Intelligent Resource Allocation in IoT: An ACO-GA Approach

The rapid proliferation of Internet of Things (IoT) devices demands efficient resource allocation to optimize utilization and minimize communication overhead. This research introduces a novel hybrid approach that combines Ant Colony Optimization (ACO) and Genetic Algorithm (GA) to optimize resource allocation in IoT environments. The primary goal is to enhance network performance and reduce communication costs between IoT devices and gateways. Our hybrid approach leverages the strengths of ACO in finding optimal paths and GA's local search capabilities for fine-tuning, resulting in a robust and reliable solution for resource allocation in complex IoT settings. Comparative analysis, based on various benchmarks, demonstrates that the proposed method significantly outperforms existing resource allocation techniques, achieving substantial reductions in total communication costs. The results highlight the effectiveness of the hybrid ACO-GA approach for efficient resource management in IoT networks.