A quantum-resistant consensus framework for decentralized anomaly detection in wireless sensor networks

Abstract

Wireless Sensor Networks (WSNs) are vulnerable to malicious nodes and sensor node failures, which compromise data integrity and network reliability. These threats result in incorrect decisions and reduce system trust. To address this, machine learning algorithms enable anomaly detection by identifying abnormal sensor nodes, while blockchain ensures secure and tamper-proof data storage. However, reliable consensus is essential before data validation in the blockchain. A hybrid framework combining ML, blockchain, and a modified HotStuff consensus algorithm with post-quantum cryptographic systems provides secure, fault-tolerant, and quantum-resistant consensus, ensuring trustworthy and resilient WSN operations.