Performance comparison of VCN algorithms for node localization in wireless sensor networks

2011 ◽  
Author(s):  
Ran Wang ◽  
Hui Zhao ◽  
Yang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Performance Comparison ◽  
Wireless Sensor ◽  
Node Localization

scholarly journals Localization by Salp Swarm Optimization with Doppler Effect in Wireless Sensor Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 26-40
Author(s):  
Panimalar Kathiroli ◽  
◽  
Kanmani. x Kanmani. S
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Doppler Effect ◽  
Computation Time ◽  
Wireless Sensor ◽  
Multidimensional Space ◽  
Node Localization ◽  
Swarm Optimization ◽  
Localization Model ◽  
Random Node

Wireless sensor networks (WSNs) have lately been widely used due to its abundant practice in methods that have to be spread over a large range. In any wireless application, the position precision of node is an important core component. Node localization intends to calculate the geographical coordinates of unknown nodes by the assistance of known nodes. In a multidimensional space, node localization is well-thought-out as an optimization problem that can be solved by relying on any metaheuristic’s algorithms for optimal outputs. This paper presents a new localization model using Salp Swarm optimization Algorithm with Doppler Effect (LOSSADE) that exploit the strengths of both methods. The Doppler effect iteratively considers distance between the nodes to determine the position of the nodes. The location of the salp leader and the prey will get updated using the Doppler shift. The performance validation of the presented approach simulated by MATLAB in the network environment with random node deployment. A detailed experimental analysis takes place and the results are investigated under a varying number of anchor nodes, and transmission range in the given search area. The obtained simulation results are compared over the traditional algorithm along with other the state-of-the-art methods shows that the proposed LOSSADE model depicts better localization performance in terms of robustness, accuracy in locating target node position and computation time.

Performance Comparison of Multi-Agent Middleware Platforms for Wireless Sensor Networks

2018 ◽  
Vol 18 (7) ◽  
pp. 3039-3049 ◽  
Author(s):  
Miguel Augusto Bauermann Brasil ◽  
Bernhard Bosch ◽  
Flavio Rech Wagner ◽  
Edison Pignaton de Freitas
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Performance Comparison ◽  
Wireless Sensor ◽  
Multi Agent

scholarly journals Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 343 ◽  
Author(s):  
Dezhi Han ◽  
Yunping Yu ◽  
Kuan-Ching Li ◽  
Rodrigo Fernandes de Mello
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Random Mutation ◽  
Localization Accuracy ◽  
De Algorithm ◽  
Distance Vector

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

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