A study on the application of different two-objective evolutionary algorithms to the node localization problem in wireless sensor networks

2011 ◽  
Author(s):  
Massimo Vecchio ◽  
Roberto Lopez-Valcarce ◽  
Francesco Marcelloni
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithms ◽  
Wireless Sensor ◽  
Localization Problem ◽  
Node Localization

scholarly journals Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2515 ◽  
Author(s):  
Ivana Strumberger ◽  
Miroslav Minovic ◽  
Milan Tuba ◽  
Nebojsa Bacanin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Tree Growth ◽  
Optimization Algorithm ◽  
Environmental Control ◽  
Wireless Sensor ◽  
Problem Instance ◽  
Localization Problem ◽  
Node Localization

Wireless sensor networks, as an emerging paradigm of networking and computing, have applications in diverse fields such as medicine, military, environmental control, climate forecasting, surveillance, etc. For successfully tackling the node localization problem, as one of the most significant challenges in this domain, many algorithms and metaheuristics have been proposed. By analyzing available modern literature sources, it can be seen that the swarm intelligence metaheuristics have obtained significant results in this domain. Research that is presented in this paper is aimed towards achieving further improvements in solving the wireless sensor networks localization problem by employing swarm intelligence. To accomplish this goal, we have improved basic versions of the tree growth algorithm and the elephant herding optimization swarm intelligence metaheuristics and applied them to solve the wireless sensor networks localization problem. In order to determine whether the improvements are accomplished, we have conducted empirical experiments on different sizes of sensor networks ranging from 25 to 150 target nodes, for which distance measurements are corrupted by Gaussian noise. Comparative analysis with other state-of-the-art swarm intelligence algorithms that have been already tested on the same problem instance, the butterfly optimization algorithm, the particle swarm optimization algorithm, and the firefly algorithm, is conducted. Simulation results indicate that our proposed algorithms can obtain more consistent and accurate locations of the unknown target nodes in wireless sensor networks topology than other approaches that have been proposed in the literature.

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.

Sign in / Sign up

Export Citation Format

Share Document