational time. Simulation results demonstrated that the proposed algorithm have competence result compared to PSO, GbLN-PSO and TLBO algorithms in terms of localization accuracy at 0.02%, 0.01% and 59.16%. Computational time result shows the proposed algorithm less computational time at 80.07%, 17.73% and 0.3% compared others.

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WSN Node Localization Algorithm Based on Adaptive Particle Swarm Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.143-144.302 ◽

2011 ◽

Vol 143-144 ◽

pp. 302-306 ◽

Cited By ~ 4

Author(s):

Ying Gao ◽

W. S. Zhao ◽

C. Jing ◽

Wei Zheng Ren

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Localization Algorithm ◽

Node Localization ◽

Node Density ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Localization Precision ◽

Swarm Theory ◽

Range Free

In order to overcome shortcomings of existing range-free wireless sensor network (WSN) node localization methods such as huge computation volume and great effect of node density on localization precision, a WSN localization algorithm based on adaptive particle swarm optimization (APSO) was put forward in combination with particle swarm theory and DV-Hop algorithm. This algorithm improved localization precision by more than 20%, and the effect of node density on localization precision was significantly less than DV-Hop algorithm without any addition of hardware facilities and communication load.

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Localization of Large-Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection

Wireless Communications and Mobile Computing ◽

10.1155/2018/2473875 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18

Author(s):

Huanqing Cui ◽

Yongquan Liang ◽

Chuanai Zhou ◽

Ning Cao

Keyword(s):

Wireless Sensor Networks ◽

Particle Swarm Optimization ◽

Sensor Networks ◽

Large Scale ◽

Particle Swarm ◽

Wireless Sensor ◽

Localization Algorithm ◽

Swarm Optimization ◽

Localization Accuracy ◽

Unknown Node

Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.

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Study of range free centroid based localization algorithm and its improvement using particle swarm optimization for wireless sensor networks under log normal shadowing

International Journal of Information Technology ◽

10.1007/s41870-018-0201-5 ◽

2018 ◽

Vol 12 (3) ◽

pp. 975-981 ◽

Cited By ~ 5

Author(s):

Vikas Gupta ◽

Brahmjit Singh

Keyword(s):

Wireless Sensor Networks ◽

Particle Swarm Optimization ◽

Sensor Networks ◽

Particle Swarm ◽

Wireless Sensor ◽

Localization Algorithm ◽

Swarm Optimization ◽

Log Normal ◽

Range Free

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

Journal of Communications Technology Electronics and Computer Science ◽

10.22385/jctecs.v7i0.115 ◽

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.

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Method of Optimization for Target Localization Model Parameters Based on LSSVR

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.934 ◽

2011 ◽

Vol 268-270 ◽

pp. 934-939

Author(s):

Xue Wen He ◽

Gui Xiong Liu ◽

Hai Bing Zhu ◽

Xiao Ping Zhang

Keyword(s):

Particle Swarm Optimization ◽

Fitness Function ◽

Particle Swarm ◽

Parameters Optimization ◽

Target Localization ◽

Likelihood Method ◽

Support Vector ◽

Swarm Optimization ◽

Localization Accuracy ◽

Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

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Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy

Critical Developments and Applications of Swarm Intelligence - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-5134-8.ch002 ◽

2018 ◽

pp. 27-52 ◽

Cited By ~ 2

Author(s):

Jiarui Zhou ◽

Junshan Yang ◽

Ling Lin ◽

Zexuan Zhu ◽

Zhen Ji

Keyword(s):

Particle Swarm Optimization ◽

High Efficiency ◽

Particle Swarm ◽

Global Optimum ◽

Population Diversity ◽

Local Optimum ◽

Swarm Optimization ◽

Local Optima ◽

Von Neumann ◽

Swarm Intelligence Algorithm

Particle swarm optimization (PSO) is a swarm intelligence algorithm well known for its simplicity and high efficiency on various problems. Conventional PSO suffers from premature convergence due to the rapid convergence speed and lack of population diversity. It is easy to get trapped in local optima. For this reason, improvements are made to detect stagnation during the optimization and reactivate the swarm to search towards the global optimum. This chapter imposes the reflecting bound-handling scheme and von Neumann topology on PSO to increase the population diversity. A novel crown jewel defense (CJD) strategy is introduced to restart the swarm when it is trapped in a local optimum region. The resultant algorithm named LCJDPSO-rfl is tested on a group of unimodal and multimodal benchmark functions with rotation and shifting. Experimental results suggest that the LCJDPSO-rfl outperforms state-of-the-art PSO variants on most of the functions.

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