Meta-Heuristics optimization for mobile sink in WSNs based smart grid | ||
| IJCI. International Journal of Computers and Information | ||
| Article 3, Volume 11, Issue 2, July 2024, Pages 13-24 PDF (794.46 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijci.2024.261722.1152 | ||
| Authors | ||
| Duaa Gomaa* 1; Osama Abdel-Raouf2; Nancy El-Hefnawy3; Ahmed Kafafy4 | ||
| 1Data Science Dept., Faculty of Artificial Intelligence, Menofia university, Egypt. | ||
| 2Department of Machine intelligence, Faculty of Artificial Intelligence, Menoufia University | ||
| 3Faculty of Computers and Information, Tanta University, Tanta, Egypt | ||
| 4Department of Operation research, Faculty of computers and information, Menofia university | ||
| Abstract | ||
| Wireless Sensor Network (WSN) is made up of many battery-powered sensor nodes that are utilized for information gathering and transmission to sink in Smart grid (SG). The harsh channel conditions that characterise SG environment provide an important obstacle to WSN deployment in SG applications. While WSN sensors near the sink convey data to distant sensors, their energy is rapidly exhausted. So, energy holes are known as hotspot problems. In this paper, meta-Heuristics optimization algorithms are used to develop WSN-based SG. Particle swarm optimization, firefly algorithm and imperialist competitive algorithm, which are computationally efficient to handle WSN issues, have been studied. Single and multiple sinks, either static or mobile, offer greater flexibility and adaptability in monitoring and collecting data. Mobility is used to improve overall network coverage, help overcome network failures by moving to cover gaps, enhance connectivity, and even redistribute energy consumption in the network by allowing the workload to be shifted from one sensor to another. Particle Swarm Optimization enables fine-tuning of the resulting density to increase network lifetime and achieve better result. mobility is employed to help hotspot problem by balancing the energy consumption across the network, thereby extending the overall network lifetime with 1.6 months. | ||
| Keywords | ||
| Meta-heuristic; Particle Swarm Optimization; wireless sensor network; smart grid; mobility | ||
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