Posts from 2020-07

ECONECT: Developing connected environmental sentinel systems to better understand the degradation of rivers, the decline of bees and birds

Context Presentation

The ECONECT project began in early 2020, with the objective to develop a communication infrastructure allowing the remote monitoring of autonomous, connected, and versatile systems to measure the responses of bioindicator organisms to chemical contamination, habitat degradation and global warming.

Three sentinel systems are considered:(1) the connected hive, allowing to monitor the dynamics of bee colonies (colony mass, temperature and location of the bee cluster, foraging traffic, etc.) and the cognitive capacities of bees; (2) the connected bird-feeder to submit individually monitored tits to behavioral tests to assess their cognitive abilities; (3) the aquacosm, a floating enclosure allowing the measurement of eco-markers in an aquatic environment (growth dynamics of phototrophic biofilms, relative importance of autotrophic and heterotrophic processes within the ecosystem ...).

In 2022, a network of 12 sentinel stations will be deployed in the Zone Atelier Pyrénées-Garonne (PYGAR). Each station will be characterized by a spatial analysis of land use and the quality of habitats and by the measurement of concentrations of chemical contaminants (trace metal elements, PAHs, pesticides) in different compartments of the environment. Participatory science protocols will be used to supplement the available data set and to assist in the interpretation of observed trends, while providing environmental education opportunities for the public.

schema (EN) - Arnaud Elger


Environmental sensor; Bioindicator; Animal cognition; Chemical status; Landscape integrity; Artificial intelligence

Scientific goals

•    to design a communicating infrastructure to collect data from different sensors in the field;

•    to develop automated tools for the real-time analysis of collected data, for extracting their ecological significance;

•    to examine the relevance of our sentinel systems to assess the quality of the environment, particularly in terms of chemical status and landscape integrity.



Model Self-Calibration using Self-Adaptive Multi-Agent System

Context Presentation

The purpose of this project is to propose a cooperative agent model, based on the self-adaptive multi-agent system theory (AMAS), allowing an efficient and fast exploration of the parameter space, autonomously and automatically. This exploration should allow a continuous readjustment of the simulation until convergence, improving the control of the macro-level over the micro-level.

On an application standpoint, the purpose of this project is to produce a realistic traffic that satisfies the best a set of objectives and constraints at both micro and macro levels. This traffic should also allow interaction with humans and adapt to events that could occur in the virtual environment. 

CALICOBA_simple - Darmo


Self-adaptive Multi-agent Systems, Self-Calibration, Multi-Agent Simulation

Scientific goals

1. Enrich the AMAS theory with general learning mechanisms andstrengthen the coupling between micro and macro levels.

2. Propose a new generic calibration method of models.

3. Enrich GAMA tools


A Smart Clean Garden for Toulouse 3 University

Context Presentation

This project is built in cooperation with Epurtek factory and INRAe who displays planted filters in Occitany, France and other countries. The actual network extends with the help of the regional GIS ‘EAU Toulouse and the national group of water Re-Use from Aqua-Valley and CapEnergy platforms. Major research and innovation issues are: 

1. To obtain and feed a pluri-topic data base from environmental sensors settled down into the filters for in-field, providing a spatially and temporally improved datasets that allows the implementation of deterministic models and open the black boxes of the filters sediment and soil. These models of transport and reaction (ex MIN3P) will describe the water and pollutants flows through the filters. This task started from cooperation between the research labs (UMR ECOLAB) at Toulouse, the university of La Rochelle, and UMR ECO&SOL from Montpellier.

 2. To demonstrate how the invertebrate addition in the filters will improve the purification efficiency in agreement with the biodiversity influence that governs natural ecosystem functioning. Research hypotheses will be tested in filter replicates at the level of lab microcosms, in campus pilots and across-campus comparisons.

 3. The assessment of biodiversity effects on filter benefits and services in terms of water quality providing, water quantity resource management, energy cost, recreational area for campus users, teaching and innovation supports.

