Optimizing Dynamic Bandwidth Allocation for IoT Devices with Machine Learning Algorithms

The immense increase in the Internet of Things (IoT) has posed a formidable problem in the effective implementation of bandwidth allocation for a broad and vast system of devices. Conventional Dynamic Bandwidth Allocation (DBA) strategies are not very effective in optimizing network throughput, especially where there are many devices and their traffic patterns are both heterogeneous and highly dynamic. The current paper suggests a model of Enhanced Dynamic Bandwidth Allocation (EDBA) that uses machine learning algorithms in predicting bandwidth demand and dynamically allocating resources. The model proposed applies the algorithm techniques of clustering IoT devices according to bandwidth usage patterns, which is then used to predict on-demand bandwidth needs using machine learning algorithms like linear regression. Simulation experiments prove the EDBA technique to be much more effective in enhancing the main Quality of Service (QoS) metrics, including throughput, latency and packet loss in comparison with the other conventional dynamic bandwidth allocation strategies. The findings reveal that, through machine learning integration, there is efficient bandwidth use, there is minimized network congestion, and there is high-quality service provision, despite the fluctuation of network conditions. This will offer a flexible solution to scale in line with the expanding needs of the 21st century IoT systems.