A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks

2010 ◽  
Vol 54 (6) ◽  
pp. 960-976 ◽  
Author(s):  
Andreas Konstantinidis ◽  
Kun Yang ◽  
Qingfu Zhang ◽  
Demetrios Zeinalipour-Yazti
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Assignment Problem ◽  
Wireless Sensor ◽  
Power Assignment ◽  
Multi Objective

scholarly journals Toward a Robust Multi-Objective Metaheuristic for Solving the Relay Node Placement Problem in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 677 ◽  
Author(s):  
José Lanza-Gutiérrez ◽  
Nuria Caballé ◽  
Juan Gómez-Pulido ◽  
Broderick Crawford ◽  
Ricardo Soto
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Evolutionary Algorithm ◽  
Energy Cost ◽  
Search Algorithm ◽  
Relay Node ◽  
Wireless Sensor ◽  
Placement Problem ◽  
Node Placement ◽  
Multi Objective

During the last decade, Wireless sensor networks (WSNs) have attracted interest due to the excellent monitoring capabilities offered. However, WSNs present shortcomings, such as energy cost and reliability, which hinder real-world applications. As a solution, Relay Node (RN) deployment strategies could help to improve WSNs. This fact is known as the Relay Node Placement Problem (RNPP), which is an NP-hard optimization problem. This paper proposes to address two Multi-Objective (MO) formulations of the RNPP. The first one optimizes average energy cost and average sensitivity area. The second one optimizes the two previous objectives and network reliability. The authors propose to solve the two problems through a wide range of MO metaheuristics from the three main groups in the field: evolutionary algorithms, swarm intelligence algorithms, and trajectory algorithms. These algorithms are the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Multi-Objective Artificial Bee Colony (MO-ABC), Multi-Objective Firefly Algorithm (MO-FA), Multi-Objective Gravitational Search Algorithm (MO-GSA), and Multi-Objective Variable Neighbourhood Search Algorithm (MO-VNS). The results obtained are statistically analysed to determine if there is a robust metaheuristic to be recommended for solving the RNPP independently of the number of objectives.

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