Sampling-Based Density Peaks Clustering for Energy-Aware Industrial IoT Networks
S Rajkumar, R Gopalakrishnan, T S Shreeraksha, S Dhanaraj, V Kabiyashwanth · 2025
In Industrial Internet of Things (IIoT) settings, effective data processing and energy management are essential due to the resource-limited characteristics of the linked equipment. This research introduces a unique Sampling-Based Density Peaks Clustering (SDPC) algorithm designed for energy-efficient clustering in IIoT networks. The suggested method improves classic Density Peak Clustering by incorporating a sampling mechanism that decreases computational burden while preserving excellent clustering precision. SDPC efficiently clusters devices by determining cluster centers through local density and distance parameters, hence reducing communication costs and prolonging the network’s operational lifespan. The energy consumption model is integrated into the clustering process, prioritizing devices with greater energy reserves as cluster heads, hence enhancing energy load distribution throughout the network. Simulation results indicate that SDPC realizes substantial enhancements, featuring a 30% decrease in energy usage and a 25% augmentation in network durability relative to leading clustering methods. This method has significant potential for enhancing energy management in IIoT networks, facilitating more sustainable and scalable industrial processes.