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Maximizing energy efficiency using Dinklebach’s and particle swarm optimization methods for energy harvesting wireless sensor networks

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Abstract

Recently, new ideas for harvesting energy of the sensor nodes from the surroundings are proposed. As compared to these renewable energy sources, radio frequency (RF) signals are widely considered as a promising solution to provide power to low-powered sensor nodes in a continuous manner. Here the energy efficiency of the Wireless Powered Sensor Network (WPSN) is viewed as the maximization problem, and it is derived as a nonlinear fractional programming problem. Also, it is non-convex and hence, it is challenging to solve. Dinklebach’s method is used to transform the concave-convex fractional problem into a convex optimization problem with the usage of methods in convex optimization theory. Also, an intelligent algorithm based on Particle Swarm Optimization (PSO) is provided to solve the energy efficiency maximization problem systematically and distributively. The simulation results show that the proposed algorithms based on Dinkelbach’s method and the PSO method achieve maximum energy efficiency.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Engineering College, Kovilpatti, Tamilnadu, 628503, India

    K Mohaideen Pitchai

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Correspondence to K Mohaideen Pitchai.

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Pitchai, K.M. Maximizing energy efficiency using Dinklebach’s and particle swarm optimization methods for energy harvesting wireless sensor networks. Sādhanā 47, 60 (2022). https://doi.org/10.1007/s12046-022-01839-w

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  • DOI: https://doi.org/10.1007/s12046-022-01839-w

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