With increasing congestion in urban airspace, centralized Unmanned Aircraft System Traffic Management (UTM) face scalability challenges and risks of single-point failures, driving the need for robust decentralized solutions to ensure safe autonomous UAV navigation. This paper introduces the Graph-Attentive Deep Collision Avoidance (GADCA) framework, a decentralized Multi-Agent Reinforcement Learning (MARL) approach for collision avoidance in urban airspace. GADCA extends the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) by integrating Graph Attention Networks (GAT) to model dynamic inter-agent relationships, enabling UAVs to navigate effectively using local observations and shared situational awareness. Addressing limitations in prior work, GADCA incorporates 3D dynamics, continuous velocity action spaces, and scalable fleet sizes for robust performance. Unlike centralized UTM methods, GADCA operates in a decentralized manner during execution, where each UAV independently makes collision-avoidance decisions based on local and shared information, without relying on a central controller. It combines decentralized MARL execution with graph-based relational reasoning to enhance collision avoidance. Experimental results show that GADCA outperforms MADDPG across fleets of 5 to 10 UAVs, achieving 45.8% to 67.7% improvements in learning performance, 12.0% to 54.5% enhancements in task completion, up to 23% reduction in conflicts, and 58% to 100% reduction in collision risk, with no physical collisions recorded. The framework’s dynamic adjacency matrix and collective reward mechanism effectively balance navigation efficiency and safety in complex 3D urban environments, using a highly realistic Gauss-Markov mobility model.

International audience

International audience

Transactional systems have been designed to deal with specific database models, such as centralized versus decentralized databases like blockchain. This heterogeneity brings many constraints on the integration of the different systems involved in collaborative business processes, leading to sacrificing either consistency or low latency. Hence, a unified approach that can handle high-volume streaming transactions while ensuring data consistency and low latency is required. To achieve this objective, in this work, we analyze the limitations of these existing transactional models, particularly those relying on log-based mechanisms for durability and recovery. Then, we propose a Transactional Framework that unifies diverse transactional models through decentralized, event-driven, end to end transactional mechanisms. Our solution integrates adaptive compensation strategies to correct errors in real time and prevent duplicate processing while efficiently managing streaming data. By consolidating different transaction models into a single cohesive system, our framework addresses also scalability in order to maintain data coherence under high loads. Our evaluation demonstrates that this approach provides a robust, scalable, and efficient solution for modern transactional environments.

Non-Fungible Tokens (NFT) represent a sub form of cryptocurrencies, which allow investments into art, digital rights or any form of unique assets. Our study adds to a young body of research, which tries to analyze the characteristics of these new markets. In our study we present a new methodology to analyze peer-to-peer transactions in Non-Fungible tokens (NFT). We argument that this approach is superior to using prefabricated market data from NFT platform data, because it allows to track transactions to the point 0. Compared to that prefabricated market data could prove to be incomplete, missing peer-to-peer transactions. The difficulty of our approach was to develop a methodology which looks at all available transactions of a NFT on the blockchain, and not just at transactions which take place at specific trading platforms. This research is challenging due to the amount, and complexity of blockchain data which need to be stored and analyzed. For our study we constructed a dataset for the eth blockchain, which represented a complete chain of blockchain transactions, while then comparing our data with platform data on specific NFT’s. We found that our approach provides a more detailed view of the transactions in specific NFT’s. This detailed view allowed us to see specific transaction details. Our approach shall inspire future research to favor complete datasets of NFT transactions, before easily available data from trading platforms.

Service Level Agreements (SLAs) play a critical role in modern service ecosystems, formalizing commitments between providers and customers to ensure compliance with performance metrics and Quality of Service (QoS) requirements. However, the lack of standardized, user-friendly tools for defining and customizing 5G SLAs in machine-readable formats, such as XML, hinders automation and interoperability in service management. To address this challenge, we propose a formal approach based on the Event-B method to model SLA contracts and ensure their correctness and reliability. We define an incremental Event-B model that captures SLA constraints and verify their consistency using proof obligations and animation. This ensures that SLA enforcement does not alter the expected execution semantics of contracts.

Despite being a cornerstone of scientific progress, reproducibility remains a key challenge in deep reinforcement learning (DRL). This paper presents a reproducibility study of the RELIANCE method, a DRL-based approach for drone collision avoidance originally proposed by Ouahouah et al. We identify critical flaws in RELIANCE’s exploration strategy and reward function, which impede learning efficiency and task completion. By refining the exploration strategy and introducing a distance-based reward shaping mechanism, we achieve notable improvements in collision avoidance performance, reward accumulation, and success rates. Our modifications yield a 17-fold increase in success rates and a 15.56% reduction in collisions, alongside higher cumulative rewards. These findings highlight the vital role of early exploration and carefully crafted reward functions in DRL applications.

