Posts from 2019-07-09

SandFox: Dashboard for monitoring the energetic consumption

Context Presentation

In a context of energy transition and reduced energy consumption, the SandFox project is a collaborative project between IRIT and Berger-Levrault. This project aims to design a dashboard that displays these energy consumptions and facilitate data analysis and comparison. It also notifies users of detected anomalies on energetic data.

SandFox is a web application based on user-centered design. The dashboard offers an interactive map with the ability to select buildings and display multiple data curves based on selected buildings. This application is developed with Angular, typescript and D3.js.

The research on this project aims to study different visualizations and interactions to facilitate the comparison of buildings and/or periods on the timeline. One proposed solution is a spiral heatmap representation that displays and compares different periods.


Figure 1 : « SandFox dashboard »

Scientific Goals

- The main goal of the project is to study the different ways of visualizing and interacting with the data for :

- Displaying information on energy consumption for different users

- Analyzing and comparing these data


neOCampus, Energy Consumption, Dashboard, innovation, Data Visualization, Data Interaction, Human–computer interaction

Contacts / / /

Emergent User-Centered Services in Ambient and Smart Environments

Context Presentation

Cyber-physical and ambient systems surround the human user with services at her/his disposal. From these services, complex composites services, tailored to the user preferences and the current situation, can be composed automatically and on the fly. In order to produce the knowledge necessary for automatic composition in the absence of both prior expression of the user's needs and specification of a process or a composition model, we develop a generic solution based on online reinforcement learning. It is decentralized within a multi-agent system in charge of the administration and composition of the services, which learns incrementally from and for the user.


Figure 1: Opportunistic Software Composition

Scientific Goals

- Design a decentralized and distributed system that learns and decides on compositions

- Consider user preferences and context


Ambient intelligence; Service discovery, selection and composition; Multi-agent system; Machine learning; Smart city; neOCampus

Contacts – –,

Acceptability Conditions for Human to Cooperate with an Autonomous Driving System

Context Presentation

This internship concerns autonomous vehicles of level 3. A level 3 vehicle is only capable of taking full control and operating given specific conditions. However the vehicle is not fully autonomous and the human driver must remain vigilant even in the autonomous driving phase as he can be asked at any moment to retake control over the vehicle in a short amount of time in the event of a failure (situation too complex for the vehicle to handle for example). It is therefore important to make sure that the human driver is always in a state fit to take back control.

In this context, our goal is to design a system capable of observing the human driver while using the vehicle in various situations and to extract a personal profile from these observations that will then be used to measure the attention of the human driver. In the case of low attention, the system will also need to act by either trying to increase the attention of the human driver, or telling the autonomous vehicle that the human driver is not fit to take back control at a moment's notice.


Figure 1 : « A distracted driver in an autonomous vehicle »

Scientific Goals

- to understand and define the intrinsic and contextual factors defining the driver's attention level

- to design a multi-agent system to estimate at runtime the human driver's attention level and to maintain it based on contextual information

- to define and implement an experimental protocol to evaluate the proposed multi-agent system.


Autonomous vehicle, cooperative multi-agent system, dynamic learning, synthetic environment.


Interactive data physicalization: “phygitals” Interaction techniques for data exploration

Context Presentation

Over the last decades, the amount of data has increased to 29000 Go produced each second. Understanding the data requires tools to transform these numbers, texts and images into concrete representations. The field of data visualization aims to produce data representation to visualize and analyze abstract data. Building, people or vehicles produce a lot of data collected by many sensors. These specific data are related to a physical location (e.g. number of people in a room is related to the room, humidity in a floor is related to the floor, etc.) Bring and display them close from their physical context allow people to make a better representation of the data (Embedded Data Representations, Willet et al. , 2017)

In this project we aim to design interaction techniques to navigate and manipulate the data close to a physical referent. The main goal is to develop a full interactive physical model of the campus endowed with situated data.


Figure 1 : (left) TouchGlass project (Cabric et al. 2019) and (right) the design space of interaction techniques for physical referents endowed with situated data

Scientific Goals

- Conceptualize the Interaction Techniques With Situated Data and establish a design space

- design and evaluate interaction techniques with situated data

- Which interaction techniques could be designed to support an interactive scaled model of the campus with situated data ?


Phygital, model , situated data, design space

Contacts ; ; ;

Design of complex systems based on interoperable heterogeneous systems

Context Presentation

When a complex system requires the use of different components specified by different designers working on different domains, this greatly increases the number of virtual prototypes. These different components unfortunately tend to remain too independent of each other, thus preventing both different designers from collaborating and their systems from being interconnected to perform one or more tasks that could not be accomplished by one of these elements only. Co-simulation is the coupling of several simulation tools where each manages a part of a modular problem that allows each designer to interact with the complex system in order to maintain his business expertise and continue to use his own digital tools. For this co-simulation to work, the ability to exchange data between tools significantly, called interoperability, is required. We participate in the design of a co-simulation system that integrates di ff erent tools of simulation-trades based on the modeling of the behavior of devices like energy simulation and the simulation of wear of building materials within the same platform


Figure 1 : « Co-simulation architecture using dynamic data mediation »

Scientific Goals

- Take into account the concepts of architecture, communication (between simulators or with users) and visualization to define architectural models

- Architecture analysis managing interoperability

- Validation of this architecture and development of a tool for verifying certain properties of the architecture, such as coherence and semantics


neOCampus, Interoperability, Mediation, Co-Simulation, Adaptive Multi-Agent Systems


Stream Analysis and Filtering for Reliability and Post-processing of Sensor Big data

Context Presentation

Anomaly detection in real fluid distribution applications is a difficult task, especially, when we seek to accurately detect different types of anomalies and possible sensor failures. Resolving this problem is increasingly important in building management and supervision applications for analysis and supervision. Our case study is based on a real context: sensor data from the SGE (Rangueil campus management and operation service in Toulouse).

