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A Procedure to Observe Context-induced Renewal of Pavlovian-conditioned Alcohol-seeking Behavior in Rats

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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

This summary is machine-generated.

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.

Keywords:

adaptive context caching context-awareness internet of things reinforcement learning

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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.