A highly effective algorithm for mitigating and identifying congestion through continuous monitoring of IoT networks, improving energy consumption

Abstract

The Internet of Things (IoT) consists of non-standardized computer devices that can create wireless network connections to send data. These devices have limited storage, bandwidth, and computing capacities, which may cause network congestion when nodes move or leave their allotted area. IoT networks need congestion control to enhance the efficiency of data transfer. The study examines IoT congestion and proposes using alternate nodes to maintain dataflow and quality of service (QoS). The study presents RAoNC, a novel algorithm designed to improve routing algorithms in network clusters for the purposes of congestion monitoring, avoidance, and mitigation. Congestion management techniques efficiently process network information update query packets and reduce large-header handshaking packets. Improve network performance by reducing congestion, packet loss, and throughput. The proposed method speeds up packet transfer to reduce network node packet transmission delays. The optimization approach minimizes power usage across all network nodes. We assessed the efficacy of our approach by comparative analysis utilizing NS2 simulations and contrasted the suggested algorithm with prior studies. The simulation shows that RAoNC significantly improves congestion performance. We will assess the novel RAoNC algorithm in relation to DCCC6, LEACH, and QU-RPL. The throughput increased by 28.36%, weighted fairness index by 28.2%, end-to-end delay by 48.7%, energy consumption by 31.97%, and the number of missed packets in the buffer decreased by 90.35%.