TL;DR
The proposed algorithm can achieve the tradeoff between the energy efficiency and QoS provisioning by sacrificing about 10% network lifetime but improving about 15% outage performance.
A novel method for the Search and Optimization of IoT Service Composition towards QoS and Energy balance (SOISC-QE) is proposed and an enhanced Dung Beetle Optimization (DBO) is designed by integrating two complementary mechanisms.
This study presents a new method for selecting the most suitable route by combining the Markov Chain Model (MCM) with reinforcement learning techniques (MCM‐RLA) to ensure that the chain and reward functions align with the Quality of Service (QoS).
The results show the effectiveness of EACCO to design reliable, energy neutral and sustainable indoor IoT swarm robotic systems.
This study proposes a novel Energy-Aware Cluster Optimization Routing (EACO-R) protocol, which creates adaptive energy-aware routing paths de-pending on residual energy, connection stability, transmission distance, and network congestion.
This paper proposes a QoS-aware and energy-efficient metaheuristic optimization-based service placement strategy for an integrated IoT and fog computing environment to improve QoS and demonstrates that the developed hybrid algorithm reduces energy consumption by 3.09% and minimize network usage significantly compared w...
Findings validate the efficacy of incorporating swarm intelligence into the 5G architectures as a viable and self-optimizing solution for the promotion of connectivity and signal power performance in the next-generation high-density wireless networks.
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