Optimal Sensor and Relay Nodes Power Scheduling for Remote State Estimation with Energy Constraint

Area of Science:

• Wireless Sensor Networks (WSNs)
• Remote State Estimation
• Power Management

Background:

• Existing methods for remote state estimation in WSNs often use predefined thresholds to balance energy consumption and estimation accuracy.
• The optimal infinite time and energy case for sensor and relay node power scheduling is not achievable.
• Previous approaches focused on adjusting trade-offs rather than finding optimal scheduling strategies.

Purpose of the Study:

• To develop an optimal sensor and relay node power scheduling algorithm for wireless sensor networks (WSNs) over a finite time period with limited energy.
• To achieve the minimum estimation error within a given energy budget.
• To address the limitations of previous threshold-based methods.

Main Methods:

• Developed a novel algorithm to find the optimal sensor and relay node scheduling strategy.
• Unified sensor-to-relay and relay-to-relay error covariance updates by converting the former into the latter.
• Enabled analytical comparison of average error covariances for different scheduling sequences using MATLAB simulations.

Main Results:

• The proposed algorithm successfully identifies the optimal scheduling strategy for minimal estimation error under energy constraints.
• Demonstrated the feasibility of unifying error covariance update methods.
• Simulation results show the developed strategy significantly outperforms other existing methods in terms of average estimation error covariance.

Conclusions:

• The developed scheduling algorithm is proven to be optimal for remote state estimation in WSNs with power constraints.
• The unification of error covariance updates provides a robust analytical framework for comparing scheduling strategies.
• The proposed approach offers superior performance compared to existing strategies for energy-limited WSNs.