# From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

arXiv: https://arxiv.org/abs/2211.11259 · HTML: https://mlsanity.com/paper/2211.11259/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 164 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Context Aware Computing for The Internet of Things: A Survey](https://mlsanity.com/paper/1305.0982/) (2013)
- [A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks](https://mlsanity.com/paper/1712.07084/) (2017)
- [On the Role of Age of Information in the Internet of Things](https://mlsanity.com/paper/1812.08286/) (2018)
- [Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing](https://mlsanity.com/paper/1812.08593/) (2018)
- [The Role of Big Data Analytics in Industrial Internet of Things](https://mlsanity.com/paper/1904.05556/) (2019)
- [A Survey of Deep Learning for Data Caching in Edge Network](https://mlsanity.com/paper/2008.07235/) (2020)
- [Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks](https://mlsanity.com/paper/1606.04236/) (2016)
- [Time Aware Least Recent Used (TLRU) Cache Management Policy in ICN](https://mlsanity.com/paper/1801.00390/) (2018)
- [Learning-based Dynamic Cache Management in a Cloud](https://mlsanity.com/paper/1902.00795/) (2019)
- [CA4IOT Context Awareness for Internet of Things](https://mlsanity.com/paper/1301.1084/) (2013)
- [Cache policies for cloud-based systems: To keep or not to keep](https://mlsanity.com/paper/1312.0499/) (2013)
- [Information Centric Networking in the IoT: Experiments with NDN in the Wild](https://mlsanity.com/paper/1406.6608/) (2014)

## Abstract (arXiv)

Context information is in demand more than ever with the rapid increase in the number of context-aware Internet of Things applications developed worldwide. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to a large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability and heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of the state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.