Integrating Edge Computing with Swarm Intelligence for Efficient IoT Device Management

Remote data processing problems and network management issues stemming from IoT device expansion require new combinations of edge computation solutions together with smart decision-making technology. The proposed research merges edge computing and swarm intelligence by employing modified Particle Swarm Optimization (PSO) for implementing dynamic resource management and workload distribution across IoT devices. A test environment composed of 500 IoT devices achieved substantial enhancements when using this system which exhibited 43% fewer latency levels while requiring 67% less energy and achieving 89% workload accuracy compared to traditional cloud platforms. The system proved 98% effective throughout periods of maximum usage. The integrated solution presents itself as an optimal solution for managing IoT devices at scale because it achieves enhanced performance by optimizing resource utilization and minimizing unnecessary computational overhead.