A privacy-aware and sustainable joint optimization for resource-constrained internet of things using deep reinforcement learning

초록

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/COQ accounting and budget-aware training/logging. Simulations show that the proposed algorithm achieves ti88 % higher anomaly detection, ti39 % higher packet success, ti52 % less energy consumption, and ti74 % lower cloud cost than the best baseline, demonstrating superior utility, reliability, and sustainability in IoT workloads.

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