A Modular Approach to Context-Aware IoT Applications

Abstract

The Internet of Things (IoT) refers to an environment of ubiquitous sensing and actuation, where devices are connected to a distributed backend infrastructure. It offers the opportunity to access a large amount of input data, and process it into contextual information about different system entities for reasoning and actuation. State-of-the-art IoT applications are generally black-box, end-to-end application-specific implementations, and cannot keep up with timely resolution of all this live, continually updated, heterogeneous data. In this work, we propose a modular approach to these context-aware applications, breaking down monolithic applications into an equivalent set of functional units, or context engines. By exploiting the characteristics of context-aware applications, context engines can reduce compute redundancy and computational complexity. In conjunction with formal data specifications, or ontologies, we can replace application-specific implementations with a composition of context engines that use common statistical learning to generate output, thus improving context reuse. We implement interconnected context-aware applications using our approach, extracting both user activity and location context from wearable sensors. We compare our infrastructure to single-stage monolithic implementations, demonstrating a reduction in application latency by up to 65% and execution overhead by up to 50% with only a 3% reduction in accuracy.