 4. To provide a new generation filter that matches with the requirement of the recent European policies about water reuse quality and may be recognized as a new technology for local water cycle in our territories

Diapositive1 - magali Gerino


Planted Filter, Water, Biodiversity

Scientific goals

This garden provides the potential to become an outdoor living lab as a demonstrator of sustainable and low carbon solution for wastewater treatment and recycling though nature-based solution. The main challenge is to demonstrate the advantages of having a biodiverse and smart clean garden on a campus or a smart city, in terms of environment, economy, society (quality of life) and energy, by comparison with the classic filters. This implies an adaptative management of the water resource in cooperation with other research laboratories and stakeholders, as public water managers and private factories that wish to favor the ecological and energetic transition in this field.


Appel a projet PIA3 "Territoires d'innovation pédagogique"

Le projet PIA Campus BTP et usages du numérique au travers de son porteur de projet, le lycée le Garros, vient d'être retenu sur l'appel à projet PIA. Nous avons contribué sur une fiche plateforme d'expérimentation sur un bâtiment à hauteur de 1 Million d'euros.

Lire le communiqué :

cp_orientation_et_formation_campusco_orientation_cmq (1)

Dynamic Learning of the Environment for Eco-Citizen Behavior

Context Presentation

The development of sustainable smart cities requires the deployment of Information and Communication Technology (ICT) to ensure better services and available information at any time and everywhere. As IoT devices become more powerful and low-cost, the implementation of an extensive sensor network for an urban context can be expensive.

This thesis addresses the problem of estimating missing information in urban contexts. The objective is to estimate accurate environmental information where physical sensors are not available. The proposed solution, HybridIoT, uses the Adaptive Multi-Agent System (AMAS) to estimate accurate environmental information under conditions of uncertainty arising from the urban application context in which the project is applied, such as openness, heterogeneity and large-scale, which have not been explored by the state-of-the-art solutions.

illustration - Davide Guastella


Smart city, Cooperative Multi-Agent Systems, Missing Information Estimation, Heterogeneous Data Integration

Scientific goals

- Limiting the number of ad hoc devices to be deployed in an urban environment

- The exploitation of heterogeneous data acquired from mobile, intermittent devices

- Real-time processing of information

- Self-calibration of the system


Toward a Data Lake

Context Presentation

neOCampus is a large operation with different kinds of projects and actors. Started in 2013, its goal is to improve the university campus user’s everyday life through data analysis for people, fluid consummation reduction, reduce building environmental footprint, etc.… Overall, it tends to make the campus smarter. All those projects have one common point: data. Including images, sensor logs, administrative data, configurations, we can find every kind of data and each must be stored somewhere.

This project is centered around this problem with a data management system architecture which is the data lake.The conception of this kind of solution must include handling every kind of data and making it possible to follow the life of a data from the input to the usage in a project. It does not only have to store every kind of data, it is needed to know what is stored, where and in the proper format to use it in the easiest way. When a new data has arrived, the system will automatically rawly store it, find the more valuable format, extract information from this data and make this knowledge available for any purpose.

datalake - Vincent-Nam Dang


Data Lake, Data Driven Project, Big Data, Data Management, Data Analysis

Scientific goal

•    To develop a datalake architecture to change the architecture of the data management system in neOCampus.

Contacts, franç,

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. Our case study is based on a real context: sensor data from the SGE (Rangueil campus management and operation service in Toulouse).

We propose an automatic pattern-based method for anomaly detection in time-series called Composition-based Decision Tree (CDT). We use a modified decision tree and Bayesian optimization to avoid manual tuning of hyper-parameters. Our method uses sequences of patterns to identify remarkable points corresponding to multiple anomalies. The compositions of patterns existing into time-series are learned through an internally generated decision tree and then simplified using Boolean algebra to produce intelligible rules.

Our approach automatically generates decision rules for anomaly detection. All our experiments were carried out on real and synthetic data. We show that our method is precise for classifying anomalies compared to other methods. It also generates rules that can be interpreted and understood by experts and analysts, who they can adjust and modify.

