TL;DR
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 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...
The results demonstrate that EGWO achieves faster convergence speed, higher recommendation quality, and uniform behavior across all test scenarios, and is suitable for practical application in real-time agricultural use cases, ensuring guaranteed optimal service allocation and improved decision support at the network's...
A modular QoS-aware decision-making framework that integrates QoS profiles, key performance indicators (KPIs), utility functions, and multi-criteria decision-making mechanisms to support both static and dynamic service allocation is proposed.
The GWO-SSA approach provides a robust, scalable, and energy-efficient cluster solution for an IoT environment, making it suitable for real-time and large-scale sensor network applications.
This work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications.
The proposed RSO–SFO framework integrates both strategies within a unified fitness function designed to minimize deployment cost while ensuring efficient allocation of fog resources, confirming the effectiveness and robustness of the proposed hybrid strategy for optimal service placement in fog-based IoT environments.
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