| Summary: | The Internet of Things (IoT) is evolving, driven by the increasing demand for bandwidth. A key focus is on minimizing communication delays. This paper introduces a new solution called Predictive Dynamic Bandwidth Allocation (PDBA), using adaptive predictive algorithms in the IoT context. The approach involves predicting resource needs and network conditions, allowing for efficient bandwidth allocation. The PDBA framework uses advanced predictive algorithms to foresee bandwidth requirements for IoT devices at specific intervals, contributing to low communication latency—crucial for responsive IoT applications.To handle dynamic changes in the IoT environment, like device connectivity fluctuations during sleep mode transitions, our framework incorporates a dynamic perceptual algorithm inspired by reinforcement learning principles. This real-time adaptation mitigates the impact of environmental fluctuations, ensuring consistently low latency. Simulations across various IoT scenarios demonstrate the PDBA framework’s effectiveness. The adaptive predictive algorithm significantly improves latency by nearly 10%, reduces packet loss to 6.8%, and increases throughput to 94.2% compared to traditional methods, with notably lower computing times of 0.69 seconds. These results underscore PDBA’s potential to enhance Quality of Service (QoS) in IoT networks. The article provides a comprehensive examination of the PDBA framework’s components, its seamless integration into the IoT environment, and its substantial role in optimizing communication performance within IoT networks.
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