Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model
Abstract
:1. Introduction
1.1. Contributions and Paper Outline
- Utilizing the spatio-temporal correlation of sensor nodes, a SSIM is proposed to quantify the valuable information of sensor data, which decays with space and time.
- A single-step optimal decision-making mechanism is proposed. The possible scheduling results under different node layouts are analyzed, and a method to solve the boundary node distribution among various scheduling situations is provided.
- A long-term optimal decision-making mechanism is proposed, which is modeled as a Markov decision process, and the Q-learning algorithm is utilized to solve the optimal scheduling results.
- With a single-step mechanism, the approximate bounds for the node layout between partial scheduling results are obtained from the theoretical analysis and numerical calculation, which match with the simulation results. The optimal scheduling results with a long-term mechanism corresponding to different node layouts are obtained. Finally, the different performances of the two mechanisms are experimentally verified, and the advantages and limitations of each are summarized.
1.2. Related Work
2. System Model and Problem Formulation
2.1. System Model
2.2. Spatio-Temporal Scope Information Model
2.3. Problem Formulation
3. Single-Step Optimal Mechanism
3.1. Three-Node Isosceles Triangle Layout
3.2. Three-Node General Triangular Layout
4. Long-Term Optimal Mechanism
4.1. States, Actions, and Rewards
4.2. Q Learning Algorithm
5. Numerical and Simulation Results
5.1. Scheduling Results with Single-Step Optimal Mechanism
5.1.1. Three-Node Isosceles Triangle Layout
5.1.2. Three-Node General Triangular Layout
5.2. Scheduling Results with Long-Term Optimal Mechanism
5.3. Performance Comparison of Two Mechanisms
5.4. Complexity Analysis
6. Experimental Evaluation
6.1. Data Analysis
6.2. Performance Comparison
6.3. Summary and Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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References | Information Source | Sensors | Metrics | Research Results |
---|---|---|---|---|
[18] | Single-point | Two | Estimation Error | Optimal time shift between two sensors |
[19,20] | Multi-point | Multiple | Estimation Error | DRL-based scheduling mechanism |
[22,23] | Single-point (Noisy Ornstein-Uhlenbeck process) | Single | Mutual information | Closed-form VoI expressions |
[24,25] | Regional | Multiple | Error-tolerable sensing (ETS) coverage | AoI violation probability, Optimal sensors’ transmission power |
[26,27] | Time-varying Gauss–Markov Random Field (GMRF) | Multiple | Mean squared Estimation error | Closed-form expressions for estimation error, optimal spatial-temporal Sampling rates |
[28,29] | N Gaussian process | Multiple | Mean squared error (MSE) | Optimal scheduling policy |
[30] | Single-point | Multiple | Spatial-temporal mutual information | Optimal update interval |
[31] | Multi-point | Multiple | Overall utility of information update | Status update node set |
This work | Regional | Three | Spatial-temporal Scope information | Node activation strategy in any layout |
Typical Layout | Scheduling Results | Conditions |
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Boundary | ||
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Boundary | ||
Boundary | ||
Typical Layout | Scheduling Results | Conditions |
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Boundary A | ||
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Boundary B | ||
Boundary C | ||
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Liu, Y.; Dong, C.; Qin, X.; Xu, X. Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model. Sensors 2023, 23, 5437. https://doi.org/10.3390/s23125437
Liu Y, Dong C, Qin X, Xu X. Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model. Sensors. 2023; 23(12):5437. https://doi.org/10.3390/s23125437
Chicago/Turabian StyleLiu, Yang, Chen Dong, Xiaoqi Qin, and Xiaodong Xu. 2023. "Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model" Sensors 23, no. 12: 5437. https://doi.org/10.3390/s23125437