An Intelligent Dynamic Bandwidth Allocation Method to Support Quality of Service in Internet of Things


  • R. Mohandas
  • D. John Aravindhar



Dynamic bandwidth allocation, Reinforcement Learning, Machine learning models, Quality of Service


Worldwide, Internet of Things (IoT) devices will surpass a range of five billion by 2025 and developed countries will extend to advance by supplying almost two-thirds of such connections. With existing infrastructure, allocating bandwidth to billions of IoT devices is going to be cumbersome. This paper addresses the problem of Dynamic bandwidth allocation in IoT devices. We enhanced the dynamic bandwidth allocation algorithms to support QoS in different bandwidth ranges. Our Proposed innovative Machine learning-based Intelligent Dynamic Bandwidth Allocation (IDBA) algorithm allocates the bandwidth effectively between IoT devices based on utilization patterns observed through machine learning methods. Moreover, we showed that an IDBA algorithm results in supporting quality of service in terms of ensuring uninterrupted bandwidth to critical IoT application where bandwidth tolerance is zero percent, along with that IDBA increasing the network throughput correlated to other dynamic bandwidth allocation algorithms. We demonstrate simulations in different applications. The results show that IDBA achieves better throughput even in low bandwidth range.


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How to Cite

Mohandas, R., & Aravindhar, D. J. (2021). An Intelligent Dynamic Bandwidth Allocation Method to Support Quality of Service in Internet of Things. International Journal of Computing, 20(2), 254-261.