Authors: Semran Ojha, Professor Rahul Patidar, Professor Jayshree Boaddh
Abstract: The rapid expansion of the Internet of Things (IoT) has created significant challenges in managing computation and data processing efficiently. As billions of interconnected devices generate massive workloads, traditional cloud infrastructures experience latency and performance bottlenecks. To address these limitations, this research introduces an intelligent edge-level load distribution model using Random Forest techniques. The proposed system leverages edge computing to process tasks closer to data sources, thereby reducing dependency on centralized cloud servers. The Random Forest algorithm is utilized to learn from historical task patterns and predict optimal job scheduling sequences. This is integrated with a wolf optimization strategy that dynamically adjusts load distribution across heterogeneous edge nodes without prior training requirements. Experimental evaluation conducted using MATLAB demonstrates that the proposed model effectively minimizes makespan by 0.79% and enhances edge utilization by 16.25% compared to the existing Preference-Based Stable Matching (PBSM) model. These improvements confirm that machine learning- driven edge load balancing can significantly improve resource allocation, task completion time, and overall network efficiency in large-scale IoT environments.
