Pso and Fpo Based Optimization in Tuning Mpc of Wsn Model For Maximum Energy Harvesting
S. Thiyagarajan1, P. Gowthaman2, M. Venkatachalam3, M. Saroja4
1S. Thiyagarajan, Department of Electronics Science, Jaya College of Arts and Science, Chennai (Tamil Nadu), India.
2P. Gowthaman, Department of Electronics, Erode Arts and Science College, Erode (Tamil Nadu), India.
3M. Venkatachalam, Department of Electronics Science, Jaya College of Arts and Science, Chennai (Tamil Nadu), India.
4M. Saroja, Department of Electronics, Erode Arts and Science College, Erode (Tamil Nadu), India.
Manuscript received on 24 April 2019 | Revised Manuscript received on 03 May 2019 | Manuscript Published on 07 May 2019 | PP: 235-262 | Volume-7 Issue-6S3 April 2019 | Retrieval Number: F1047376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The reduction of the energy consumption compensated by harvesting RF energy of a WSN using techniques realized to practical extent, considering numerous forced limitations. In scheming energy harvesting regulation, techniques were suggested on control methods. The techniques reserved must promise in such a way the limitations forced by the utility with regard to quantity of acquired data are attained, whereas the lifetime of the network is prolonged, in contrast to present and simple arraying. In several such arraying, the controller for supervising, managing acquires more unnecessary estimations, or estimations that are intensely mutual from the nodes. Hence, control techniques have to be schemed for efficient energy harvest considering the utility demands through volume of estimations from data. Amidst several control techniques, model predictive control (MPC) is a part of important controlling methods. It has several effective uses. This paper contrasts MPC method for automated tuning with particle swarm optimization (PSO) and flower pollination optimization (FPO). The major confrontation of MPC is in tuning of control factors for several WSN targets, and PSO or FPO application for automated tuning may become member of solutions. MPC tuning issue is an optimization challenge. Optimization methods like PSO or FPO can be utilized. PSO and FPO are meta-exploratory approaches that are familiar to explore a global optimal solution rather at a higher proportion and without using ascent. The computational results for energy harvest of WSN reveal the influence of the suggested PSO and FPO dependent tuning.
Keywords: Wireless Sensor Networks, Model Predictive Control, Feedback Tuning, Energy Harvesting, Particle Swarm Optimization, Flower Pollination Optimization.
Scope of the Article: Energy Harvesting and Transfer for Wireless Sensor Networks