Impacts assessments of lighting systems

LAPLACE / LERASS – University Toulouse III Paul Sabatier

Context presentation

When it comes to evaluate the quantifiable effects of products or services on the environment, Life Cycle Assessment (LCA) is probably the most efficient and recognized tool. Thanks to a “cradle to grave” approach, LCA identifies and quantifies, throughout the life of products, the physical flows of matter and energy associated with human activities (extraction of raw materials, manufacturing of the product, distribution, use, collection and disposal towards end-of-life). For each of its flows correspond impact indicators which allow to establish the overall potential impact of the system on our environment.

With regard to lighting, ultra-efficient lighting have made it possible to improve energy efficiency during use phase and thus greatly limit its impact on the environment. Before the development of these new technologies, lighting represented 14% of European consumption and 19% of global electricity consumption (2009). Today, the UNEP (United Nations Environment Program) estimates it at 15 % worldwide (2,940 TWh) for 5% of global greenhouse gas emissions. In France, the total electricity consumption due to lighting is 56 TWh, emitting 5.6 million tonnes of CO2 (Ademe - 2017).

However, despite major advances in terms of energy efficiency, many direct or indirect impacts on our environment, our health, well-being and productivity are not considered, and we can no longer neglect these impacts.

It is then necessary to define a new methodology, which will allow the extension of the classic LCA by taking into account several economic, health and social criteria, in particular regarding the potential impacts on human (impacts on circadian rhythms); the impacts on ecosystems (light pollution); the several uses of light (residential, commercial, public lighting, etc.); or even social acceptability on and by the user of the system (security, comfort, working conditions, etc.).

The aggregation of these criteria, with a classic life cycle assessment and a life cycle cost analysis (cumulative cost of a product throughout its life cycle), will give a global vision (economic, social and environmental) of the potential impacts of lighting and will helps to define a decision support tool for establishing coherent and appropriate strategies around the transformation of our lighting systems.


LED, Lighting, Life Cycle Assessment (LCA), Life Cycle Cost (LCC), Efficacy, Lifetime

Scientific goals

- Define the characteristics of a LED lamp and in particular the duo [Lifespan

- Efficacy] for it to be considered the most efficient system according to the different energy mixes.

- Define the economic optimum for the lifetime of the lamps, depending on the type of use.

- Quantify and compare the circadian impact and light pollution with the impact categories of LCA.

- Evaluate the most efficient systems for horticultural lighting.


Dynamic and real-time Learning of the Environment for Eco-Citizen Behavior in Smart Cities

IRIT-SMAC/University of Palermo (Italy)

Context presentation

The development of sustainable smart cities requires the deployment of information and communication technology 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 project proposes a technique for estimating missing environmental information in large-scale environments named HybridIoT. Our proposal enables providing information whereas devices are not available for an area of the environment not sufficiently covered by sensing devices. The contribution of our proposal is summarized in the following points:

-    limiting the number of sensing devices to be deployed in an urban environment;

-    the exploitation of heterogeneous data acquired from intermittent devices;

-    real-time processing of information;

-    self-calibration of the system.

HybridIoT exploits both homogeneous (information of the same type) and heterogeneous information (information of different types or units) acquired from the available sensing device to provide accurate estimates in the point of the environment where sensing devices are not available.


Figure 1 HybridIoT uses information from intermittent and mobile devices to provide accurate estimates.


Smart City, Missing Data Estimation, Heterogeneous Data Integration

Scientific goal

HybridIoT enables estimating accurate environmental information under conditions of uncertainty arising from the urban application context in which the project is situated, and which have not been explored by the state-of-the-art solutions :

-    openness: sensors can enter or leave the system at any time without the need for any reconfiguration;

-    large scale: the system can be deployed in a large, urban context and ensure correct operation with a significant number of devices;

-    heterogeneity: the system handles different types of information without any a priori configuration.


Davide Guastella, Valérie Camps, Marie-Pierre Gleizes {davide.guastella, valerie.camps,


8ième journée scientifique neOCampus

Mercredi 7Juillet 2021 - 8H30 à 13h00

Auditorium Concorde - Bâtiment U4

Université Toulouse III – Paul Sabatier

Lien pour s'inscrire :

Programme neOCampus 2021

SANDMAN: Anomaly Detection in a Data Stream Issued from Smart Buildings

IRIT and  LMDC , Toulouse University


Anomaly detection, multi-agent system, smart buildings, energy management, data stream

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 existing software can 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. To improve the energy management, an innovative anomaly detection system should aim at analyzing raw data, detect any kind of anomalies (point, contextual, collective) in an open environment, at large scale. The developed AI system is called SANDMAN (semi-Supervised ANomaly Detection with Multi-AgeNt systems). The system is semi-supervised by an expert of the field who confirms or overturns the feedback of SANDMAN. 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 system. The results show the robustness of the AI regarding the detection of noisy data, of different types of anomalies, and the scaling.  

Scientific goal

Anomalies detection in smart buildings streaming data by a semi-supervised multi-agent system.


