Post-doctoral position on Distributed embedded reasoning for the Web of Things

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Post-doctoral position on Distributed embedded reasoning for the Web of
Things
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Keywords
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Web of things, Semantic Web, Rule-based reasoning, Embedded, Distributed,
Edge computing

Context
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This Postdoc position is in the context of the CosWot project (“Constrained
Semantic Web of Things” https://coswot.gitlab.io/), funded by the French
National Research Agency. CoSWoT considers semantic web technologies for
the Web of things (WoT). The objectives of the project are to propose a
distributed WoT-enabled software architecture embedded on constrained
devices with two main characteristics: 1) it uses ontologies to
declaratively specify the application logic of devices and the semantics of
the exchanged messages; 2) it adds rule-based reasoning [1, 2, 13, 14]
functionalities to devices, so as to distribute processing tasks among
them. Doing so, the development of applications including devices of the
WoT will be highly simplified: our platform will enable the development and
execution of intelligent and decentralised smart WoT applications despite
the heterogeneity of devices.
The main objectives of this Postdoc are to provide contributions to
distributed reasoning on the Web of Things.

Research Lab
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The Postdoc will be a member of the LabHC Laboratory, St-Etienne, France.

Lab. Hubert Curien (
https://laboratoirehubertcurien.univ-st-etienne.fr/en/index.html) is a
joint research unit of CNRS (UMR 5516), Université Jean Monnet in
Saint-Etienne, and the Institut d’Optique Graduate School, working on
topics related to optics, photonics and microwave, computer science,
telecom and image. The members from LaHC involved in the CoSWoT project
include researchers of its team named as Data Intelligence. They specialise
in AI and data processing.
Close collaboration will also be necessary with the LIRIS Lab. team where a
PhD student works on incremental and embedded reasoning.

Objectives
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The objective of the Postdoc is to design and implement an efficient
distributed reasoner for the Web of Things (WoT). The reasoner should be
able to work on constrained (with limited processing capacity, memory and
energy, i.e., sensor nodes and other embedded devices with
micro-controllers) and autonomous devices. The target architecture is based
on edge computing: main components, including sensors and actuators as well
as intermediate nodes and gateways of various computing capabilities.

Expected Contributions
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There are some existing works paving the way for such reasoners, including
[1-12]. However, they are not suitable for WoT and diversely constrained
objects. Such devices are not all capable of performing all reasoning
tasks. We aim for edge intelligence where incremental reasoning concerns
both sensor data streams and contextual data. As it is probable that all
constrained objects will not be able to execute all reasoning tasks,
distributing these data and tasks optimally over a network of WoT nodes
will also be necessary [8-9].
The postdoc will define a method for the distribution of reasoning tasks
among the edge and devices, where each device collaboratively performs a
part of the reasoning tasks. At runtime, reasoning tasks must be
distributed in an efficient manner and to the appropriate locations. This
will be done while considering WoT constraints including proximity to the
data source, capabilities and resources constraints, current computational
load, bandwidth, etc.

Candidate Profile
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PhD in computer science.
Skills in semantic web knowledge representation, rule-based reasoning and
distributed algorithms are required.
Proficiency in the English language for speaking, writing and reading are
necessary.
Programming skills in C, JavaScript are a plus.
French language skills are not a prerequisite.
Depending on the candidate native language, French or English will be the
working language.

Salary:  around 2192 € net per month during 1 year
There will also be an option to teach in the university.

Expected starting date: October 2021

Place of work
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LabHC, University St-Etienne, France
short missions at other partner’s locations will be required.

To apply
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Application Deadline: 11/07/2021

Candidates should send the following:
A motivation letter
A CV
All documents attesting the required skills and knowledge
2 selected publications
Contact information of 2 professors who can provide recommendation on the
candidate

The applications should be sent to singh.d.kamal@gmail.com

References
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[1] Nenov, Y., Piro, R., Motik, B., Horrocks, I., Wu, Z., & Banerjee, J.
RDFox: A highly-scalable RDF store. In ISWC: 3-20, 2015.
[2] Terdjimi, M., Médini, L., Mrissa, M. HyLAR: Hybrid Location-Agnostic
Reasoning. In ESWC Devs Workshop 2015.
[3] Terdjimi, M., Médini, L., Mrissa, M. HyLAR+: Improving Hybrid
Location-Agnostic Reasoning with Incremental Rule-based Update. In WWW
2016, companion volume.
[4] Terdjimi, M., Médini, L., Mrissa, M. Web Reasoning using Fact Tagging.
In WWW 2018, companion volume
[5] Chevalier, J., Subercaze, J., Gravier, C., Laforest, F. Slider: an
Efficient Incremental Reasoner. In SIGMOD 2015.
[6] Chevalier, J., Subercaze, J., Gravier, C., Laforest, F. Incremental and
Directed Rule-Based Inference on RDFS. In DEXA 2016.
[7] Jacopo Urbani and Ceriel Jacobs. 2020. Adaptive Low-level Storage of
Very Large Knowledge Graphs. In Proceedings of The Web Conference 2020 (WWW
’20). Association for Computing Machinery, New York, NY, USA, 1761–1772.
DOI:https://doi.org/10.1145/3366423.3380246
[8] Seydoux, N., Drira, K., Hernandez, N., & Monteil, T. EDR: A Generic
Approach for the Dynamic Distribution of Rule-Based Reasoning in a
Cloud-Fog continuum. In Semantic Web Journal, 2019.
http://semantic-web-journal.net/system/files/swj2238.pdf
[9] Su, X., Li, P., Riekki, J., Liu, X., Kiljander, J., Soininen, J. P.,
..... & Li, Y. (2018, March). Distribution of semantic reasoning on the edge
of internet of things. In 2018 IEEE International Conference on Pervasive
Computing and Communications (PerCom) (pp. 1-9). IEEE.
[10] Maarala, A. I., Su, X., & Riekki, J. (2017). Semantic reasoning for
context-aware Internet of Things applications. IEEE Internet of Things
Journal, 4(2), 461-473.
[11] Ren, X., & Curé, O. Strider: A hybrid adaptive distributed RDF stream
processing engine. In International Semantic Web Conference (pp. 559-576).
Springer, Cham (2017).
[12] Su, X., Gilman, E., Wetz, P., Riekki, J., Zuo, Y., & Leppänen, T.
(2016, June). Stream reasoning for the Internet of Things: Challenges and
gap analysis. In Proceedings of the 6th Int. Conf. on Web Intelligence,
Mining and Semantics (p. 1). ACM.
[13] Charles L Forgy.  Rete: A fast algorithm for the many pattern/many
object pattern match problem.  InRea-dings in Artificial Intelligence and
Databases, pages 547–559. Elsevier, 1989.
[14] William Van Woensel and Syed Sibte Raza Abidi. Optimizing semantic
reasoning on memory-constrained platforms using the rete algorithm.  In
European Semantic  Web  Conference,  pages  682–696.  Springer, 2018