EDR: A Generic Approach for the Distribution of Rule-Based Reasoning in a Cloud-Fog continuum

Submitted by Nicolas Seydoux on 06/15/2019 - 14:05

Tracking #: 2238-3451

Authors: 
Nicolas Seydoux
Khalil Drira1
Nathalie Hernandez
Tierry Monteil

Responsible editor: 
Guest Editors Sensors Observations 2018

Submission type: 
Full Paper
Abstract: 
The successful deployment of the Semantic Web of Things (SWoT) requires the adaptation of the Semantic Web principles and technologies to the constraints of the IoT domain, which is the challenging research direction we address here. In this context we promote distributed reasoning approaches in IoT systems by implementing a hybrid deployment of reasoning rules relying on the complementarity of Cloud and Fog computing. Our solution benefits from the complementarity between Cloud and Fog infrastructures. Indeed, remote powerful Cloud computation resources are essential to the deployment of scalable IoT applications, and locally distributed constrained Fog resources, close to data producers, enable low-latency decision making. Moreover, as IoT networks are open and evolutive, the computation should be dynamically distributed across Fog nodes according to the transformation of the network topology. For this purpose, we propose the Emergent Distributed Reasoning (EDR) approach, implementing a dynamic distributed deployment of reasoning rules in a Cloud-Fog IoT architecture. We elaborated mechanisms enabling the genericity and the dynamicity of EDR. We evaluated its scalability and applicability in a simulated smart factory use-case. The complementarity between Fog and Cloud in this context is assessed based on the experimentation conducted.
Full PDF Version: 
PDF icon swj2238.pdf
Previous Version: 
EDR: A Generic Approach for the Dynamic Distribution of Rule-Based Reasoning in a Cloud-Fog continuum
Tags: 
Reviewed

Decision/Status: 
Accept

Solicited Reviews:
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Review #1
By Maxime Lefrançois submitted on 16/Jul/2019
Suggestion:
Accept
Review Comment:

The authors' response has addressed the minor issues pointed out in my review, and the paper is modified accordingly.
I thank the reviewers for the additional pdf with the revisions in the attached archive, it helped to assess the modifications.
I am in favor of accepting this version for publication.

Review #2
Anonymous submitted on 18/Jul/2019
Suggestion:
Accept
Review Comment:

I appreciated the work of the author in answering my comments. I think that al lot of points were clarified and the paper was considerably improved with respect to the prior version. Thus, I suggest to accept it.

Review #3
Anonymous submitted on 27/Aug/2019
Suggestion:
Accept
Review Comment:

The paper proposes a hybrid deployment of reasoning rules relying on the complementarity of Cloud and Fog computing. The proposed solution benefits from the remote powerful Cloud computation resources, essential to the deployment of scalable IoT applications while avoiding low-latency decision making by including the local distributed constrained Fog computation resources, close to data producers.

The paper proposes the Emergent Distributed Reasoning (EDR) approach, implementing a dynamic distributed deployment of reasoning rules in a Cloud-Fog IoT architecture. Mechanisms enabling the genericity and the dynamicity of EDR are presented, and the scalability and applicability are evaluated in a simulated smart factory use-case.

Overall the writing is sufficient, and the text is okay. The work proposes an original approach to deploy reasoning rules in a Fog IoT architecture. The evaluation is well explained, some small details like the format of the sensor data were added after the minor review. Overall, there are no significant problems with the replication of the evaluation.

The proposed approach is interesting from a research point-of-view, but it is hard to see its use in real IoT projects since "normal" IoT developers do not have the required set of skills. To future work, the authors should consider developing a tool that abstract the required skills, guiding developers through the process, and making the adoption of the approach easier.

Unfortunately, the authors did not address all comments from the previous reviews. Related work has not been updated. There is at least one typo in the reference (Chili).