The interaction between humans and AI in safety-critical systems presents a unique set of challenges that remain partially addressed by existing frameworks. These challenges stem from the complex interplay of requirements for transparency, trust, and explainability, coupled with the necessity for robust and safe decision-making. A framework that holistically integrates human and AI capabilities while addressing these concerns is notably required, bridging the critical gaps in designing, deploying, and maintaining safe and effective systems. This paper proposes a holistic conceptual framework for critical infrastructures by adopting an interdisciplinary approach. It integrates traditionally distinct fields such as mathematics, decision theory, computer science, philosophy, psychology, and cognitive engineering and draws on specialized engineering domains, particularly energy, mobility, and aeronautics. Its flexibility is further demonstrated through a case study on power grid management.

With the emergence of Connected and Smart Cities, the need to predict traffic conditions has led to the development of a large variety of forecasting algorithms. In spite of various research efforts, the choice of models and techniques strongly depends on the use case, the highway infrastructure as well as the provided dataset. This study is launched as part of a project which aims to design an Intelligent Transport System (ITS) dedicated to highway supervisors to regulate traffic. This system needs to be supplied by continuous, real-time forecasting of short-term traffic congestions in order to make decisions accordingly. In this paper, we propose a general framework that, first, performs different data preprocessing techniques to improve data quality, and second, provides real-time multiple horizons predictions. Our framework uses different models combining Machine learning and Deep learning algorithms. Experiments results confirmed the necessity of the data preprocessing step, especially with highly dynamic data and heterogeneous mobility contexts. In addition, our methodology is tested in a real case study and shows very encouraging results.

The Information-Centric Networking (ICN) paradigm is shaping the foreseen future Internet architecture by focusing on the data itself rather than its hosting location. It is a shift from a host-centric communication model to a content-centric model supporting among others unique and location-independent content names, in-network caching and name-based routing. By leveraging the easy data access, and reducing both the retrieval delay and the load on the data producer, the ICN can be a viable framework to support the Internet of Things (IoT), interconnecting billions of heterogeneous constrained objects. Among several ICN architectures, the Named Data Networking (NDN) is considered as a suitable ICN architecture for IoT systems. However, its default caching approach lacks a data freshness mechanism, while IoT data are transient and frequently updated by the producer which imposes stringent requirements in terms of information freshness. Furthermore, IoT devices are usually resource-constrained with harsh limitations on energy, memory and processing power. We propose in this paper a caching strategy and a novel cache freshness mechanism to monitor the validity of cached contents in an IoT environment while minimizing the caching process cost. We compared our solution to several relevant schemes using the ccnSim simulator. Our solution exhibits the best system performances in terms of hop reduction ratio, server hit reduction ratio and response latency, yet it provides the lowest cache cost and significantly improves the content validity.

This article describes how the named data networking (NDN) has recently received a lot of attention as a potential information-centric networking (ICN) architecture for the future Internet. The NDN paradigm has a great potential to efficiently address and solve the current seminal IP-based IoT architecture issues and requirements. NDN can be used with different sets of caching algorithms and caching replacement policies. The authors investigate the most suitable combination of these two features to be implemented in an IoT environment. For this purpose, the authors first reviewed the current research and development progress in ICN, then they conduct a qualitative comparative study of the relevant ICN proposals and discuss the suitability of the NDN as a promising architecture for IoT. Finally, they evaluate the performance of NDN in an IoT environment with different caching algorithms and replacement policies. The obtained results show that the consumer-cache caching algorithm used with the Random Replacement (RR) policy significantly improve NDN content validity in an IoT environment.

Information-Centric Networking (ICN) is an emerging network paradigm based on name-identified data objects and in-network caching. Therefore, ICN contents are distributed in a scalable and cost-efficient manner. With the rapid growth of IoT traffic, ICN is intended to be a suitable architecture to support IoT networks. In fact, ICN provides unique persistent naming, in-network caching and multicast communications which reduce the data producer load and the response latency. Using ICN in an IoT environment requires a study of caching policies in terms of cache placement strategies and cache replacement policies. To this end, we address, in this paper, caching challenges with the aim to identify which caching policies are suitable for IoT networks. Simulation findings show that the combination of the consumer-cache caching strategy and the RR cache replacement policy is the most convenient in IoT environments in terms of hop reduction ratio, server hit reduction and response latency.

Information-Centric Networking (ICN) is a new paradigm proposing a shift in the main Internet architecture from a host-centric communication model to a content-centric model. ICN architectures target to meet user demands for accessing the information regardless of its location. A major building block of ICNs concerns caching strategies. Concomitantly, Machine-to-Machine (M2M) technologies are considered the main pattern for the Internet of Things (IoT). Unifying M2M and ICN into a single framework raises the challenge of cache coherence. In this paper, we propose a novel cache coherence mechanism to check the validity of cache contents. We also propose a caching strategy suitable to M2M environment. Extensive experimentations are conducted to evaluate the performance of our proposals. They show that the combination of our two proposed schemes results in a notable improvement in content validity at the expenses of a certain degradation in both server hit and hop reduction ratios.