Predictive fault management in smart sensor networks using a dynamic quantum-AI architecture (DynaQuAI).

Area of Science:

• Computer Science
• Artificial Intelligence
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) require predictive intelligence for industrial and smart infrastructure applications.
• Existing WSNs face challenges with dynamic configurations, hardware heterogeneity, and strict privacy needs.
• Real-time failure prediction is crucial for maintaining the reliability of constrained sensor nodes.

Purpose of the Study:

• To introduce DynaQuAI, a quantum-inspired, edge-intelligent framework for real-time failure prediction in WSNs.
• To enhance reinforcement learning exploration in sparse and noisy WSN environments using classical, quantum-inspired algorithms.
• To ensure privacy in collaborative WSN learning through federated approaches and secure aggregation.

Main Methods:

• Utilized probabilistic state encoding and oscillatory exploration schedules inspired by quantum mechanics.
• Implemented a lightweight federated learning approach with secure parameter aggregation via pairwise masking.
• Incorporated local differential privacy for formal statistical privacy guarantees.
• Evaluated DynaQuAI on a simulated testbed with 500 heterogeneous sensor nodes and realistic fault injection scenarios.

Main Results:

• Achieved 33% higher fault-prediction accuracy compared to federated deep-learning baselines.
• Demonstrated 25% reduced energy consumption and 40% faster convergence during policy learning.
• Maintained real-time inference latency of 85 ms using minimal computational resources (5%).

Conclusions:

• DynaQuAI provides a robust, privacy-conscious, and extendable learning framework for next-generation IoT ecosystems.
• The quantum-inspired techniques significantly improve WSN failure prediction and learning efficiency.
• The framework is suitable for long-term deployment in resource-constrained and privacy-sensitive WSN applications.