Dynamic sensor transmission power scheduling for remote state estimation

Automatica ◽  
2014 ◽  
Vol 50 (4) ◽  
pp. 1235-1242 ◽  
Author(s):  
Zhu Ren ◽  
Peng Cheng ◽  
Jiming Chen ◽  
Ling Shi ◽  
Huanshui Zhang
Keyword(s):  
State Estimation ◽  
Transmission Power ◽  
Power Scheduling ◽  
Remote State Estimation

scholarly journals Transmission Power Scheduling for Energy Harvesting Sensor in Remote State Estimation

2014 ◽  
Vol 47 (3) ◽  
pp. 122-127 ◽  
Author(s):  
Yuzhe Li ◽  
Daniel E. Quevedo ◽  
Vincent Lau ◽  
Subhrakanti Dey ◽  
Ling Shi
Keyword(s):  
Energy Harvesting ◽  
State Estimation ◽  
Transmission Power ◽  
Power Scheduling ◽  
Remote State Estimation

scholarly journals Sensor transmission power control in remote state estimation over a fading communication channel

2017 ◽  
Vol 40 (9) ◽  
pp. 2813-2820
Author(s):  
Heng Zhang ◽  
Wei Xing Zheng
Keyword(s):  
Power Control ◽  
State Estimation ◽  
Control Strategy ◽  
Optimal Solution ◽  
Weighted Average ◽  
Transmission Power ◽  
Estimation Errors ◽  
Approximation Solution ◽  
Remote Estimation ◽  
Remote State Estimation

This paper investigates the problem of sensor power control for the scenario of remote state estimation. Most existing works mainly focus on designing sensor power scheduling schemes to minimize average estimation errors or terminal estimation errors when the sensor’s transmission capability is restrained by the energy budget. By contrast with these objectives, we aim to balance the cost of sensor power and the quality of remote estimation in this work. Specifically, we are interested in the problem that minimizes the expected weighted average sum of the remote state estimation errors and the sensor’s transmission power costs in an infinite time horizon. A Markov decision process framework is adopted to present the structure of the optimal power control strategy. However, it is not possible to find an analytical expression of the optimal solution. Thus, we further present an approximation solution and then derive a suboptimal sensor power control strategy. Finally, a simulation example is provided to show the effectiveness of our designed sensor control strategy.

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

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1073
Author(s):  
Yufei Han ◽  
Mengqi Cui ◽  
Shaojun Liu
Keyword(s):  
State Estimation ◽  
Estimation Error ◽  
Relay Node ◽  
Estimation Accuracy ◽  
Average Error ◽  
Relay Nodes ◽  
Trade Off ◽  
Error Covariance ◽  
Power Scheduling ◽  
Remote State Estimation

We study the sensor and relay nodes’ power scheduling problem for the remote state estimation in a Wireless Sensor Network (WSN) with relay nodes over a finite period of time given limited communication energy. We also explain why the optimal infinite time and energy case does not exist. Previous work applied a predefined threshold for the error covariance gap of two contiguous nodes in the WSN to adjust the trade-off between energy consumption and estimation accuracy. However, instead of adjusting the trade-off, we employ an algorithm to find the optimal sensor and relay nodes’ scheduling strategy that achieves the smallest estimation error within the given energy limit under our model assumptions. Our core idea is to unify the sensor-to-relay-node way of error covariance update with the relay-node-to-relay-node way by converting the former way of the update into the latter, which enables us to compare the average error covariances of different scheduling sequences with analytical methods and thus finding the strategy with the minimal estimation error. Examples are utilized to demonstrate the feasibility of converting. Meanwhile, we prove the optimality of our scheduling algorithm. Finally, we use MATLAB to run our algorithm and compute the average estimation error covariance of the optimal strategy. By comparing the average error covariance of our strategy with other strategies, we find that the performance of our strategy is better than the others in the simulation.

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