Comprehensive review on congestion detection, alleviation, and control for IoT networks

Context: The Internet of Things (IoT) comprises various computing devices that operate on a non-standard platform and can connect to wireless networks to transmit data. These devices typically have limited storage capacity, restricted network bandwidth, and a lower level of computing power, which can cause congestion in the network. Hence, it is crucial to have a congestion control mechanism in place to facilitate efficient data transfer in IoT networks. Objective: To address congestion in the IoT, this research attempts to offer an overview of several congestion detections, avoidance, and control-based routing protocol techniques. Method: A systematic mapping study was carried out to pinpoint relevant literature. From this process, 102 publications were identified as the most relevant studies of congestion detection, congestion avoidance, congestion control, routing protocol, congestion control in 6LoWPAN, and learning-based congestion control. Results: Most relevant articles are clustered based on congestion detection (10%), congestion avoidance (12%), congestion control (23%), avoiding congestion through routing protocol (14%), congestion control in 6LoWPAN (19%), and controlling the congestion through learning based methods (24%). Conclusion: Congestion control is necessary for IoT to maintain network stability, reliability, and performance. It helps to ensure that critical applications can operate seamlessly and that IoT devices can communicate efficiently without overwhelming the network. Congestion control algorithms ensure that the network operates within its capacity, preventing network overload and maintaining network performance.