Abstract
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.
Highlights
Over the last years, Wireless Sensor Networks (WSNs) have attracted a great interest in both industry and academy
Nigam and Agarwal [28] designed a branch-and-cut algorithm to deploy the minimum number of Relay Node (RN) in ST-WSNs, such that there was a prespecified delay bound between the sensors and the sink node
This paper presents a more complete development with an intensive statistical study comparing the performance of the metaheuristics solving the two Relay Node Placement Problem (RNPP)
Summary
Wireless Sensor Networks (WSNs) have attracted a great interest in both industry and academy. A Multi-Objective (MO) approach means a more realistic focus, where several conflicting objectives are simultaneously optimized [12] On this basis, this paper studies how to efficiently deploy energy-harvesting RNs in previously-established static T-WSNs by following an ST network model. This paper studies how to efficiently deploy energy-harvesting RNs in previously-established static T-WSNs by following an ST network model This problem is known as the Relay Node Placement Problem (RNPP). The authors apply a set of MO metaheuristics to solve two formulations of the same RNPP with a different number of objectives: two and three The goal of this analysis is to identify potential robust MO techniques, which could be recommended as a general solving-method for problems as the one considered in this work. Conclusions and future lines of research are left for Section 8
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