Handling congestion control for IoT application

Abstract

The Internet of Things (IoT) is a networked system of interconnected computing devices, mechanical and digital devices, and things with unique identities that may interact without human involvement. The CoAP Simple Congestion Control/Advanced (CoCoA+) protocol for Constrained Application Protocol (CoAP) has been defined by the Internet Engineering Task Force (IETF). In many instances, CoCoA+ developed from LinuxRTO outperforms basic CoAP. In light traffic and bursty traffic environments, CoAP performs much worse. The CoAP standard includes a congestion control mechanism with exponential backoff. This backoff method may be insufficient for some IoT applications. Each IoT application would have unique properties. One of the most crucial problems in IoT networks is congestion control. Furthermore, events such as unwanted retransmissions and packet collisions induced by high-loss networks and packet transmission failures can cause congestion in the network. Various congestion-handling mechanisms for CoAP have been designed to improve the performance of IoT applications. newlineTwo techniques for dealing with congestion in CoAP networks are proposed and designed. The Adaptive Congestion Control Algorithm is the name of our initial protocol, an adaptive congestion control protocol for CoAP evolved from CoCoA+ (AdCoCoA). Instead of utilizing fixed values, the suggested approach estimates retransmission timeout (RTO) using dynamic parameters. This dynamic adaptation aids in the improvement of CoAP performance and the reduction of unwanted retransmissions. AdCoCoA has lower RTO values, a lower retransmission index (11.6%), greater throughput (71.3%), a higher packet delivery ratio (36.4%), and a higher packet sending rate (55.6%) than CoCoA+. newlineAdaptive Congestion Control with Dynamic Backoff is our second key approach (ACCB). ACCB is a refinement of our previous mechanism AdCoCoA. Instead of fixed values, the suggested method estimates round-trip-time (RTT), RTT variance (RTTVAR), and RTO using dynamic factors.