Dynamic Event-Triggered Set-Membership Estimation for Wireless Sensor Networks over Vessel Navigation with Privacy-Preservation

This paper mainly investigates privacy-preserving wireless sensor networks (WSNs) applied to vessel navigation, which are based on the speed and resistance information measured by the vessels sensors. In the application of vessel navigation, data transmission between sensors is vulnerable to attacks. To address information leakage caused by attacks during information transmission, privacy preservation is applied to the system states to protect privacy and security. To enhance resource utilization, a set-membership estimator is developed based on the system's dynamics. This estimator analyzes perceived and measured data from sensors to determine the actual data values. In the next step, the privacy of the distributed system is thoroughly proven to ensure the protection of state information. On this basis, the set-membership estimation involving the differential privacy approach is probed into developing recursive convex optimization principle. Subsequently, the steady-state performance of steady state is analyzed for the distributed transmission constrained system. In contrast to existing results, the proposed framework explicitly integrates differential privacy and measurement uncertainty into a unified optimization problem. This integration achieves a rigorous balance between security and estimation robustness. Finally, a practical example from vessel navigation is used to demonstrate the effectiveness and feasibility of the proposed methods.