RL-ACK: A Reinforcement Learning-Based Adaptive ACK Framework for Heterogeneous Smart Kitchen IoT Networks
TL;DR
This paper proposes RL-ACK, a reinforcement learning–based adaptive acknowledgment framework implemented at the edge router that reduces latency, control-plane overhead, reliability violations, and utilization pressure, and maintains Tier 1 deadline-compliant delivery above 95% under high network utilization.
Abstract
Smart kitchen IoT environments integrate robotic manipulators, sensing modules, and intelligent appliances within dense wireless deployments. Such environments generate heterogeneous traffic including latency-critical control signals, high-bandwidth multimedia streams, and best-effort telemetry. Conventional MQTT deployments rely on static QoS-based acknowledgment (ACK) behaviors that cannot adapt to dynamic congestion and packet loss. This paper proposes RL-ACK, a reinforcement learning–based adaptive acknowledgment framework implemented at the edge router. Per-message ACK selection is formulated as a Markov Decision Process (MDP), and the ACK policy is learned using a Deep Q-Network (DQN). The reward function balances latency, control-plane overhead, reliability violations, and utilization pressure. Extensive NS-3 simulations and a real Wi-Fi testbed demonstrate up to 36.7% latency reduction, approximately 22% signaling overhead reduction, and ACK-induced energy reduction for Tier 3 (BET) devices compared with static MQTT QoS 1, while maintaining Tier 1 deadline-compliant delivery above 95% under high network utilization ( $\rho = 0.9$ ).