5 scientific tracks

The scientific tracks of LIST3N that have been collectively defined by taking stock of the activities of the four previous research groups (ERA, LOSI, M2S, Tech-CICO). These five tracks reflect the main scientific areas that LIST3N cover, independently of the application domain (which are constantly evolving).


In this axis, the aim is to respond to new scientific challenges in several fields such as information and communication technologies (ICT), and various application areas such as industry 4.0 or energy. Today, we are talking about everything that is connected (humans, cars, drones, etc.). The social fabric is growing and becoming in the digital world a social network on different software platforms. Technologies are evolving rapidly and the challenges are high demand, low latency, high performance, resource optimisation, high efficiency and so forth. Everything has to become intelligent. Artificial intelligence (AI) is being integrated wherever possible and feasible and we are talking about smart cities, smart grids and smart agronomy to name just a few examples that exist today. AI is also used in the cyber world, based on computer networks, with a cyber economy around crypto-currencies, the dark web, malicious code... AI has to learn, block attacks, and even react. The rapid evolution of technologies can be a source of fear (example of the blocking in France of the deployment of Linky connected meters and more recently of the new generation of cellular networks the new generation of 5G cellular networks). It is therefore essential to study at least the acceptability of technologies by their users. These technologies often aim to bring more comfort and convenience to the human being and the community. For this reason, some of the classic challenges concern self-configuration, self-optimisation, self-protection and self-repair. The increase in the number of connected devices and users, accompanied by the de facto increase in the volume of data exchanged, makes the scientific questions to be addressed in this area much more complex than before.

Data processing

This area is focusing on the development of emerging data processing and artificial intelligence methods. With the ever-increasing influx of data from multiple and heterogeneous sensors, the use of conventional signal processing methods (even the fastest ones) is becoming limited or even inefficient. It is becoming essential to develop new signal and image processing methods that can adapt to this context. Part of the work in this area will be devoted to machine learning. The objective is to develop machine learning methods to optimise a complex system. In this context, the methods will have to take into account various characteristics : heterogeneous data, structured data, missing data, large volume of data, absence or volume of data, absence or partial knowledge of the data generation model, etc. Another part of this scientific axis will concern the development of parametric methods for the detection and localisation of abrupt changes between two (or more) probability laws and statistical tests between two (or more) hypotheses. The main objective is to contribute to the optimisation of the performance of decision methods, in particular in the case of monitoring dynamic systems and sequential detection in non-stationary signals. The design of monitoring algorithms requires the mastery of two essential requirements which are, on the one hand, a sufficient "sensitivity" to the parameters of interest (failures, anomalies, targets...) that one wants to detect and localise and, on the other hand, a sufficient "insensitivity" to the nuisance parameters (disturbances, non-stationarity, errors and uncertainties).


The axis focuses on the modelling, performance evaluation and optimisation of systems, whether they be production, logistics, transport, health or simply organisational. The scientific objective is the development of resolution methods that are efficient in terms of performance and fast in terms of calculation time. These methods can be of the exact or approximate type with single or multi-criteria optimisation objectives. The optimisation axis relies on skills in operational research (mathematical, linear, non-linear, integer or binary linear, integer or binary programming) and in computer programming. The problems studied are combinatorial problems characterised by different decision horizons ranging from strategic (long-term decision) to operational (short-term decision) and tactical (medium-term decision). Among a multitude of problems studied, we can mention entities, cell layout, planning or scheduling of activities, dimensioning and activities, dimensioning and vehicle routes. The major socio-economic issues such as the digitalisation of companies, industry 4.0, energy or health bring important scientific challenges and the optimisation axis will be able to respond to them by developing innovative and efficient resolution methods. The scientific challenges arising from these issues concern both performance evaluation models and optimisation methods. These challenges include the creation of models with stochastic variables, the use of multi-criteria decision methods, the taking into account of numerous and often heterogeneous constraints (technological, organisational, energetic, social constraints, etc.) and the need to take into account and move from cost models to profit models. The current scientific orientation also concerns the treatment of combined problems in the decision horizon (short/medium term or medium/long term) but also the treatment of real time problems such as production scheduling in industry 4.0. The development prospects of this scientific axis are therefore very promising.

Dependability, reliability, and maintenance 

This scientific axis mainly aims at developing stochastic approaches for the modeling of operational safety problems. The optimal management of a complex system (whatever its nature: nuclear reactor, manufacturing production line, turbojet production line, oil platform and submerged pipelines, transport systems, waste storage, etc.) throughout its life cycle (design, operation, dismantling) requires a compromise between the often contradictory objectives of economic performance (costs, benefits) and operational safety (reliability, availability, etc.), with minimal risk for the system itself, but also for the population and the environment). In order to provide decision support elements for this problem and to respond to an increasingly pressing socio-economic demand for the safety and security of industrial installations and technological systems, it is necessary to have tools and methods for analyzing systems, as well as models for the quantitative evaluation of safety performance (reliability and maintenance). We propose to work on two complementary themes associated, on the one hand, with the proposal of reliability and prognostic models allowing a reinforced consideration of the dependencies between components and describing the links between the operating conditions of a system and its aging, and on the other hand, the development of conditional or predictive maintenance models adapted to these new models and integrating imperfect maintenance. The main objective of this last point of view is to continue to progress towards the construction of dynamic maintenance models, integrating prognostic indicators and allowing a synergy of monitoring and maintenance activities. Faced with the increasing complexity of the problems encountered in operational safety, we adopt a multidisciplinary approach that seeks to rely on techniques and methods from both the engineering sciences and mathematics.

Technologies and practices

This track deals with collectives that interact, coordinate and cooperate using technologies (social media, information systems, augmented reality document management systems, connected objects, platforms) that are not always adapted to their practices. How do these collectives organise themselves? What are the possible uses of these technologies, within working groups, communities ? How can we design technologies that allow these collectives to organise themselves in a way that is adapted to their practices? These questions can be asked in the healthcare domain, for caregivers, in Industry, for industrial process control teams, digital designers, for citizens and society, with territorial organisations, groups of experts, the collaborative economy, or makers. In this scientific track, we will describe in detail the practices within existing or emerging collectives - mediated or not by technologies -, propose concepts to characterize the practices, and (re)design technologies that support these practices, whether they are architectures, systems, or interaction techniques. In this scientific track, we will analyse how different technologies are 'put into practice' by different actors, in different places. Technologies in practice reflect an absence of stability and represent a "set of rules and resources that are (re)-constituted during the recurrent engagement of actors with the technologies that are proposed to them" (Orlikowski 2000, p. 407). This line of research is part of a tradition of research aimed at demystifying technological determinism, following numerous studies conducted on technologies at work by ethnomethodologists, sociologists or organisational specialists (Suchman 1983; Orlikowski 1992; Heath et al., 1994). By crossing ICST and SHS research, the research in this axis aims to reduce the socio-technical gap ("the great divide between what we know we must support socially and what we can support technically" (Ackerman 2000)). The research results of the projects carried out within this track therefore aim to propose appropriate technologies for more efficient and innovative collectives and better conditions for carrying out their activities.
Date of update 20 février 2024