Social Media Intelligence (SOCMINT) is widely used for gathering sensitive information about individuals, companies, or organizations, fueling potential attacks. This study highlights the inadequacy of current privacy protection measures and proposes an automated, sustainable approach to correlate user profiles, including homonyms and pseudonyms, solely through publicly available data. A sustainable method presents a solution to bypass API privatization and anti-scraping measures for data collection and formatting, emphasizing data correlation and the creation of cross-referenced dictionaries. Aggregated data enables indirect correlations, enhancing accuracy and relevance compared to existing methods. The purpose of this study is to create a self-learning knowledge base that addresses acronyms, homonyms, and pseudonyms while incorporating context to establish deeper connections between data. Comparisons will account for contextual relevance rather than relying solely on distances or metrics. Despite challenges posed by GDPR implementation and diminishing exploitable data, analysis of a database containing 7,600 accounts from major social platforms reveals over 90% accuracy in user-to-profile associations with 30% of homonyms and unpredictable pseudonyms or usernames. Notably, the study unveils the feasibility of retrieving information from private profiles solely through the application of dictionaries. The study demonstrates the limits of privacy on social networks, with users playing a passive role in the extensive dissemination of their data. Therefore, governance structures require innovative solutions to guarantee end-to-end protection and privacy.

The anticipated proliferation of Unmanned Aerial Vehicles (UAVs) in the airspace in the coming years has raised concerns about how to manage their flights to avoid collisions and crashes at various stages of flight. To this end, many Unmanned Aircraft Traffic Management systems (UTM) have been designed. These systems use various methods for managing UAV conflicts. Several surveys have reviewed conflict resolution methods for UAVs. However, to the best of our knowledge, there is no survey specifically addressing conflict detection and resolution methods in UTM, particularly those using AI-based methods. Therefore, this article serves as a comprehensive survey of all UAVs conflicts detection and resolution methods proposed in the literature and their use in the UTM systems. This survey classifies the methods into two categories: classical (non-learning) methods and learning-based methods. Classical methods typically rely on pre-defined algorithms or rules for UAVs to avoid collisions, whereas Artificial Intelligence-based methods, including Machine Learning (ML) and especially Reinforcement Learning (RL), enable UAVs to adapt to their environment, autonomously resolve conflicts, and exhibit intelligent behavior based on their experiences. It also presents their application in the conflict resolution service for UTMs. Additionally, the challenges and issues associated with each type of methods are discussed. This article can serve as a foundational resource for researchers in guiding their selection of methods for conflict resolution, particularly those relevant to UTM systems.

Recently, the integration of Network Function Virtualization (NFV) with the blockchain has been gaining a lot of attention. This combination aims to avoid traditional NFV issues such as trust, payment, and security. Several works have been proposed to orchestrate, buy, execute, and manage the life cycle of a Virtual Network Function (VNF). Thus, they came as NFV marketplaces and orchestration platforms. In this paper, we provide a novel secure End-to-End NFV marketplace called “VNFO-DCSC”, that uses dynamic composite smart contracts. This platform provides full orchestration, management, execution, and monitoring of VNFs in a secure way. “VNFO-DCSC” is implemented following the European Telecommunications Standards Institute (ETSI) standards [1] for NFV management and it is available on GitHub.

The increasing use of Unmanned Aerial Vehicles (UAVs), especially in the civil sector, has brought to the fore-front the challenge of managing and integrating them within national airspace alongside manned aircraft. Unmanned Traffic Management (UTM) is a specialized system designed to oversee unmanned aircraft operations. A UTM system provides an array of services and can adopt either a centralized or decentralized architecture. The decentralized architecture serves as a possible solution to the single point of failure problem; however, it may introduce security issues in the areas of identification, flight plan management and communication links. These issues invariably erode the performance and efficiency of the UTM. Renowned for its robust security capabilities, blockchain technology presents an effective means to enhance the security framework of a UTM. In this article, we delineate the security challenges that could impact a UTM, and we explore the avenues through which blockchain technology has been leveraged to address these challenges.

Technology is always improving, which raises standards of living and makes life easier for people. A significant portion of these advancements are aimed at healthcare systems, with a particular emphasis in recent years on assisting elderly people in maintaining their independence in their homes. Data is collected continuously from various sensors and is used for contextual anomaly detection. As a result, the amount of sensor contextual data grows exponentially. These sensor data’s ability to be shared, reused, and interpreted was, however, constrained by the lack of syntactic or semantic integrity. On the other hand, understanding the Quality of Context (QoC) is acknowledged as a crucial component for the health monitoring system’s performance. The QoC has a significant impact on how contextual data behaves in health monitoring systems. As a result, health monitoring systems need to manage the QoC they rely on carefully and effectively. To address these problems, the main goal of this study is to develop a Context and Quality of Context (CQoC) ontology, which offers a framework for handling and utilizing contextual data, any QoC criteria, as well as context interpretation in a health monitoring system. Another purpose of this paper is the development of a Semantic QoC-aware Health Monitoring System (SQoCHMS) for the elderly that assures their safety and issues early alerts to help them deal with minor everyday problems related to such disorders.

In this article, we study the NP-Hard combinatorial optimization problem of the minimum initial marking (MIM) computation in labeled Petri net (L-PN) while considering a sequence of labels to minimize the resource consumption in a flexible manufacturing system (FMS), and we propose an approach based on the ant colony optimization (ACO) precisely the extension Rank-based ACO to optimal resource allocation and scheduling in FMS. The ACO meta-heuristic is inspired by the behavior of ants in foraging based on pheromones deposit. The numerical results show that the proposed algorithm obtained much better results than previous studies.

