A simulation-based project exploring adaptive IoT security using quantum key distribution and entropy-driven key refresh.
Modern IoT systems move massive amounts of sensitive data, yet most still rely on long-lived cryptographic keys that quietly weaken over time. This project tackles that gap by integrating quantum key distribution (BB84) with adaptive, entropy-driven security for IoT communication.
Instead of treating encryption as a static layer, this framework makes security self-adjusting, using quantum-generated keys that evolve based on real-time randomness and system behavior.
The following diagram illustrates the overall architecture of the Adaptive Quantum-IoT Encryption Framework, showing the interaction between IoT devices, QRNG-based key generation, adaptive entropy control, and cloud communication.
This graph shows the entropy values generated by the Quantum Random Number Generator (QRNG) over time.
The entropy remains close to the ideal value of 1 bit, validating the randomness and reliability of the quantum key source.
This plot highlights the time instances where encryption keys were refreshed dynamically.
Key refreshes are triggered whenever entropy drops below the defined security threshold, demonstrating adaptive security behavior.
This graph represents the communication latency observed during the encrypted IoT data transmission.
Despite adaptive key refresh operations, the system maintains low and stable latency, confirming efficiency.
Most IoT security solutions stop at strong encryption.
This work focuses on when encryption should change and why.
Key differentiators:
- Quantum-generated keys instead of pseudo-random sources
- Entropy-aware key rotation rather than fixed refresh cycles
- Designed with real IoT scalability and latency constraints in mind
The framework follows a layered, system-level architecture:
-
Quantum Layer
BB84 protocol simulation generates secure cryptographic keys. -
Entropy Intelligence Layer
Continuously evaluates randomness quality and triggers adaptive key rotation. -
IoT Communication Layer
Secure, low-latency data exchange across multiple IoT nodes. -
Edge / Cloud Monitoring Layer
Tracks performance, entropy stability, and communication latency.
Each layer is decoupled yet coordinated, enabling flexibility and scalability.
- Quantum Computing: Qiskit (BB84 protocol)
- Security Engineering: Entropy modeling, adaptive key management
- IoT Systems: Multi-node network simulation
- Programming: Python
- Performance Analysis: Latency and entropy profiling
- Entropy stability consistently between 0.998 – 1.000
- Secure communication latency maintained under 22 ms
- Scaled to a 200-node IoT network without entropy degradation
- Demonstrated resistance to key reuse and long-term predictability
- Security systems benefit from adaptability, not static assumptions
- Quantum principles can be meaningfully applied beyond theory
- System-level design is critical when working with constrained devices
- Hardware-based quantum key integration
- Deployment on edge-level IoT devices
- Hybridization with post-quantum cryptographic schemes
- Adaptive key refresh maintains consistently high entropy
- Security adaptation introduces minimal latency overhead
- Demonstrates feasibility of quantum-inspired IoT security
Varri Sneha
B.Tech in Electronics and Communication Engineering
IIIT Manipur
- Python 3.8+
- pip
git clone https://github.com/VarriSneha/adaptive-quantum-iot-encryption.git
cd adaptive-quantum-iot-encryption
pip install -r requirements.txt