Constrained Adaptive Exponential Backoff: An Algorithm for Effective RTO Estimation in CoAP

The Constrained Application Protocol (CoAP) has become a widely adopted communication standard for the Internet of Things (IoT) and wireless sensor networks (WSNs). However, its default congestion control mechanism, based on binary exponential backoff (BEB), lacks adaptability to dynamic network conditions. This limitation often results in excessive retransmissions, increased latency, and inefficient energy consumption, particularly in resource-constrained environments. To address these challenges, this study proposes a novel Constrained Adaptive Exponential Backoff (CAEB) algorithm designed to enhance retransmission timeout (RTO) estimation through an adaptive, lightweight approach. CAEB integrates a logarithmic adjustment mechanism and weighting factors based on retransmission count and active node density, enabling real-time adaptability while maintaining computational simplicity. The proposed algorithm was implemented and evaluated in the Cooja simulator using the Contiki operating system under both continuous and periodic traffic scenarios. The experimental results demonstrated that CAEB consistently achieved lower flow completion time, higher throughput, and reduced packet loss and retransmissions compared with BEB, with these improvements confirmed as statistically significant based on the two-sample t-tests (p < 0.05) and the Holm–Bonferroni correction method. These findings highlight the effectiveness of CAEB in mitigating congestion and improving reliability in constrained IoT networks. The proposed algorithm not only advances methodological approaches for RTO estimation but also offers practical implications for energy-efficient and scalable IoT communication systems, particularly in applications such as smart agriculture, environmental monitoring, and structural health monitoring, where timely and reliable data delivery is critical.

Adaptive backoff algorithm; Constrained adaptive exponential backoff; Constrained application protocol; Congestion control; RTO estimation

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