Abstract
The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue for the successful operation of every wireless sensor network. This article pursues the maximization of area coverage and area k-coverage by using computational intelligence algorithms, i.e., a genetic algorithm and a particle swarm optimization algorithm. Their performance was evaluated via comparative simulation tests, made not only against each other but also against two other well-known algorithms. This appraisal was made using statistical testing. The test results, that proved the efficacy of the algorithms proposed, were analyzed and concluding remarks were drawn.
Highlights
A wireless sensor network (WSN) is a network of wirelessly interconnected devices that are spread over an area of interest
The efficacy of both the genetic algorithm and the Particle swarm optimization (PSO) algorithms in maximizing the coverage and the k-coverage of area in a WSN deployed at a two-dimensional squared area is investigated via analytical simulation tests performed in MathWorks MATLAB environment
The optimization of the area coverage in WSNs is the subject of the research work presented in this article
Summary
A wireless sensor network (WSN) is a network of wirelessly interconnected devices that are spread over an area of interest. A WSN, based on the combinational use of its sensor nodes and its base station(s), is able to monitor the ambient conditions over wide regions of interest and send relevant information to distant destinations This is why WSNs are considered to be the core of Internet of Everything (IoE) and support an endlessly evolving range of human activities related with industry, agriculture, surveillance and reconnaissance, smart homes, smart cities, environment and habitat monitoring, biomedical applications, military applications traffic control, fire detection, inventory control, agriculture, machine failure diagnosis, and energy management applications [3,4,5,6,7,8,9,10]
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