Testbed: Q-SMART

Q-SMART: Quantum Secure Data Exchange for Resilient Smart Home Cognitive Networks

Advanced cognitive home automation platform using digital twins and multi-agentic AI for energy optimization and air quality management.

The Q-SMART testbed represents a transformative leap in intelligent home automation, validating a cognitive, self-learning platform that advances how residential systems optimize energy consumption and indoor air quality. This initi multi-agent AI frameworks to create personalized cognitive hubs that learn, predict, and autonomously control home environments, achieving up to a ative demonstrates the integration of digital twin technology with multi-agentic AI frameworks to create personalized cognitive hubs that learn, predict, and autonomously control home environments while achieving up to 25% reduction in energy consumption.

Built on decentralized open-source components, the Q-SMART platform leverages wireless mesh networks and dynamic live 3D models to create comprehensive digital twins of residential environments. The system employs multi-agentic AI frameworks that continuously analyze occupancy patterns, environmental conditions, and energy usage to optimize HVAC and ventilation systems in real-time. Extended Reality (XR) interfaces provide intuitive visualization and control capabilities, enabling residents to interact naturally with their intelligent home ecosystem.

Expected outcomes include validated frameworks for cognitive home automation, established benchmarks for energy optimization in residential digital twin applications, and demonstrated integration methodologies for multi-agentic AI systems in edge computing environments. The testbed will quantify improvements in energy efficiency, indoor air quality metrics, and occupant comfort levels while providing industry-standard validation for decentralized intelligent home architectures.

The Q-SMART testbed contributes to industry advancement in the following ways:

  1. Cognitive Architecture Standards: Establishing frameworks for self-learning residential automation systems that adapt to occupant behavior patterns and environmental conditions.
  2. Edge-Native AI Validation: Demonstrating secure, privacy-preserving AI processing capabilities within home environments without cloud dependency.
  3. Digital Twin Integration: Advancing methodologies for real-time 3D modeling and simulation of residential energy and air quality systems.
  4. Quantum-Safe Protocols: Validating implementation of post-quantum cryptographic security measures for future-proof smart home communications.
  5. Multi-Agentic Frameworks: Developing industry standards for coordinated AI agent collaboration in residential automation and optimization.
  6. Energy Optimization Benchmarks: Creating performance metrics and validation methods for AI-driven residential energy management systems

Lead Developer:

WINNIO