Context-aware & privacy-preserving homecare monitoring through adaptive query derivation for IoT data streams with DIVIDE

Tracking #: 3129-4343

This paper is currently under review
Authors: 
Mathias De Brouwer
Bram Steenwinckel
Ziye Fang
Marija Stojchevska
Pieter Bonte
Filip De Turck
Sofie Van Hoecke
Femke Ongenae

Responsible editor: 
Guest Editors SW Meets Health Data Management 2022

Submission type: 
Full Paper
Abstract: 
Integrating Internet of Things (IoT) sensor data from heterogeneous sources with domain knowledge and context information in real-time is a challenging task in IoT healthcare data management applications that can be solved with semantics. Existing IoT platforms often have an issue with preserving the privacy of patient data. Moreover, the configuration and management of context-aware stream processing queries in semantic IoT platforms requires much manual, labor-intensive effort. Generic queries can deal with context changes but often lead to performance issues caused by the need for expressive real-time semantic reasoning. In addition, query window parameters are part of the manual configuration and cannot be made context-dependent. To tackle these problems, this paper presents DIVIDE, a component for a semantic IoT platform that automatically and adaptively derives and manages the queries of the platform’s stream processing components in a privacy-preserving, context-aware and scalable manner. By performing semantic reasoning to derive the queries when context changes are observed, their real-time evaluation does require any reasoning. The results of an evaluation on a homecare monitoring use case demonstrate how activity detection queries derived with DIVIDE can be evaluated in on average less than 3.7 seconds and can therefore successfully run on low-end IoT devices.
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Under Review