Enhancing Congestion Control to Improve User Experience in IoT Using LSTM Network

TL;DR

This paper proposes a novel IoT congestion control strategy using LSTM networks to predict network traffic patterns, aiming to enhance user experience through improved connectivity and reliability.

Contribution

It introduces an IoT-specific LSTM-based congestion control method that leverages temporal data analysis for better network management.

Findings

Improved throughput and reduced latency.

Enhanced packet loss management.

Higher user satisfaction scores.

Abstract

This study suggests a new strategy for improving congestion control by deploying Long Short-Term Memory (LSTM) networks. LSTMs are recurrent neural networks (RNN), that excel at capturing temporal relationships and patterns in data. IoT-specific data such as network traffic patterns, device interactions, and congestion occurrences are gathered and analyzed. The gathered data is used to create and train an LSTM network architecture specific to the IoT environment. Then, the LSTM model's predictive skills are incorporated into the congestion control methods. This work intends to optimize congestion management methods using LSTM networks, which results in increased user satisfaction and dependable IoT connectivity. Utilizing metrics like throughput, latency, packet loss, and user satisfaction, the success of the suggested strategy is evaluated. Evaluation of performance includes rigorous testing and comparison to conventional congestion control methods.