Predictive RTO for CoAP using Lightweight Support Vector Regression in Internet of Things

Abstract

Internet of Things (IoT) networks require lightweight application layer messaging, and CoAP is an option because it supports REST-style interactions over UDP on constrained devices. However, CoAP congestion control still depends on fixed heuristics, including binary exponential backoff (BEB) and RTT-based mechanisms such as CoCoA and CoCoA+, which do not adapt well to dynamic and lossy wireless links. This paper proposes prCoAP, a lightweight data-driven approach that replaces heuristic Retransmission Timeout (RTO) selection with a per-attempt linear Support Vector Regression (SVR) ensemble for direct RTO prediction from node-observable features. The model runs on-device on low-end microcontrollers and operates within strict memory and energy budgets. The framework also includes a calibrated Random Forest drop classifier that identifies likely-to-fail transactions in later retransmission attempts and terminates them early to reduce channel occupancy. We evaluate the approach using a discrete-event simulator implementing IEEE 802.15.4 and RFC 7252 and validate it against the FIT IoT-LAB testbed. Our experiments confirm that the proposed linear SVR achieves 97.25% PDR, outperforming standard CoAP under the evaluated conditions. We also evaluate a kernel SVR variant; while it improves regression fit (R2 0.84 vs. 0.63), the linear SVR provides better system-level efficiency, achieving comparable PDR with lower energy overhead.