RL-ACK: A Reinforcement Learning-Based Adaptive ACK Framework for Heterogeneous Smart Kitchen IoT Networks

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 ( $\rho = 0.9$ ).