DCMF: A Dynamic Context Monitoring and Caching Framework for Context Management Platforms
Ashish Manchanda, Prem Prakash Jayaraman, Abhik Banerjee, Kaneez Fizza, and Arkady Zaslavsky

TL;DR
This paper introduces DCMF, a dynamic framework for context caching in IoT platforms that improves cache hit rates and reduces expiry by evaluating context relevance and freshness in real-time.
Contribution
The paper presents a novel DCMF framework with a context evaluation engine and management module that dynamically assesses context relevance and manages cache updates in IoT environments.
Findings
12.5% higher cache hit rate
up to 60% reduction in cache expiry
demonstrated scalability in real-world data
Abstract
The rise of context-aware IoT applications has increased the demand for timely and accurate context information. Context is derived by aggregating and inferring from dynamic IoT data, making it highly volatile and posing challenges in maintaining freshness and real-time accessibility. Caching is a potential solution, but traditional policies struggle with the transient nature of context in IoT (e.g., ensuring real-time access for frequent queries or handling fast-changing data). To address this, we propose the Dynamic Context Monitoring Framework (DCMF) to enhance context caching in Context Management Platforms (CMPs) by dynamically evaluating and managing context. DCMF comprises two core components: the Context Evaluation Engine (CEE) and the Context Management Module (CMM). The CEE calculates the Probability of Access (PoA) using parameters such as Quality of Service (QoS), Quality of…
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