A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems
نویسندگان
چکیده
Fifth-generation (5G) empowered internet of things (IoT) edge networks suffer from latency in delay-sensitive applications. To fulfil the low requirements beyond fifth-generation (B5G)-IoT applications and provide quality service (QoS) to IoT-edge communication, it is important minimize server delay. Furthermore, 5G-IoT systems consume more power than their predecessors, which a concern given growing size future IoT networks. This research presents hybrid power-aware approach for B5G-IoT (HLPA B5G-IoT) that minimizes with minimum overhead on battery-constrained nodes while simultaneously providing power-efficient solution B5G-IoT-edge HLPA has novel algorithm classifier tool (ACT) selecting appropriate optimization algorithms based characteristics systems. The ACT matrix not only parametrically compares existing approaches but also identifies crucial parameters enable selection load balancing energy efficiency. In this paper, metaheuristic algorithms, i.e., biogeography-based (BBO) grey wolf (GWO), are tailored meet efficiency proposed load-balancing reduces improves overall network performance by 33.33%, 27.45%, 23.52%, 21.56%, 13.72%, 11.76%, 7.84% compared simulated annealing (SA), genetic (GA), particle swarm (PSO), bacteria foraging (BFA), ant colony (ACO), bat (BA), SA PSO (GSP), respectively. power-efficiency consumes 46.6%, 40%, 32.2%, 27.7%, 15.5%, 11.1%, 6.6% less SA, GA, PSO, BFA, ACO, BA, GSP,
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3200035