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This study suggests a new strategy for improving congestion control bydeploying Long Short-Term Memory (LSTM) networks. LSTMs are recurrent neuralnetworks (RNN), that excel at capturing temporal relationships and patterns indata. IoT-specific data such as network traffic patterns, device interactions,and congestion occurrences are gathered and analyzed. The gathered data is usedto create and train an LSTM network architecture specific to the IoTenvironment. Then, the LSTM model's predictive skills are incorporated into thecongestion control methods. This work intends to optimize congestion managementmethods using LSTM networks, which results in increased user satisfaction anddependable IoT connectivity. Utilizing metrics like throughput, latency, packetloss, and user satisfaction, the success of the suggested strategy isevaluated. Evaluation of performance includes rigorous testing and comparisonto conventional congestion control methods.

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