A Cluster-based Congestion Avoidance Technique for Data Aggregation for the IoT Network Using the Heuristic Technique

Abstract

Congestion issues have arisen due to the rise of Internet of Things (IoT) devices and increased data traffic within IoT networks. Congestion can result in decreased reliability, higher latency, and performance degradation due to the overwhelming amount of data generated by various IoT devices. To optimize data flow, reduce col- lisions, and improve overall network efficiency, this paper looks into the root causes of congestion. It proposes a new adaptive algorithm called Congestion Avoidance through Cluster Head (CA-CH). A thorough examination of the current mechanisms for controlling congestion is provided to combat it effectively. The research assesses distributed and centralized methods, considering how well they apply to dynamic and diverse IoT environments. The cluster head is an essential strategy for ensuring reliable communication and effective data traffic management. The traffic manage- ment in the Internet of Things network is efficiently managed in the article by the BUG and Bully algorithms. Utilizing the Contiki OS Cooja simulator, the simulation is conducted. According to the results of the simulation, the CA-CH performs better than the previous methods in terms of packet delivery ratio, average delay of (10.6 ms), throughput with different cluster heads (0.98), average energy consumption of (6.6 mJ/packet), packet sending rate, and overall throughput of (30%).