ReCoVAC: conditions for REtaking COntrol by self-obserVAtion of situations within a Connected autonomous vehicle

IRIT - CLLE, Toulouse University


Autonomous vehicles, self-adaptive multi-agent systems, driving control recovery.

Connected autonomous vehicles of level 3, called "conditioned automation", 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 supervision system adapted to each driver, by integrating human factors, to allow a 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 to anticipate and react to it as best as possible. The driving context is composed of indicators that characterize the elements that describe part of the driving process: human, vehicle, and environment. The system is based on self-adaptive multi-agentlearning 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

- Study the impact of the factors describing the different elements present in the system context on the ability of the system to converge towards a solution.

- Insure the acceptability of the system by human driver


Interaction between patient and exoskeleton for cerebral palsy in children



Cerebral palsy, gait analysis, robotic rehabilitation

Children with cerebral palsy (CP) have an altered locomotion with abnormal gait patterns such as crouch gait, for which the lower limb joints display too much flexion. An exoskeleton adapted to the needs of the patient could improve their walking abilities. An experimental session of gait analysis was conducted to analyze and compare precisely the gait of two twin sisters, one with CP, with the aim of developing an exoskeleton to treat CP. The healthy child showed no differences with the standard results except for the frontal plane kinematics of the knee and hip. The study of the muscular activity revealed an over-activation of all the muscles of the child with CP. The kinematic results showed too much flexion in the sagittal plane for the hip, knee and ankle, as well as asymmetric deviations in the frontal and transverse planes. The kinematics of the pelvis and lumbar region were also altered. Although the data was scarce due to experimental difficulties, the study of joint moments and powers showed altered profiles compared to the results of the healthy child. These findings will allow to run simulations of an exoskeleton and to develop a control strategy adapted to this particular child. 

Figure: General configuration of the experimental configuration 

Figure: Kinematics of the right hip, knee and ankle in the sagittal plane. The red results stand for the healthy child and the blue results for the CP child


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.



Dynamic Collection of Cooperative Awareness Messages for Collision Avoidance with Vulnerable Road Users

IRIT – Toulouse University                                         


V2X Communication architectures, vulnerable road users safety, cooperative awareness messages.

With the evolution of Intelligent Transportation System (ITS), vehicles are capable of performing intelligent decisions and cooperative communications with other road users to exchange data and expand their environmental awareness. This communication is introduced as vehicle-to-everything (V2X) where vehicles exchange cooperative awareness messages « CAM » that includes important information (eg: position, speed, heading angle, vehicle type…). The generation rules of the CAM messages are defined by the ETSI standard and they are implemented in the facilities layer of each vehicle. The frequency of sending CAMs is between 10 and 1 Hz. However, we found out that following the standard, vehicles must generate a CAM if any change in their behavior is detected. This can lead to overloading the network if the number of road users is high, and might not be necessary if a vehicle has no.

Scientific goal

The aim of this work is to design a novel  neighboring mechanism to enhance the current version of the standard. Through a centralized server with a global vision of the network, the vehicles will be able to efficiently adapt the frequency of sending CAM messages using the information of surrounding neighbors received from the edge-server. Our mechanism is extended to support vulnerable road users (pedestrians, cyclists…) where reducing their transmission frequency undoubtedly helps in saving energy on their connected devices.


Déploiement d’un système de suivi des déplacements et de la pollution sur vélos pour la mise à disposition sécurisée de données atmosphériques.

Cette thèse s’inscrit dans le cadre du projet CLUE : Cycle-based Laboratory for Urban Evolution. Ce projet scientifique vise à équiper une partie des vélos évoluant dans le campus et dans Toulouse d’un ensemble de capteurs afin d’étudier les déplacements des usagers, mais aussi de profiter du réseau de capteurs mobiles ainsi déployé pour collecter des informations sur la pollution atmosphérique sur le campus et plus largement dans la ville.

Objectifs scientifiques

Plus particulièrement, l’objectif de cette thèse s’articule autour des points suivants :

• La collecte d’un jeu de données dans Toulouse (données de mobilité et mesure de polluants atmosphérique) - inexistant à ce jour - et sa mise à disposition.

• Le déploiement d’un noeud de collecte sans fil des informations, grâce à la technologie LoRa (longue portée, basse consommation d’énergie), et la sécurisation des données sensibles (localisation).

• La présentation des données aux différents acteurs/utilisateurs (chercheurs en aérologie, cyclistes, personnes en charge de l’aménagement du campus) :

– Système de contrôle d’accès aux données multi-roles

– Compromis protection de la vie privée/utilisabilité des données

• L’intégration de différents capteurs existants et tests en environnement réel, en particulier pour les capteurs “black carbon” et oxydes d’azote

• Le raffinement et la validation in situ des modèles de diffusion de polluants utilisés en aérologie


- Christophe Bertero (LAAS) : 

- Jean-François Léon (LA) :

- Matthieu Roy (LAAS) :

- Gilles Tredan (LAAS) :


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


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