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Internet of Things: The Optimal Generation Rates under Preemption Strategy in a Multi-Source Queuing System.
Tianci Zhang1, Shutong Chen1, Zhengchuan Chen1,2
1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Entropy (Basel, Switzerland)
|August 27, 2021
Summary
Optimizing information freshness in the Internet of Things (IoT) is crucial. This study introduces a multi-source preemptive queuing model, finding global preemption superior to self-preemption for timely data delivery.
Area of Science:
- Computer Science
- Information Theory
- Network Engineering
Background:
- The Internet of Things (IoT) generates vast data, necessitating timely information for strategic decisions.
- Information freshness is critical for effective IoT system performance.
- Preemption strategies significantly enhance data timeliness in queuing systems.
Purpose of the Study:
- To investigate optimal generation rate control in a multi-source preemptive queuing model for enhanced information freshness.
- To compare the effectiveness of self-preemption and global-preemption strategies.
- To introduce a weighted average age of information (AoI) metric for system-wide freshness.
Main Methods:
- Developed a multi-source preemptive queuing model.
- Analyzed self-preemption and global-preemption strategies.
- Formulated and solved optimization problems for generation rate allocation.
- Derived closed-form solutions and approximate solutions for various load conditions.
Main Results:
- Proved the self-preemption rate allocation problem is convex and provided an efficient algorithm.
- Derived a closed-form approximate optimal solution for self-preemption under light loads.
- Derived a closed-form optimal solution for the global-preemption strategy.
- Demonstrated global preemption yields better overall timeliness than self-preemption.
Conclusions:
- Global preemption strategies offer superior information freshness compared to self-preemption in multi-source IoT systems.
- The proposed weighted average AoI effectively quantifies system-wide information timeliness.
- The derived solutions, including approximations, are accurate and applicable across different load scenarios.
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