Distributed State Estimation Over Wireless Sensor Networks With Energy Harvesting Sensors

Area of Science:

• Electrical Engineering
• Computer Science
• Control Systems

Background:

• Wireless Sensor Networks (WSNs) face challenges with intermittent data transmission due to energy harvesting limitations.
• Sensor node energy levels directly impact the reliability of internode communication.

Purpose of the Study:

• To develop a novel distributed state estimator for WSNs with probabilistic energy harvesting.
• To analyze the impact of intermittent communication on state estimation accuracy.
• To derive optimal estimator gains and analyze convergence properties.

Main Methods:

• A novel distributed state estimator using indicator functions for intermittent communication.
• Eigenvalue analysis and mathematical induction to study energy level distribution evolution.
• Minimization of estimation error covariance trace for optimal gain derivation.

Main Results:

• A theoretical framework for distributed state estimation in energy-constrained WSNs.
• Analysis of energy level probability distribution and its steady-state behavior.
• Derivation of optimal estimator gains and proof of convergence for estimation error bounds.

Conclusions:

• The proposed distributed state estimator effectively handles intermittent communication in WSNs.
• The theoretical results provide a foundation for designing robust state estimation algorithms in energy-harvesting WSNs.
• The approach is validated through a target tracking example.