Fuzzy based Congestion Control and Congestion Aware Routing Technique for IoT Networks

Abstract

Introduction: In Internet of Things (IoT) networks, the existing congestion control mechanisms did not often scale efficiently or maintain the heterogeneity of devices, causing performance bottlenecks and uneven network performance. Existing congestion control protocols may not adjust well to these dynamics, causing increased packet loss, suboptimal performance, and higher latency. 

Objectives: The main objectives of this work are to detect congestion at node level or link level and to determine congestion aware routing paths. 

Methods: A Fuzzy-based congestion control and congestion aware routing (FCC-CAR) technique for IoT networks is proposed. In this technique, Q-learning is applied at each node to select the parent node based on the link quality, node degree and hop distance metrics. Then, congestion at any intermediate node is detected by means of congestion degree, packet processing delay and packet loss, by applying Fuzzy logic decision model. Depending on the detected congestion status, appropriate congestion control mechanisms are applied. 

Results: Experimental results have shown that FCC-CAR technique minimizes the packet drop and delay while increasing the throughput.

Conclusion: Thus the proposed technique performs congestion control by means of congestion detection and congestion aware routing.