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2025 | Buch

Internet of Things. 7th IFIPIoT 2024 International IFIP WG 5.5 Workshops

GRAAL4IoT 2024, STAND4IoT 2024, Posters, Nice, France, November 6–8, 2024, Proceedings

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Über dieses Buch

This book constitutes the refereed proceedings of the workshops held in conjunction with the 7th IFIP WG 5.5 International Cross-Domain Conference on Internet of Things, IFIPIoT 2024, in Nice, France, in November 2024. For the conference, including the workshops, a total of 33 submissions has been received. This volume includes 5 workshop papers and 3 poster papers. They have been organized in the following topical sections: IFIP IoT’24 Posters Session; GRAAL4IoT’24: First International Workshop on the Design, Verification, and Validation of IoT Systems and STAND4IoT'24: First International Workshop on IoT Standards.

Inhaltsverzeichnis

Frontmatter

IFIP IoT’24 Posters Session

Frontmatter
An Open Source Trusted Edge Architecture of Federated Dataspaces for the Food Supply Chain
Abstract
Currently, quality assurance in the food supply chain is predominantly conducted manually with traditional destructive methods. Best before date forecasts depend mainly on time of arrival without including weather or cold chain. Measured data within the food supply chain is not digitized and can therefore not be aggregated for analysis.
We propose a new architecture for improving sustainability of the food supply by combining innovative measurement techniques with a trusted cloud edge infrastructure. Forecasts based on digital twins and a shared federated data space for freshness data serve to optimize supply chain processes and reduce the loss of fresh produce along the supply chain. In this paper, we present a suitable system architecture with independent data spaces and flexible implementations for trusted local deployments.
Alexander Tessmer, Jannis Mast, Philipp Loer, Matthias Brunner, Felix Lippert, Nils Aschenbruck
Design and Experimentation of a Distributed Information Retrieval-Hybrid Architecture in Cloud IoT Data Centers
Abstract
The proliferation of data from IoT devices has introduced challenges for efficient and accurate information retrieval in distributed Cloud Data Centers. This paper presents a Hybrid Distributed Information Retrieval (H-DIR) model optimized for IoT environments using advanced semantics and a hybrid query approach. The model aims to improve retrieval precision and efficiency by leveraging semantic concepts and contextual metadata, while also enhancing security and data privacy. Applied in a real industrial setting for gardening services, the H-DIR model demonstrates potential to increase revenue through economic quantification of services and value creation via faster preventive measures.
Davide Tosi, Roberto Pazzi
A Cyber-Physical Infrastructure for Smart Energy Buildings
Abstract
The advancement of renewable energy and low carbon technologies, such as electric vehicles, necessitates that smart buildings adopt innovative energy use cases to become adaptive and responsive.
Additionally, the proliferation of Internet of Things (IoT) devices introduces new applications for enhancing comfort, air quality, health, and energy consumption. These evolutions require Building Automation Systems (BAS) to manage new devices and implement novel applications, which are often beyond the capabilities of current BAS technologies. Consequently, this paper proposes a Cyber-Physical Architecture that facilitates the integration of third-party IoT devices and the development of novel use cases. Specifically, the architecture supports the implementation of a Smart Energy Management System alongside standard BAS to optimize energy usage in smart buildings through IoT and artificial intelligence algorithms. The paper also presents a case study of the architecture’s implementation in a smart building in Nice, France, and discusses the advantages and disadvantages of the proposed cyber-physical architecture for smart energy buildings.
Benoit Couraud, Erwin Franquet, Honorat Quinard, Pierre-Jean Barre, Paulo Moura, Yann Rozier, Franck Dechavanne, Pierre Costini, Azeddine El Youssfi, Ahmad Taha, Sonam Norbu, David Flynn

GRAAL4IoT’24: First International workshop on the DesiGn, VeRificAtion, and VALidation of IoT Systems

