DIRED: Dual Indicator Random Early Detection for Congestion Control in WSN

Wireless Sensor Networks (WSNs) find applications in diverse fields such as environmental monitoring, healthcare, and military surveillance. Nonetheless, one of the primary challenges encountered by WSNs is congestion. Congestion arises in WSNs when there is a high volume of traffic on the network, leading to significant repercussions. These repercussions encompass packet loss, heightened latency durations, and diminished network efficiency. This paper presents an innovative congestion control mechanism named Dual Indicator Random Early Detection (DIRED). DIRED leverages two indicators, namely queue length and packet loss rate, to dynamically adjust the dropping probability of packets, thereby mitigating congestion and enhancing network performance. For the successful execution of the DIRED model, a congestion control algorithm is introduced. This strategy efficiently prevents the deterioration of network performance that can commonly arise from extremely low packet drop probabilities. Simulations are conducted to evaluate the proposed DIRED model, comparing it with the KACO and KFOA techniques. The results demonstrate that DIRED outperforms KACO and KFOA in terms of network performance, achieving a more optimal balance between performance metrics and packet-dropping probability. Wireless Sensor Networks (WSNs) are used in fields like environmental monitoring, healthcare, and military surveillance, but they often face congestion issues. Congestion in WSNs leads to packet loss, increased latency, and reduced efficiency. This paper introduces Dual Indicator Random Early Detection (DIRED), a congestion control mechanism using queue length and packet loss rate to adjust packet dropping probability. DIRED enhances network performance by dynamically managing congestion, supported by an algorithm to maintain efficient network operation. image