Average Delay-based early Congestion Detection in Named Data of Health Things

Abstract

The Internet of Health Things (IoHT) is receiving more attention from researchers because of its wide use in the healthcare field. IoHT refers to medical devices whose main purpose is to transmit health data in a secure and lossless manner between them and healthcare personnel. However, in a medical emergency, sensors transmit vital patient data simultaneously and frequently, increasing the risk of congestion and packet loss. This problem is highly undesirable in an IoHT system, leading to undesirable results. To address this issue, a new approach based on Named Data Networking (NDN) (which is considered as the most appropriate internet architecture for IoT systems) is proposed to control congestion in IoHT systems. The proposed approach, Average delay-based early congestion Detection (ADCD), detects and controls congestion at consumer nodes by calculating the average queuing delay based on the one-way delay similar to that proposed in Sync-TCP. Then according to the calculated value, ADCD divides the network into three states: no-congested state, less congested state, and heavily congested state. The adjustment of the congestion window size is done according to the state of the network. ADCD was implemented in ndnSIM and compared to the Interest Control Protocol ICP. The results show that ADCD maximizes bandwidth utilization compared to ICP and maintains a reasonable delay.