Frontmatter
SINDIT: A Framework for Knowledge Graph-Based Digital Twins in Smart Manufacturing
Abstract
Digital twins are revolutionizing smart manufacturing by facilitating real-time monitoring, simulation, and optimization of physical processes. This paper introduces the SINDIT framework, a comprehensive approach tailored for developing knowledge graph-based digital twins. By seamlessly integrating cognitive capabilities, SINDIT enhances decision-making and operational efficiency within manufacturing systems. Central to its architecture is a robust data pipeline, adept at organizing and linking vast amounts of heterogeneous data, thereby enabling advanced data analytics and reasoning.
Case studies from the pilots of the COGNIMAN project underscore the practical utility and benefits of the SINDIT framework. These studies showcase notable enhancements in predictive maintenance, process optimization, and overall productivity. By harnessing the power of knowledge graphs and cognitive capabilities, SINDIT represents a promising avenue for driving innovation and efficiency in smart manufacturing. Through this framework, manufacturers can achieve a higher level of operational insight and agility, leading to improved performance and competitiveness in the market.
An Ngoc Lam, Gøran Brekke Svaland, Miguel Ángel Barcelona, Shane Keaveney, Wissam Mallouli, Luong Nguyen, Assia Belbachir, Xiang Ma, Akhilesh Kumar Srivastava, Ahmed Nabil Belbachir
Enhancing IoT Security in 6G Networks: AI-Based Intrusion Detection, Penetration Testing, and Blockchain-Based Trust Management (Work-in-Progress Paper)
Abstract
The exponential growth of Internet of Things (IoT) devices in upcoming 6G networks poses significant security challenges, particularly concerning Distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized access. This paper presents the NATWORK project’s approach to addressing these challenges through three distinct use cases (UC): UC#3.1 focuses on developing AI-driven machine learning techniques for anomaly detection and DDoS mitigation; UC#3.2 introduces advanced AI-powered penetration testing and vulnerability assessment tools; and UC#3.3 explores blockchain-based security mechanisms to enhance trust and secure communications in IoT ecosystems. Collectively, these use cases aim to fortify IoT networks against evolving cyber threats, ensuring data integrity and network resilience.
Vinh Hoa La, Wissam Mallouli, Manh Dung Nguyen, Edgardo Montes de Oca, Ana Cavalli, Péter Vörös, Károly Kecskeméti, Mohammed Alshawki, Sándor Laki, Antonios Lalas, Sarantis Kalafatidis, Asterios Mpatziakas, Nikolaos Makris, Anastasios Drosou
Building Digital Twins from the Unseen: Leveraging Similar Workflows to Protect IoT-Equipped Infrastructures
Abstract
In IoT-based systems, managing risk effectively is crucial for maintaining operational continuity, especially when faced with attacks on physical assets such as sensors or actuators. These situations demand immediate and coordinated human responses to minimize damage. This paper explores the initial challenges in managing and predicting the evolution of human-involved workflows in such contexts, especially in the absence of comprehensive historical data for model training. We propose a novel approach leveraging digital twins, modeled using BPMN 2.0, to facilitate knowledge transfer across semantically similar workflows. This methodology allows for preliminary predictions regarding unmonitored workflows by utilizing insights from previously established ones. We present initial results from both synthetic and real-world data, which suggest the potential of our approach to enhance risk management practices in IoT settings. These findings are intended to foster discussion and further exploration within the research community.
Bernat Coma-Puig, Jacek Dominiak, Victor Muntés-Mulero
Threats to the IoT Device Production Processes – A Blind Spot in the Product Security Lifecycle
Abstract
The production of embedded and constrained IoT devices is a security-critical but often neglected step in the product security lifecycle. The secure development of devices has become empowered over the last decade via the implementation of DevOps processes. However, the transmission of created artifacts into the production site and onto the device itself is a regularly overlooked procedure in the security assessment. This study shows the complexity and proposes a production model that is split into four stages for analysis. The four stages comprise (1) the transmission of artifacts, (2) the management of artifacts, (3) programming of the device, and (4) provisioning of the IoT device. Assets and threat actors are defined, and critical scenarios are introduced to explain their impact on IoT device production. Concluding, the discussion presents possible approaches and their limitations based on the given variety. In the future, this will facilitate the protection of critical and valuable phases of production, thereby enhancing the security and trustworthiness of IoT devices.
Philipp Schubaur, Peter Knauer, Dominik Merli

STAND4IoT’24: First International Workshop on IoT Standards

Frontmatter
Empowering Real-Time IoT Applications: A Brief Review on Leveraging GPU Acceleration for Latency Reduction
Abstract
The rapid increase in the number of IoT (Internet of Things) devices and the consequent surge in data transmission pose significant challenges to real-time data processing and telecommunication technologies. This has led to a growing interest in edge computing as a means to mitigate latency issues associated with centralized cloud processing. In this context, the integration of energy-efficient programmable GPUs (Graphics Processing Units) alongside CPUs (Central Processing Units) in IoT devices presents a promising opportunity to address latency challenges in real-time IoT applications. This brief review explores the potential of integrating energy-efficient programmable GPUs (Graphics Processing Units) alongside CPUs (Central Processing Units) in IoT devices to tackle latency issues in real-time IoT applications. The focus is on how GPUs can accelerate real-time IoT applications and minimize latency, providing valuable insights for developers looking to harness the capabilities of GPUs in IoT devices. Key considerations include identifying suitable real-time IoT applications’ parts for GPU offloading and efficiently managing the offloading process.
Amina Selma Haichour, Khaled Benfriha
Backmatter
Metadaten
Titel
Internet of Things. 7th IFIPIoT 2024 International IFIP WG 5.5 Workshops
herausgegeben von
Gaëtan Rey
Jean-Yves Tigli
Erwin Franquet
Copyright-Jahr
2025
Electronic ISBN
978-3-031-82065-6
Print ISBN
978-3-031-82064-9
DOI
https://doi.org/10.1007/978-3-031-82065-6