Markov model-based congestion control for the internet of things

Internet of Things (IoT) devices use the lightweight RESTful Constrained Application Protocol (CoAP). Resource-constrained IoT networks need efficient congestion control (CC) for reliable communication. Radio channel capacity and device hardware limitations can cause congestion. CoAP, which operates on top of UDP, must independently manage CC. Simple CoAP rules handle congestion but do not react to changing network conditions. IoT applications demonstrate considerable resource limitations that present new issues for the design of CC techniques. Current CC mechanisms, such as Default CoAP and CC with advanced mechanisms (CoCoA), exhibit restricted adaptation to fluctuating traffic conditions. This research presents a three-state Markov model for CC that utilizes probabilistic state transitions to adapt dynamically to network conditions and manage varying congestion levels effectively. The proposed Markov model is validated against Default CoAP and CoCoA using Contiki OS and the Cooja simulator. A comparative performance analysis of the Markov model has shown 199.237 kbps throughput, 22.174% packet loss, and a delay of 126.603 ms compared to the Default CoAP and CoCoA mechanisms at higher transmission intervals. These findings suggest that Markov-based adaptive techniques could improve CC in constrained networks, enabling more reliable IoT communications.