scholarly journals Signcryption Based Security for PSO-GD Localization in WSN

2019 ◽  
Vol 8 (4) ◽  
pp. 10592-10596
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Node ◽  
Particle Swarm ◽  
Location Information ◽  
Localization Algorithm ◽  
Swarm Optimization ◽  
Secure Localization ◽  
Beacon Node ◽  
Unknown Node

A signcryption based security for localization using PSO-GD algorithm in WSN is proposed which combines particle swarm optimization (PSO) and gradient decent (GD) methods for secure localization. Initially, the beacon node signcrypt the message with the keys and broadcasted the information to the surrounding nodes so as to enable receiver to identify the sender. Then the received location information is unsigncrypted by each unknown sensor node and verified. If the unsignsryption is performed successfully, the unknown node accepts the message, otherwise discarded. From the location information gathered, the unknown node estimates its location coordinates through PSOGD localization algorithm.

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

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.

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.  

scholarly journals Monte Carlo localization algorithm based on particle swarm optimization

Automatika ◽  
2019 ◽  
Vol 60 (4) ◽  
pp. 451-461 ◽  
Author(s):  
Cuiran Li ◽  
Jianli Xie ◽  
Wei Wu ◽  
Haoshan Tian ◽  
Yingxin Liang
Keyword(s):  
Monte Carlo ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Localization Algorithm ◽  
Swarm Optimization ◽  
Monte Carlo Localization

Binary Particle Swarm Optimization (BPSO) Algorithm for Distributed Node Localization

2014 ◽  
Vol 556-562 ◽  
pp. 3666-3669
Author(s):  
Soo Young Shin ◽  
Ifa Fatihah Mohamed Zain
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Computation Time ◽  
Wireless Sensor ◽  
Binary Particle Swarm Optimization ◽  
Localization Error ◽  
Node Localization ◽  
Swarm Optimization ◽  
Unknown Node

In this paper, we propose a binary particle swarm optimization (BPSO) algorithm for distributed node localization in wireless sensor networks (WSNs). Each unknown node performs localization under the distance measurement from three or more neighboring anchors. The node that gets localized will be used as a reference for remaining nodes. A comparison of the performances of PSO and BPSO in terms of localization error and computation time is presented using simulations in Matlab.

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%.

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