An Active Queue Management to Assist Time-critical Applications in Rural Area Networks using deep learning

Rural area networks (RANs) face unique challenges in providing reliable, high-performance network services, especially for time-critical applications such as real-time video streaming. Limited network resources, including bandwidth and buffer size, can overclock and degrade the performance of time-critical applications. Active Queue Management (AQM) is a technique for managing the queue of packets waiting to be transmitted and has been shown to improve network performance by reducing congestion and queuing delays. In this paper, we present an AQM algorithm that uses deep learning to predict the waiting delay for each packet in the queue based on the request type and other relevant factors. AQM Should be able to reduce latency and end-to-end user and avoid lock out phenomena with in the internet through AQM in rural area. The proposed algorithm prioritizes time-critical packets, such as real-time video streaming programs, to ensure they are prioritized in the queue. We evaluate the effectiveness of the proposed algorithm using simulations and compare its performance with other AQM algorithms. Overall, the proposed algorithm has the potential to improve the performance and reliability of time-critical applications in RANs, which has a significant impact on the quality of life and economic development in rural areas.