A Hybrid Approach to Monitor Context Parameters for Optimising Caching for Context-Aware IoT Applications

TL;DR

This paper introduces a hybrid algorithm combining AHP and Sliding Window techniques to monitor context freshness, enhancing caching performance in IoT middleware for real-time applications.

Contribution

It presents a novel hybrid approach for dynamic context monitoring that improves cache efficiency in IoT context management platforms.

Findings

Significantly improves cache freshness and relevance in IoT applications.

Enhances real-time context data reliability and responsiveness.

Demonstrates effectiveness through experiments with real-world IoT data.

Abstract

Internet of Things (IoT) has seen a prolific rise in recent times and provides the ability to solve several key challenges faced by our societies and environment. Data produced by IoT provides a significant opportunity to infer context that is key for IoT applications to make decisions/actuations. Context Management Platform (CMP) is a middleware to facilitate the exchange and management of such context information among IoT applications. In this paper, we propose a novel approach to monitoring context freshness as a key metric, to improving the CMP's caching performance to support the real-time context needs of IoT applications. Our proposed hybrid algorithm uses Analytic Hierarchy Process (AHP) and Sliding Window technique to ensure the most relevant (as needed by the IoT applications) context information is cached. By continuously monitoring and prioritizing context attributes, the strategy adapts to IoT environment changes, keeping cached context fresh and reliable. Through experimental evaluation and using mock data obtained from a real-world mobile IoT scenario in section~\ref{use case}, we demonstrate that the proposed algorithm can substantially enhance context cache performance, by monitoring the context attributes in real time.