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Adaptive Context Caching for IoT-Based Applications: A Reinforcement Learning Approach
Shakthi Weerasinghe1, Arkady Zaslavsky1, Seng Wai Loke1
1School of Information Technology, Deakin University, Geelong, VIC 3145, Australia.
Sensors (Basel, Switzerland)
|July 11, 2023
Summary
Adaptive context caching (ACOCA) optimizes internet-of-things (IoT) applications by improving context-management platform (CMP) performance and cost efficiency. This novel mechanism enhances real-time context query responses, offering significant long-term gains.
Area of Science:
- Computer Science
- Data Management
- Artificial Intelligence
Background:
- Internet-of-Things (IoT) applications require extensive data handling for context awareness.
- Managing transient context data in caches is an under-explored research area.
- Context-management platforms (CMPs) face challenges in real-time performance and cost efficiency.
Purpose of the Study:
- To propose a novel Adaptive Context Caching (ACOCA) mechanism for CMPs.
- To maximize both cost and performance efficiency in near real-time context query responses.
- To address challenges in efficient context selection for caching and cost management.
Main Methods:
- Implementation of a scalable, selective context-caching agent using the Twin Delayed Deep Deterministic Policy Gradient (TD3) method.
- Incorporation of an adaptive context-refresh switching policy.
- Integration of a time-aware eviction policy and a latent caching decision management policy.
Main Results:
- ACOCA demonstrates significant long-term efficiencies for CMPs, surpassing previous studies.
- The mechanism justifies its added complexity through substantial cost and performance gains.
- Evaluated on a real-world parking traffic dataset, ACOCA outperformed traditional and context-aware caching policies by up to 84.7% in cost efficiency.
Conclusions:
- ACOCA offers a comprehensive solution for the entire context-management life cycle.
- The proposed mechanism significantly enhances CMP cost and performance efficiency.
- ACOCA represents a substantial advancement in context-aware caching for IoT applications.
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