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Exploring Enhanced Swarm Intelligence algorithms for Efficient Cyber Foraging on Transient Mobile Clouds

T. F. Ndambomve, F. Mokom, K. D. Taiwe

Abstract



In the evolving landscape of mobile computing, Transient Mobile Clouds (TMCs) leverage nearby mobile devices to form ad hoc computational networks, offering low-latency, cost-effective alternatives to centralized cloud systems. However, efficient task offloading in TMCs mobility faces unique challenges in maintaining resource distribution, minimizing energy consumption, and accommodating dynamic device participation, energy constraints, and fluctuating network conditions. Traditional Swarm Intelligence (SI) algorithms like Particle Swarm Optimization (PSO) and Discrete Artificial Bee Colony (DABC) struggle to balance energy consumption, latency, and load balancing in such environments. This study introduces two advanced task allocation algorithms: Enhanced Multi-Objective Particle Swarm Optimization (EMO-PSO) and Enhanced Multi-Objective Discrete Artificial Bee Colony (EMO-DABC), tailored for Transient Mobile Cloud (TMC) systems. These algorithms have adaptive cyber foraging mechanisms that improve computational load balance, processor utilization, throughput and energy efficiency by dynamically adjusting parameters based on device energy thresholds, task priority and neighbourhood clustering. Simulation results show substantial reductions in energy consumption (up to 18% for EMO-PSO and 15% for EMO-DABC), task completion times (9% improvement over standard DABC), and enhanced load balancing (up to 20% improvement for EMO-DABC). These improvements demonstrate the effectiveness of the proposed algorithms in optimizing resource allocation in dynamic TMC environments.

Keywords


Natural Language Processing, semantic analysis, unsupervised learning, statistical machine learning, clustering.

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