Use of granular computing for resource optimization in IoT networks

The exponential growth of connected devices on the Internet of Things (IoT) has transformed multiple domains, from industrial automation to smart environments. However, this proliferation introduces complex challenges in efficiently managing limited resources—such as bandwidth, energy, and processing capacity, especially in dynamic and heterogeneous IoT networks. Existing optimization methods often fail to adapt in real-time or scale adequately under variable conditions, exposing a critical gap in resource management strategies for dense deployments. The present study proposes a granular computing framework designed for dynamic resource optimization in IoT environments to address this. The methodology comprises three key stages: granular decomposition to divide tasks and resources into manageable grains, granular aggregation to reduce computational load through data fusion, and adaptive granular selection to refine resource allocation based on current system states. These techniques were implemented and evaluated in a controlled industrial IoT testbed comprising over 80 devices. Comparative experiments against heuristic and AI-based baselines revealed statistically significant improvements: a 25% increase in processing throughput, a 20% reduction in energy consumption, and a 60% decrease in error rate. Additionally, quality of service (QoS) reached 95%, and latency was reduced by 25%, confirming the effectiveness of the proposed model in ensuring robust and energy-efficient performance under varying operational loads.