A Privacy-Aware and Sustainable Joint Optimization for Resource-Constrained Internet of Things using Deep Reinforcement Learning

Abstract

The rise of battery-powered Internet of Thing (IoT) fleets in buildings and campuses requires policies that manage sensing, communication, and edge–cloud offloading while considering energy, carbon, privacy, and cost limits. In this paper, we frame this challenge as a Markov Decision Process (MDP) and design a controller using Deep Reinforcement Learning (DRL). We present a Rainbow-based IoT controller that retains distributional value learning, dueling networks, NoisyNets, n-step returns, Prioritized Experience Replay (PER), and double selection, and contributes four novelties: dual-budget Lagrangian control with warm-up, connectivity-robust distributional targets reweighted by outage/queue risk, federated sketch-guided replay for underrepresented regimes, and realistic ISAC-aware macro-actions with integrated DP/CO₂ accounting and budget-aware training/logging. Simulations show that the proposed algorithm achieves ≈88% higher anomaly detection, ≈39% higher packet success, ≈52% less energy consumption, and ≈74% lower cloud cost than the best baseline, demonstrating superior utility, reliability, and sustainability in IoT workloads.