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

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.