We propose CoRP” Composition of Remarkable Points” a configurable approach based on pattern modelling, for the simultaneous detection of multiple anomalies. CoRP evaluates a set of patterns that are defined by users, in order to tag the remarkable points using labels, then detects among them the anomalies by composition of labels. CoRP is evaluated on real datasets of SGE and on state of the art datasets and is compared to classical approaches.


Figure 1: « Anomaly Detection in Sensor Networks »

Scientific Goals

- Detect different types of anomalies observed in real deployment

- Improve the supervision of sensor networks

- Use learning methods for anomaly detection on static and continuous data



neOCampus, Sensor Data, Univariate Time Series, Anomaly Detection, Pattern-based Method


Real-time distributed optimization of energy management in smart grids

Context Presentation

RennesGrid is an energy transition project in the Ker Lann business park in Bruz. In particular, this project focuses on self-consumption integrating photovoltaic panels, storage devices and energy data collection. As part of this project, this thesis aims to implement a multi-agent system managing the consumption of flexible loads, particularly electric vehicles, and the production of power sources (photovoltaics).The smart grid concept is driving an explosion in the number of controllable units (flexible loads, decentralized producers, storage units, etc.). In addition, issues related to energy management in the smart grid, whether local (voltage control at bus level, congestion control) or global such as managing the balance between consumption and production, make the problem strongly linked.

The flexibility of adaptive multi-agent systems is relevant to this issue. Indeed, it enables to manage a dynamic environment (consumption, production, power grid...). It is also open and robust. Thus, it is able to adapt to the ever-increasing energy demand and the need to keep the power grid in service, particularly when an incident happens.


Figure 1: Smart grid concept

Scientific Goals

- The realization of a micro grid simulator and a scenario generator

- The design and evaluation of an adaptive multi-agent system managing a micro grid


neOCampus, smart grid, optimization, multi-agent systems


Impact of spatial strategies of bees on colony performance

Context Presentation

Foraging for food to substantiate one’s needs is of great importance for every species. In the case of bees, who are a social species, only a small selection of individuals has the task to bring the food for the whole colony, and thus has to take into account the needs of the entire population in terms of nutrients. As central place foragers, bees will explore and exploit flowers around their nest, where different species provide bees with different amounts and qualities of nectar. Bees are as a result faced with a complex problem: finding flowers that are not already exploited by other bees, which provide the nutrients in the right amount (either by foraging on a single species of flowers with a balanced diet, or on multiple species with unbalanced but complementary diets), and create a stabilized exploitation route between them. Following each individual bee in its foraging trip has been a technological challenge. However, today, as different tracking technologies (radars, camera tracking) are being developed, assisted with colony monitoring systems (connected hives), we can finally get some insights on these complex behaviors. As data are still scarce and only available in limited, simplified situations, building theoretical models that successfully replicate the spatial strategies of bees will allow us to make predictions on more complex and ecologically relevant scenarios.

Scientific Goals

- Conduct experimental tests for the fundamental hypotheses of the behavior.

- Build a new model based on experimental tests of simple situations and theoretical knowledge of bee foraging behavior.

- Test the model’s predictions in complex environmental situations.


Spatial strategy, foraging behavior, nutritional geometry, connected hive


RECOVAC: conditions for REtaking COntrol by self-observation of situations within a connected autonomous vehicle

Context Presentation

Connected autonomous vehicles of level 3 are vehicles in which the human driver delegates driving control in specific situations. During these situations, it may be necessary for the human to regain control of the driving activity. The main objective of this thesis is to develop a system for the safe and efficient transition of two-way control between the human and the autonomous vehicle.

For this, the system must identify by self-observation and in real time situations in which the current driver will no longer be able to ensure driving. He must also provide a context for assessing the criticality of the situation as quickly as possible in order to anticipate and react to it as best as possible. The driving context is composed of indicators that characterize the elements that describes part of the driving process. The system is based on self-adaptive multi-agent learning systems.


Figure 1: Dynamic Learning using self-adaptive multi-agent system

Scientific Goals

- Dynamic learning using multi-agent systems

- Generic approach to supervise the activity of a system

- Insure the acceptability of the system by human driver


self-adaptive multi-agent systems, autonomous vehicle


Fleet of Autonomous and Connected Vehicles by Adaptive Multi-Agent System

Context Presentation

The theme addressed in this thesis concerns the autonomous and connected vehicle. Its reliability must be proven from the technological point of view, which is what most projects focusing on technological developments answer. It is essential, after having made a vehicle reliable, to study how several connected autonomous vehicles will be able to interact in order to maximize the safety of the collective. The American Society of Automotive Engineers (SAE International) has defined 5 levels of autonomy: from level 1, in which the driver performs all the maneuvers, to level 5, in which the driver is completely autonomous. This thesis concerns both vehicles with a level of autonomy 4 in which the vehicle drives, and the human driver supervises and can take over the control, as well as vehicles with a level 5 (total vehicle autonomy).

In this thesis, the aim is to study how each vehicle communicates with its neighboring vehicles and how it behaves/reacts in response to the information provided by its neighborhood. A lock consists in particular in determining which information is relevant to communicate among all the data recovered from the many sensors scattered in the vehicle and the effectors.


Figure 1: « A fleet of autonomous and connected vehicles »

Scientific Goals

- Determine which information is relevant

- How to adapt to each traffic situation

- Take the non-connected users in account


Autonomous and connected vehicle, Multi-Agent System, Smart Traffic


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