Adaptive Dynamic Average Consensus Scheme with Preserving Privacy and against False Data Injection Attacks: Dynamic Event-triggered Mechanism

It is a critical issue for achieving dynamic average consensus (DAC) in the presence of privacy eavesdroppers and false data injection (FDI) attacks, and this scenario is applicable to intelligent transportation systems. A dynamic event-triggered privacy preserving DAC (DET-PPDAC) control scheme is proposed. Firstly, in a privacy-sensitive scene, different time-varying terms are added to communication states by hiding real information from eavesdroppers. An observer and a compensator are designed to construct a control scheme for compensating for the impact of FDI attacks over a channel between a control signal and an actuator. Adaptive auxiliary variables are introduced for compensating for residual errors owing to asymmetric encryption/decryption functions. Dynamic event-triggered conditions are constructed to reduce the number of data as well as the risk of leakage by eavesdroppers, and continuous monitor from neighbors is removed. Our DET-PPDAC control scheme can also be applied to a directed graph. Stability analysis shows that the control scheme finally achieves DAC with bounded errors and Zeno-free behaviors while satisfying the requirement of privacy preservation. Simulation examples with formation of vehicles are given to demonstrate the effectiveness of the proposed control scheme.