Toward A Digital Twin IoT for the Validation of AI Algorithms in Smart-City Applications

Hamza Ngadi; Ahcene Bounceur; Madani Bezoui; Laaziz Lahlou; Mohammad Hammoudeh; Kara Nadjia; Laurent Nana

doi:10.1007/978-3-031-59933-0_8

Toward A Digital Twin IoT for the Validation of AI Algorithms in Smart-City Applications

Hamza Ngadi, Ahcene Bounceur^*, Madani Bezoui, Laaziz Lahlou, Mohammad Hammoudeh, Kara Nadjia, Laurent Nana

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The development of digital twins for road traffic has garnered significant attention within the scientific community, particularly in the realms of virtualization and the Internet of Things (IoT). The implementation of a digital twin for automobiles offers a virtual replica, capable of discerning the precise location, status, and real-time behavior of each vehicle present in the road traffic network. The primary objective of this endeavor is to create an advanced digital twin of cars that can seamlessly navigate through road traffic. To accomplish this, a meticulously selected technical approach involves employing a platform that simulates virtual sensor networks, accompanied by a purpose-built application that facilitates the access and dissemination of car-related data. Furthermore, this undertaking incorporates the utilization of an existing traffic simulator alongside a robust communication protocol to ensure seamless data transfer between the simulation environment and the sensors responsible for data collection from automobiles.

Original language	English
Title of host publication	Machine Learning for Networking - 6th International Conference, MLN 2023, Revised Selected Papers
Editors	Éric Renault, Paul Mühlethaler, Selma Boumerdassi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	108-117
Number of pages	10
ISBN (Print)	9783031599323
DOIs	https://doi.org/10.1007/978-3-031-59933-0_8
State	Published - 2024
Event	6th International Conference on Machine Learning for Networking, MLN 2023 - Paris, France Duration: 28 Nov 2023 → 30 Nov 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14525 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Conference on Machine Learning for Networking, MLN 2023
Country/Territory	France
City	Paris
Period	28/11/23 → 30/11/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

CupCarbon
Digital twin
Internet of Things
MQTT communication protocol
Smart-city
Sumo simulator

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-59933-0_8

Cite this

Ngadi, H., Bounceur, A., Bezoui, M., Lahlou, L., Hammoudeh, M., Nadjia, K., & Nana, L. (2024). Toward A Digital Twin IoT for the Validation of AI Algorithms in Smart-City Applications. In É. Renault, P. Mühlethaler, & S. Boumerdassi (Eds.), Machine Learning for Networking - 6th International Conference, MLN 2023, Revised Selected Papers (pp. 108-117). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14525 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-59933-0_8

Ngadi, Hamza ; Bounceur, Ahcene ; Bezoui, Madani et al. / Toward A Digital Twin IoT for the Validation of AI Algorithms in Smart-City Applications. Machine Learning for Networking - 6th International Conference, MLN 2023, Revised Selected Papers. editor / Éric Renault ; Paul Mühlethaler ; Selma Boumerdassi. Springer Science and Business Media Deutschland GmbH, 2024. pp. 108-117 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The development of digital twins for road traffic has garnered significant attention within the scientific community, particularly in the realms of virtualization and the Internet of Things (IoT). The implementation of a digital twin for automobiles offers a virtual replica, capable of discerning the precise location, status, and real-time behavior of each vehicle present in the road traffic network. The primary objective of this endeavor is to create an advanced digital twin of cars that can seamlessly navigate through road traffic. To accomplish this, a meticulously selected technical approach involves employing a platform that simulates virtual sensor networks, accompanied by a purpose-built application that facilitates the access and dissemination of car-related data. Furthermore, this undertaking incorporates the utilization of an existing traffic simulator alongside a robust communication protocol to ensure seamless data transfer between the simulation environment and the sensors responsible for data collection from automobiles.",

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author = "Hamza Ngadi and Ahcene Bounceur and Madani Bezoui and Laaziz Lahlou and Mohammad Hammoudeh and Kara Nadjia and Laurent Nana",

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Ngadi, H, Bounceur, A, Bezoui, M, Lahlou, L, Hammoudeh, M, Nadjia, K & Nana, L 2024, Toward A Digital Twin IoT for the Validation of AI Algorithms in Smart-City Applications. in É Renault, P Mühlethaler & S Boumerdassi (eds), Machine Learning for Networking - 6th International Conference, MLN 2023, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14525 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 108-117, 6th International Conference on Machine Learning for Networking, MLN 2023, Paris, France, 28/11/23. https://doi.org/10.1007/978-3-031-59933-0_8

Toward A Digital Twin IoT for the Validation of AI Algorithms in Smart-City Applications. / Ngadi, Hamza; Bounceur, Ahcene; Bezoui, Madani et al.
Machine Learning for Networking - 6th International Conference, MLN 2023, Revised Selected Papers. ed. / Éric Renault; Paul Mühlethaler; Selma Boumerdassi. Springer Science and Business Media Deutschland GmbH, 2024. p. 108-117 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14525 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Bounceur, Ahcene

AU - Bezoui, Madani

AU - Lahlou, Laaziz

AU - Hammoudeh, Mohammad

AU - Nadjia, Kara

AU - Nana, Laurent

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PY - 2024

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Ngadi H, Bounceur A, Bezoui M, Lahlou L, Hammoudeh M, Nadjia K et al. Toward A Digital Twin IoT for the Validation of AI Algorithms in Smart-City Applications. In Renault É, Mühlethaler P, Boumerdassi S, editors, Machine Learning for Networking - 6th International Conference, MLN 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2024. p. 108-117. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-59933-0_8

Toward A Digital Twin IoT for the Validation of AI Algorithms in Smart-City Applications

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Fingerprint

Cite this