Blockchain brings many added values to modern business systems. However, Blockchain-based applications with massive IoT devices experience some limitations. The limitations are due to the linear structure and the consensus algorithms used in Blockchain that consume the participating nodes' considerable resources. In addition, IoT devices are generally with limited resources and have limited bandwidth connections. IOTA, based on Directed Acyclic Graph (DAG), is a new distributed ledger technology (DLT) for IoT devices. It proves its high scalability by providing parallel data processing. However, DAG is still not mature enough to fully replace Blockchain. In previous work, we proposed combining Blockchain and IOTA technologies to allow scalable transactions where Blockchain is employed in the backend, and Tangle is used in the frontend. This paper considers the proposed solution, focusing mainly on the connector part that intermediates both DLT technologies. The connector is a decentralized software component that supports the interaction between the DLT implicitly. The experiments' results show the flexibility to merge both DLTs using a message queuing protocol that enables smart contracts to run on the Tangle nodes and enriches the new platform with reliability and working offline features.

In recent years, Blockchain technology has proved its efficiency in many domains. A smart contract is a software component allowing exposing services via the Blockchain network. Smart contracts are small programs that automatically execute the terms of an agreement when predetermined terms and conditions are reached. With the increasing growth in the popularity of smart contracts, searching, retrieving and understanding smart contracts before executing them becomes a significant challenge. Nevertheless, the majority of these contracts are closed source contracts. Therefore, users cannot understand their functionality and their internal mechanism. In addition, their description lacks the expressiveness and it didn't cover the QOS parameters. In this paper, we propose a Uniform Description language for Smart Contract named UDL-SC. This proposal is an extension of USDL based on the MDA approach to promote trust and minimize the ambiguity between the user and the contract provider.

International audience

Security, Safety and Confidentiality in IoT for Collaborative Business Technologies (SSCIoT) is a track of the 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE-2018). SSCIoT aims to present mature research results or work-in-progress in all areas focuses security in IoT architecture based on business process, Business Analytics, Data fusion, Physical infrastructures of information systems and other fields. This report summarizes the content of accepted papers for oral presentation in SSCIoT track.

Behavioral consistency of cloud architectures is one of the pivotal challenges in cloud computing. In this paper, we propose a new approach for checking the behavioral consistency of the topology and orchestration of cloud computing. To do so, we choose TOSCA language in order to describe the cloud application. Then, we exploit the Wright ADL that encompasses the CSP language to check the consistency of cloud architectures using FDR2 model-checker.

Service Component Architectures (SCA) is widely used for the integration of heterogeneous applications. This heterogeneity, added to the distributed aspect of SCA, can be a source of behavioral mismatches. In this paper, we present a new approach for behavioral verification of SCA software architectures. More precisely, we propose a translation of a SCA source model to a Wright ADL configuration. In order to facilitate this translation, a guiding script and translation rules are introduced. Then, by using the wr2fdr tool, the translation of this wright configuration to a CSP specification is done. Finally, the FDR2 model-checker simulates and checks the obtained specification.

Currently, much research looks at treating the behavior properties beginning with the architectural design phase in SCA (Software Component Architectures) based applications. In this paper,we propose to map SCA onto the Wright ADL in order to verify the behavioral consistency of SCA software architectures. To achieve this goal, we suggest translating this source software architecture into a Wright configuration. Using Wr2fdr tool, this Wright configuration can be automatically translated to a CSP specification acceptable by the FDR2 model-checker.

Iris recognition system captures an image of an individual's eye. In addition, the process of segmentation, normalization and feature extraction is followed by the iris of an eye image in the system. Using the algorithms proposed by J. Daugman, Iris recognition system has significantly improved over the last decade, and it has been used in so many practical applications. However, some difficulties related to Iris position and movement are still to be improved. To overcome these difficulties one can enhance the image acquisition process. Obtaining a method in extracting quality of eye images automatically from the video stream is the main area of interest in this study. Besides, a Bayesian inference solution called Bayesian Tangent Eye Shape Model (BTESM) was suggested depending on estimation of tangent shape. During image acquisition, constraints on the position and motion of the subjects can be decreased owing to this approach. Owing to maximum a posteriori estimation, we can identify similarity transform coefficients as well as the eye shape parameters in BTESM. To apply the maximum a posteriori procedure, tangent Eye shape vector was considered the state of the model which is hidden and expectation maximization depending on searching algorithm was adopted. Hence, after being tested and matched to future studies, the acquisitioned eye image has been proved to be adequate for Iris recognition system.

E-government initiatives utilizes Information and Communication Technology to improve public services and information access to citizens and users. This paper presents a description for the role of web sites in the context of the e-government perspective. Within this context, the core tasks of a web site of a public entity are proposed along with guidelines for implementation. Based on the core tasks proposed in this research, guidelines and tools are described to guide assessing public sector websites. Five websites are evaluated according to this paper proposal and comparison results are presented.