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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model.

Yang Liu¹, Chen Dong¹, Xiaoqi Qin¹

¹State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)

|July 8, 2023

Summary

This summary is machine-generated.

This study introduces a Spatio-temporal Scope Information Model (SSIM) to optimize sensor activation scheduling in the Internet of Things (IoT). The model quantifies valuable information decay, enhancing regional sensing accuracy and efficient data acquisition.

Keywords:

internet of things sensor scheduling spatio-temporal correlation spatio-temporal scope information model

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Area of Science:

Internet of Things (IoT)
Sensor Networks
Information Theory

Background:

Sensor data value diminishes over space and time.
Efficient sensor activation is crucial for accurate regional monitoring.
Existing models may not fully capture spatio-temporal information decay.

Purpose of the Study:

To propose a Spatio-temporal Scope Information Model (SSIM) for quantifying sensor data value.
To develop efficient sensor activation scheduling mechanisms for IoT systems.
To enhance regional sensing accuracy and maximize valuable information acquisition.

Main Methods:

Developed the Spatio-temporal Scope Information Model (SSIM) based on information entropy and spatio-temporal correlation.
Proposed a single-step scheduling decision mechanism for immediate optimization.
Modeled long-term scheduling as a Markov decision process using Q-learning.

Main Results:

Theoretical analysis yielded scheduling results and bounds for node layouts, consistent with simulations.
Experimental validation using a relative humidity dataset confirmed the performance of both single-step and long-term mechanisms.
Identified performance differences and limitations of the proposed SSIM and scheduling approaches.

Conclusions:

The SSIM effectively quantifies valuable sensor information, guiding efficient scheduling.
Both single-step and long-term scheduling mechanisms demonstrate practical utility in IoT monitoring.
Further research is needed to address model limitations and explore advanced applications.