Adaptive acknowledgment control in ultra-dense LoRaWAN using lightweight machine learning

Ultra-dense Low-Power Wide-Area Networks (LPWANs) face critical challenges in maintaining reliable and scalable communication due to increased contention, stringent duty-cycle regulations and energy constraints. These limitations are particularly pronounced in LoRaWAN deployments where thousands of end-devices compete for severely constrained downlink capacity. This paper addresses the pressing issue of sustaining efficient bidirectional communication in such environments by introducing a novel, context-aware acknowledgment control mechanism. Our approach replaces the conventional static confirmed mode with a lightweight, online logistic regression model embedded at the gateway enabling real-time, probabilistic ACK decisions informed by dynamic network conditions. Extensive MATLAB-based simulations involving up to 3000 devices show that the proposed strategy increases uplink delivery rates by over 50 % at scale (compared to 32 % with the standard approach), maintains downlink responsiveness above 15 % and reduces energy consumption by up to 15 % in sparse and 8–10 % in ultra-dense deployments. These results demonstrate the feasibility and effectiveness of integrating lightweight machine learning at the MAC layer as a protocol-compliant solution to improve the scalability, efficiency and resilience of next-generation LoRaWAN systems.