Distributed Energy-Based Estimation Over Harvesting-Constrained Sensor Networks.

Area of Science:

• Control Systems Engineering
• Networked Systems
• Signal Processing

Background:

• Sensor networks face challenges in distributed state and fault estimation due to energy constraints.
• Energy harvesting introduces variability in sensor operational capabilities and data transmission.
• Existing methods often struggle with the dynamic nature of energy availability in wireless sensor networks.

Purpose of the Study:

• To develop a distributed joint state and fault estimation strategy for nonlinear time-varying systems.
• To address the constraints imposed by energy harvesting on sensor data transmission.
• To design an estimator that utilizes local and neighbor data under energy limitations.

Main Methods:

• A distributed estimation framework utilizing local and neighbor sensor data.
• Modeling energy harvesting using a Poisson process and determining sensor transmission probabilities.
• Developing an energy-based filtering approach to minimize estimation error covariance.
• Analyzing the convergence performance of the proposed distributed estimator.

Main Results:

• The proposed estimator achieves simultaneous state and fault estimation in energy-constrained sensor networks.
• An upper bound for the estimation error covariance is derived and minimized.
• The estimator's convergence performance is theoretically analyzed.
• Validation through a practical example demonstrates the effectiveness of the proposed method.

Conclusions:

• The developed distributed estimation approach effectively handles energy harvesting constraints in nonlinear sensor networks.
• The method provides a robust framework for joint state and fault estimation.
• The findings offer practical implications for the design of resilient and autonomous sensor systems.