2021/02/22 by Sotirios K. Goudos, Achilles D. Boursianis, Goudos, Sotirios K. +11
Computer Science · #Distributed Sensor Networks and Detection Algorithms #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2102.11275
openalex publication_date 2021/02/22 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/01
The advent of Internet of Things (IoT) has bring a new era in communication\ntechnology by expanding the current inter-networking services and enabling the\nmachine-to-machine communication. IoT massive deployments will create the\nproblem of optimal power allocation. The objective of the optimization problem\nis to obtain a feasible solution that minimizes the total power consumption of\nthe WSN, when the error probability at the fusion center meets certain\ncriteria. This work studies the optimization of a wireless sensor network (WNS)\nat higher dimensions by focusing to the power allocation of decentralized\ndetection. More specifically, we apply and compare four algorithms designed to\ntackle Large scale global optimization (LGSO) problems. These are the memetic\nlinear population size reduction and semi-parameter adaptation (MLSHADE-SPA),\nthe contribution-based cooperative coevolution recursive differential grouping\n(CBCC-RDG3), the differential grouping with spectral clustering-differential\nevolution cooperative coevolution (DGSC-DECC), and the enhanced adaptive\ndifferential evolution (EADE). To the best of the authors knowledge, this is\nthe first time that LGSO algorithms are applied to the optimal power allocation\nproblem in IoT networks. We evaluate the algorithms performance in several\ndifferent cases by applying them in cases with 300, 600 and 800 dimensions.\n