Intelligent resource allocation in internet of things using random forest and clustering techniques

The Internet of Things has proliferated, and the number of devices integrated into intelligent networks has made resource management and allocation one of the most critical challenges. The intrinsic constraints of IoT devices, such as energy consumption, limited bandwidth, and reduced computational power, have increased the demand for more intelligent and efficient resource allocation strategies. Numerous current resource allocation methods, such as evolutionary algorithms and multi-agent reinforcement learning, are grossly inefficient at adapting well to IoT networks in light of dynamic and rapid changes due to the inherent computational complexity and high cost. This paper proposes an intelligent resource allocation approach for Internet of Things (IoT) networks that integrates clustering and machine learning techniques. Initially, IoT devices are grouped using the K-Means algorithm based on features such as energy consumption and bandwidth requirements. A Random Forest model is then trained to accurately predict the resource needs of each cluster, enabling optimal allocation. Simulation results show that the proposed approach improves prediction accuracy to 94%, reduces energy consumption by 20%, and decreases response time by 10% compared to existing methods. These results highlight the effectiveness of the approach in managing resources in dynamic and scalable IoT environments.