A Dynamic Ripple-Driven Adaptive Federated Swarm Optimization Framework for Scalable and Energy-Efficient Resource Management in IoT MEC Networks
Keywords:
Dynamic Ripple Mechanism, Federated Swarm Optimization, IoT-MEC Networks, Energy Efficiency, Resource Management, Adaptive Clustering, Computation Offloading, Load Balancing, Scalability, Edge IntelligenceAbstract
The Internet of Things (IoT) has expanded rapidly, generating massive data and computation demands that challenge traditional centralized cloud systems. Mobile Edge Computing (MEC) addresses these issues by processing data closer to devices, yet efficient resource management remains difficult due to dynamic workloads, energy constraints, and large-scale node diversity. Existing optimization methods are mostly static and centralized, leading to poor adaptability, unbalanced energy use, and scalability issues. To overcome these limitations, this research introduces a Dynamic Ripple-driven Adaptive Federated Swarm Optimization (DRAFSO)framework for scalable and energy-efficient resource management in IoT-MEC networks. The proposed DRAFSO framework integrates a ripple-inspired dynamic adaptation mechanism that emulates wave propagation to enable continuous, context-aware adjustments under varying network conditions. Combined with Federated Swarm Intelligence, the system promotes decentralized optimization and collaborative decision making among MEC nodes without sharing raw data, thus enhancing both privacy and scalability. DRAFSO jointly optimizes clustering, routing, and computation offloading through ripple-driven adaptive updates, ensuring balanced resource allocation and minimal latency. The primary objective is to design a dynamic, scalable, and energy-efficient optimization strategy that enhances adaptability and system lifetime while reducing energy consumption. Experimental results show that DRAFSO significantly improves convergence speed, energy efficiency, and network robustness compared to traditional algorithms. By enabling real-time adaptability and federated cooperation, the framework ensures sustainable and intelligent resource management across heterogeneous IoT-MEC environments. This innovation establishes a new direction for next-generation edge networks, achieving optimal performance through dynamic, privacy-preserving, and energy-aware optimization.
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