Probabilistic analysis of context caching in Internet of Things applications

Context caching plays an increasingly important role in delivering near real-time responses for context-aware distributed Internet of Things (IoT) applications, services and systems. A context management platform (CMP), a middleware which acts as an aggregator and redirector of contextual information to support smart IoT applications, requires adaptive context caching to process and manage enormous amounts of context stemming from IoT. In this work, we propose a novel approach to estimating the context information's demand probability, which helps improve the context retrieval performance of a CMP under near real-time constraints. The proposed approach uses context query logs and applies machine learning algorithms to estimate the context caching probability for context caching. We further use an evolutionary technique for optimising the context caching probability to improve the context retrieval performance of the CMP. We conduct an experimental evaluation using a research prototype CMP, Context-as-a-Service (CoaaS) and show that the proposed technique can significantly improve the context retrieval performance. Analysis of the experimental results showed with context caching probability optimized by evolutionary technique there is an average percentage decrease of 43.68% in the response time of CoaaS.