Image_IBK - ines ben kraiem


Anomaly detection, Time-series, Machine learning, Classification rules

Scientific goals

•    To detect different types of anomalies observed in real deployment

•    To generate interpretable rules for anomaly detection

•    To use learning methods for anomaly detection on static and continuous data


Collective Learning for Robotics

Context Presentation

The current digital transformation requires the creation of autonomous applications that can be adapted to complex, dynamic, heterogeneous, and unpredictable environments. These systems must be equipped with proactive learning capabilities.

To this end, Self-Adaptive Multi-Agent principle allows the decentralization and self-observation of the learning process. Each knowledge granule is an autonomous agent that cooperates with its neighbors to improve learning from exogenous and endogenous feedbacks. Detecting and solving concurrences, conflicts and incompetence leads to active endogenous learning.This work on an adaptive decentralized learning mechanism is applied to the learning of a robotic arm inverse kinematics.

Illustration Bruno Dato - Bruno Dato


Self-Adaptive Learning, Endogenous Learning, Adaptive Multi-Agent Systems, Robotics

Scientific goals

•    Design a Self-Learning System

•    Lifelong and Endogenous Learning

•    Genericity and Scalability


SANDFOX Project: Optimizing the Relationship between the User Interface and Artificial Intelligence to Improve Energy Management in Smart Buildings

Context Presentation

This research project deals with energy efficiency in buildings to mitigate the climate change. Buildings are the highest source of energy consumption worldwide. However, a large part of this energy is wasted, mainly due to poor buildings management. Therefore, being accurately informed about consumptions and detecting anomalies are essential steps to overcome this problem. Currently, some software exists to record, store, archive, and visualize big data such as the ones of a building, a campus, or a city. Yet, they do not provide Artificial Intelligence (AI) able to automatically analyze the streaming data to detect anomalies and send alerts, as well as adapted reports to the different stakeholders.

The system designed in the SANDFOX project has for objective to fill this gap. To improve the energy management, an innovative system should aim at visualizing the streaming data, editing reports, and detecting anomalies, for different stakeholders, such as policy makers, energy man-agers, researchers, technical staff or end-users of these buildings.

The paper presents the User-Centred Design approach that was used to collect the required needs from different stakeholders. The developed AI system is called SANDMAN (semi-Supervised ANomaly Detection withMulti-AgeNt systems). It processes data in a time constrained manner to detect anomalies as early as possible. SANDMAN is based on the paradigm of self-adaptive multi-agent systems. The results show the robustness of the AI regarding the detection of noisy data, of different types of anomalies, and the scaling.

 SANDFOX_image-neocampus2020 - Berangere Lartigue


Anomaly detection, dashboard, multi-agent system, smart buildings, energy management

Scientific goals

•    Anomalies detection in smart buildings streaming data by AI,

•    Restitution of the information to different stakeholders through an adapted dashboard.


Embedded Multi Gas Sensors for Indoor Air Quality Monitoring

Context Presentation

The measurement of indoor air quality is important for health protection against chemical and gaseous pollutants ... The indoor air can contain many pollutants such as CO, CO2, NO2 and VOCs. These pollutants exist in different materials and products that can be used in housing (furniture, cleaners ...), but can be also coming from human activities or outside source. In this case, the detection, measurement and monitoring of these gazeuse contaminants is necessary.

In view of its high performance and low cost, the innovative gas multi-sensor based on metal oxides semiconductors for analyzing and controlling indoor air quality is a good alternative to electrochemical and infrared sensors. This project is currently in progress in LAAS in collaboration with the LCC and Laplace and as part of a thesis funded by neOCampus and the Occitanie region.

This thesis focuses on the characterization of multiple MOX-based gas sensors and integrates these multi-sensors in electronic card to achieve a connected object to control the indoor air quality in offices and classrooms in University Paul Sabatier in Toulouse. The gas multi-sensor is a microsystem composed by four sensors on a microchip, realized to detect target gases.  



Multi-sensors, MOS, Indoor Air Quality, Smart Building, neOCampus

Scientific goals

•    To characterize new nanomaterials (SnO2, CuO, ZnO, WO3 ...) designed by the LCC by using an experimental set-up,

•    To define an operating protocol by trying different operating modes.


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