Metrics that impact on Congestion Control at Internet Of Things Environment

Abstract

Congestion is very common in wireless networks as multiple sensors try to transmit data simultaneously. The Internet Of Things (IOT) is a dynamic system. Using a specific congestion control algorithm with one IOT system provides different results from other IOT systems. This means that an IOT developer cannot use the same congestion control algorithm with different IOT systems, because the efficiency of congestion control algorithms varies from one IOT system to another according to the infrastructure of the smart system and the amount of transmitted data. The primary purpose of this work is to support analysts and designers of congestion control algorithms at IOT companies by understanding the metrics influencing congestion control. This will enable them to select the appropriate metrics depending on the nature of the IOT infrastructure and the amount of transmitted data. This study also conducts a literature review of papers that discuss transport protocols providing congestion control. The data extraction process gathered from 30 transport protocols concerning congestion control. From the reviewed papers, we extract the metrics that influence congestion control detection, notification, and mitigation. After that, we applied some statistical solutions on extracted metrics. We find queue length is the metrics used most often for congestion detection. While additive increase multiplicative decrease (AIMD) for single-bit transmitting is the most used metrics for congestion notication. Whereas rate control is the most used metrics for congestion mitigation