Predictive fault management in smart sensor networks using a dynamic quantum-AI architecture (DynaQuAI)
Abstract
Applications in industrial and smart-infrastructure Wireless sensor networks (WSNs) in the field are increasingly expected to employ predictive intelligence, which, even when operating under dynamic configurations, heterogeneity of hardware, and strong privacy requirements. The proposed paper is DynaQuAI, quantum-inspired, edge-intelligent framework that can predict real-time failures on constrained sensor nodes regarding their resources. The proposed system employs a probabilistic state encoding and oscillatory exploration schedule-mathematically inspired by quantum superposition analogies-to improve reinforcement learning exploration in sparse and noisy environments. These techniques are entirely classical algorithms implemented with standard trigonometric and probabilistic operations, requiring no quantum hardware. Such quantum-inspired algorithms enhance coverage of state-space and convergence faster than when using the usual deep RL models, with no computational demand. To support distributed nodes collaborative learning, DynaQuAI uses a lightweight federated approach on training paired with parameter aggregation through secure masks. Privacy is ensured through secure aggregation via pairwise parameter masking, which prevents the aggregation server from observing individual node updates while recovering the correct aggregate. The framework is additionally compatible with local differential privacy (ε_total ≤ 9.8 over 150 rounds at δ = 4 × 10⁻⁶) for deployments requiring…
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