Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design

Tracking #: 3281-4495

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
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 issues with preserving the privacy of patient data. Moreover, configuring and managing 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 adaptively derives and manages the queries of the platform’s stream processing components in a context-aware and scalable manner, and that enables privacy by design. 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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Solicited Reviews:
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Review #1
Anonymous submitted on 11/Oct/2022
Review Comment:

As requested in the previous review round, the authors have now added a "Research objectives and paper contribution" section that is useful in order to make the scientific contributions of the paper more clear.
Related Work has been enriched with static healthcare-IoT data management-related research works, in contrast to the context-aware solution provided by DIVIDE.
Therefore the progress beyond the SotA technologies is now obvious.

Minor comments:
The authors could merge the DIVIDE system overview with the Implementation paragraph (as there is no need for two separate small sections).
A detailed example and concrete steps for running the DIVIDE platform jar file feeding the dataset provided in GitHub would be welcome in This would enable the replication of the experiments by the research community.

Review #2
Anonymous submitted on 27/Oct/2022
Minor Revision
Review Comment:

The authors have submitted an enhanced version of the paper. In this new version, they argue that the proposed system is able to incorporate privacy by design, by allowing the end users to define which data is kept locally and which parts of the data can be sent over the network. Also, the related works section has been expanded in order to cover the privacy preservation topic. Although the enhancement is clear, the authors have ignored my suggestion regarding the lack of formalization in Section 3. Thus, I believe that this section should be substantially improved by encompassing a better problem formalization.