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• W2558883721 abstract "Coverage, one of the most important performance metrics for wireless sensor networks, reflects on how well a sensor field is monitored. Coverage problem is a devoted study of a node placement optimization problem in the coverage configuration before network deployment, where the objective is to find the optimal locations to place sensor nodes, such that the number of nodes (or the network cost) can be minimized and the coverage requirements can be satisfied. In this paper, we propose a harmony search (HS)-based deployment algorithm that can locate the optimal number of sensor nodes as well as their optimal locations for maximizing the network coverage and minimizing the network cost. The ability of HS is modified to automatically evolve the appropriate number of sensor nodes as well as their optimal locations. This can be accomplished by integrating the concept of adaptable length encoding in each solution vector to represent a variable number of candidate sensor nodes. Network coverage ratio, number of sensor nodes, and minimum distance between sensor nodes are the chief elements of a new objective function that has been offered to confirm the choice of the optimal number of sensor nodes and their positions. Experimental results show the ability of the proposed algorithm to find the appropriate number of sensor nodes and their locations. Furthermore, a comparative study with a metaheuristic Genetic-based algorithm and a random deployment technique has also been conducted and its results confirm the superiority of the proposed algorithm." @default.
• W2558883721 created "2016-12-08" @default.
• W2558883721 creator A5008616605 @default.
• W2558883721 creator A5040989979 @default.
• W2558883721 date "2017-02-01" @default.
• W2558883721 modified "2023-10-11" @default.
• W2558883721 title "Maximizing Wireless Sensor Network Coverage With Minimum Cost Using Harmony Search Algorithm" @default.
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• W2558883721 doi "https://doi.org/10.1109/jsen.2016.2633409" @default.
• W2558883721 hasPublicationYear "2017" @default.
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