Journal
IET WIRELESS SENSOR SYSTEMS
Volume 10, Issue 2, Pages 78-87Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-wss.2019.0083
Keywords
wireless sensor networks; genetic algorithms; Pareto optimisation; search problems; computational complexity; small-scale uniform environments; hybrid multiobjective optimisation algorithm; multiobjective bee algorithms; Levy flight random walk; deployment problem; WSNs; large-scale nonuniform 3D environments; small-scale uniform 2D environments; multiobjective LF bee algorithm; SPEA2 algorithms; node deployment; wireless sensor networks; multiobjective Levy flight bee algorithm; computer networks; basic network services; conflicting optimisation factors; sophisticated issue; NP-complete problem; single-objective metaheuristic algorithms
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Wireless sensor networks (WSNs) play a prominent role in the world of computer networks. WSNs rely on deployment as a basic requirement and an effective factor on the basic network services. In deployment, creating a balance between conflicting optimisation factors, e.g. connectivity and coverage, is a challenging and sophisticated issue, so that deployment turns into an NP-complete problem. The majority of existing researches has attempted to tackle this problem by applying classic single-objective metaheuristic algorithms in 2D small-scale uniform environments. In this study, a new hybrid multi-objective optimisation algorithm, which is constructed by the combination of multi-objective bee algorithms and Levy flight (LF) random walk is proposed to deal with the deployment problem in WSNs. For this purpose, two of the most important criteria, connectivity and coverage, have been considered as objectives. A series of experiments are carried out in large-scale non-uniform 3D environments, despite the fact that most of the present methods are applicable in small-scale uniform 2D environments. This study completely takes into account the stochastic behaviour of swarms, something that other papers do not consider. The evaluation results show that the multi-objective LF bee algorithm, in most cases, surpasses NSGAII, IBEA and SPEA2 algorithms.
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