Estimating the lifetime of transient context for adaptive caching in IoT applications
Shakthi Weerasinghe, Arkady Zaslavsky, Seng W. Loke, Alexey Medvedev, Amin Abken · Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing · 2022
Context-awareness in Internet of Things (IoT) applications has significant impact on how IoT data can be processed, stored if needed, reused, and repurposed across multiple IoT applications. Emerging Context Management Platforms (CMP) mediate between context providers and context consumers in order to unify access to context and, provide interoperability that allows cross-domain context querying. This paper proposes an approach to adaptive context caching which enables CMPs to serve context queries from multiple IoT applications. It presents the transient nature of context which is a unique challenge when caching context that requires regular refreshing. The paper proposes two adaptive refreshing strategies based on online-estimated lifetimes (i.e., how long before data is estimated to have changed and refreshing is needed) - reactive and full-coverage. They are evaluated by developing mathematical models and simulations. We further assess the impact of different parameters on context cache performance. The results demonstrate the efficiency of adaptive context caching to minimize operational costs whilst preserving good enough refresh rate and compliance with Service Level Agreements.