Publications
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Multi-Agent Simulation for Scheduling and Path Planning of Autonomous Intelligent Vehicles
Autonomous and Guided Vehicles (AGVs) have long been employed in material handling but necessitate significant investments, such as designating specific movement areas. As an alternative, Autonomous and Intelligent Vehicles (AIVs) have gained traction due to their adaptability, intelligence, and capability to handle unexpected obstacles. Yet, challenges like optimizing scheduling and path planning, and managing routing […]
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DLSTM-SCM– A Dynamic LSTM-Based Framework for Smart Supply Chain Management
In the retail industry, SCM holds significant importance as it ensures the efficient movement of goods from suppliers to customers. In this intricate and fast-paced environment, the availability of accurate information and data is crucial. The purpose of this paper is to develop a framework that enhances forecasting accuracy and efficiency in supply chain operations […]
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A Smart Mining Strategy for Blockchain-Enabled Cyber-Physical Systems
This article presents a novel approach to enhancing asset management and resource sharing in intelligent systems with a focus on the industrial ones using Blockchain. We introduce a hybrid network architecture—termed the Hybrid Cyber-Physical System (HyCPS)—designed to facilitate decentralized, trustworthy data sharing across all layers. Central to this framework is a robust smart consensus protocol […]
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Benchmarking OpenStack for edge computing applications
In this paper, we focus on the identification and evaluation of performance factors of OpenStack-based edge computing platforms. Such infrastructure relies on the deployment of additional computing resources close to the data source, to alleviate low throughput, latencies and network congestion. While cloud data centres offer numerous compute-intensive processing units, the edge layer leverages heterogeneous, […]
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From Simulation to Digital Twins, the Case of Internet of Things Research and Tools
The digitalisation of the environment surrounding human beings in their daily life is a major challenge facing today’s technological progress. Building digital replicas of humans and systems help us to understand our environment, to anticipate its variations and to better explain its behaviour. Research in digital twins is continuously developing due to the various benefits […]
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A Collaborative Real-Time Object Detection and Data Association Framework for Autonomous Robots Using Federated Graph Neural Network
Autonomous robotics require secure and decentralized decision-making systems that ensure data privacy and computational efficiency, especially in critical areas. Current centralized models or human input are associated with data breaches and security vulnerabilities. To counter these, we propose CoRODDA, a dedicated framework combining federated learning and graph neural networks. It enhances object detection and data […]
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Securing Autonomous Vehicles: Fundamentals, Challenges, and Perspectives
This paper introduces a comprehensive methodology aimed at enhancing security and immunity in automotive networks, placing a primary focus on the detection, prediction, and forecasting of errors in autonomous vehicles. Conventional approaches to vehicle cybersecurity often struggle to keep pace with evolving threats and provide effective error detection mechanisms. Our proposed methodology seeks to bridge […]
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Securing Autonomous Vehicles
This talk delves into the fundamentals of security for autonomous vehicles, exploring the challenges and existing solutions in this rapidly evolving field. It discusses the limitations of current security measures and introduces the application of formal methods to model safety and security in autonomous vehicles. The talk outlines the process of applying formal methods to […]
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Distributed Transactive Energy Management in Microgrids based on Blockchain
While the Internet of Energy (IoE) introduced advanced collaborative management methods through real-time monitoring and demand response programs, smart grids still grapple with challenges related to central governance, ineffective information aggregation, and privacy issues. These problems create significant hurdles in smart grid management, particularly with the high penetration of distributed energy resources (DERs). In this […]
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Photogrammetry and deep learning for energy production prediction and building-integrated photovoltaics decarbonization
Building-Integrated photovoltaics (BIPV) have emerged as a promising sustainable energy solution, relying on accurate energy production predictions and effective decarbonization strategies for efficient deployment. This paper presents a novel approach that combines photogrammetry and deep learning techniques to address the problem of BIPV decarbonization. The method is called BIM-AITIZATION referring to the integration of BIM […]
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Towards Hybrid Predictive Maintenance for Aircraft Engine: Embracing an Ontological-Data Approach
This article introduces a novel Remaining Useful Life (RUL) estimation method using Machine Learning techniques, guided by domain knowledge, and applied to a dataset of aircraft engines (C-MAPSS). Predictive maintenance, or prognostics, offers the opportunity to predict the lifespan of aircraft engines, thereby reducing costs, minimizing breakdowns, and ensuring their reliability. While existing solutions in […]
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BootBOGS: Hands-on optimizing Grid Search in hyperparameter tuning of MLP
Neural networks are widely used in the literature in a variety of fields and for a large number of applications. A major challenge in their use is the need to identify and process hyperparametric values. Grid Search is a widely used technique for meeting this task. It systematically searches for values in a predefined range […]
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