Secure Fusion Estimation of Energy-Constrained Multisensor System Against Hybrid Attacks.

This article presents a comprehensive theoretical framework for addressing the problem of secure fusion estimation in energy-constrained multisensor systems, specifically targeting hybrid attacks in multiple transmission levels. The lifespan of sensor nodes is constrained by the availability of energy supply, and all the sensors have the flexibility to choose between high-energy or low-energy levels for transmitting their measurements. Sensor data becomes vulnerable to malicious tampering when operating in a low-energy level, whereas the high-energy transmission level enables accurate data transmission. By introducing a set of Bernoulli random variables and ternary random variables, a novel measurement model is proposed to characterize scenarios involving both dual-energy transmission modes and hybrid attacks, including three statuses: safe, deception attacks, and Denial-of-Service attacks. Based on the innovation analysis approach, local secure estimators are designed to ensure that the estimation errors are minimized locally. Then, an optimal secure fusion algorithm is provided to generate the final estimated value by fusing all the local estimates. Additionally, the proposed secure fusion estimation algorithm's stability and steady-state properties are investigated. Finally, two simulation cases are conducted to provide the empirical evidence of the superior performance of the proposed approach.