Explainable and Energy-Aware Self-Adaptive IoT Architecture Using Deep Q-Learning

The dynamic and resource-constrained nature of Internet of Things (IoT) environments demands software systems that are both adaptive and transparent. Traditional static architectures fall short in addressing these challenges, particularly in scenarios requiring real-time decision-making and energy efficiency. This paper presents a modular and explainable self-adaptive software architecture that leverages Deep Q-Learning (DQN) for intelligent adaptation and integrates hybrid explainability mechanisms using SHAP and LIME. The proposed architecture supports runtime decision-making through a layered design, enabling context-aware sensing, battery-aware adaptation, and interpretable actuation. A custom reward function optimizes energy consumption, latency, and service quality, while explainability modules provide insight into both global learning behavior and individual runtime decisions. Experimental evaluation in a simulated smart home environment demonstrates the effectiveness and transparency of the system, highlighting its potential for deployment in constrained and safety-critical IoT settings.