Connected Intelligence

OpenSensingCity logo


In the context of smart cities, the deployment of multiple sensors provides access to numerous data streams in real time. The open publication of sensor data brings innovation opportunities by combining the usual benefits of open data with those of real time updates. Indeed, open data ensures transparency and, in principle, allow anybody to develop services that were never envisioned by data providers. Real time updates allow one to consider the development of new services beyond the traditional use of open data, e.g., for logging evolution and conducting a posteriori analysis. We can therefore contemplate the creation of an ecosystem of smart open urban services. While publishing sensor data via an open data platform, such as Grand Lyon’s “Smart Data” platform, is a first step towards our goal, it is now necessary to propose solutions to facilitate uses and usages of real time open data. As a matter of fact, these data are practically difficult to understand, find and eventually exploit. This is even truer when data is coming from raw stream of sensor data because constraints on processing and communication capabilities impose minimising transmitted information.

Consequently and in order to enable the development of a smart open city service ecosystem, we want to provide (1) technological solutions to help leverage open sensor data for city application developers, and (2) guidance to the actors of the open sensor data environment by analysing the stakeholder strategies, defining usage scenarios and terminologies. To this aim, we combine a social analysis of actor expectations, requirements and practices, with a technological and theoretical expertise in online data and knowledge processing and engineering. The social component of our proposal will ensure a better understanding of expectations and needs of various categories of real time open data users. The technological component is based on existing Semantic Web technologies as well as stream processing techniques. This will expectedly result in enriching and publishing stream data to an open platform, taking into account the new paradigm of linked data, stream data and reasoning. This major shift will be achieved by building new ontologies related to smart cities when necessary as well as a formalism for querying and combining streams in such a context. Additionally, by combing our work in the social and technological domains, we will define search and browsing capabilities that we will implement according to the identified expectations and needs. Finally, when those tools will be effectively developed, we will show their utility by providing a demo application that uses them and further study the usage scenarios. Our planned application will offer an intelligent transportation system that helps vehicle drivers to better find parking spots, with possible testing in the city of Lyon.



Machines and agents have more and more autonomous functions and consequently are less and less supervised by human operators or users. Therefore especially when machines interact with humans, we need to ensure that they do not harm us/them or threaten our/their autonomy, especially decision autonomy. Consequently the question of an ethical regulation or control of such autonomous agents is raised and has been discussed by several authors such as Wallach and Allen. As stated by Picard, the greater the freedom of a machine, the more it will need moral standards.

Let’s consider to motivate this problematic the trolley and footbridge dilemmas. Assume that a runaway trolley is hurtling down a track towards five people, whereas there is a single person on a neighbouring track, and two people (a thin one and a fat one) on a footbridge under which the trolley will pass. The trolley dilemma is as follows: should the driver change tracks, killing one to save five? The footbridge dilemma is: should the thin man push the fat man over the footbridge to suddenly stop the trolley? More generally, both dilemmas raise the question: considering an agent A that can make a decision that would benefit many other agents but, in doing so, an agent B would be unfairly harmed, under what circumstances would it be moral for agent A to violate agent B’s rights in order to benefit the group?

The objectives of the eThicAa project is twofold:

  1. definition of what should be a moral autonomous agent and a system of moral autonomous agents
  2. definition and resolution of ethical conflicts that could occur 1) inside one moral agent, 2) between one moral agent and the (moral) rules of the system it belongs to, 3) between one moral agent and a human operator or user, 4) between several artificial (moral) agents including or not human agents.

Ethical conflicts are characterized by the fact that there is no “good” way to solve them. Nevertheless when a decision must be made it should be an informed decision based on an assessment of the arguments and values at stake. When several agents are involved this may result in one agent taking over the (decision or action) authority from the others.

eThicAa proposes to study the four cases of ethical conflicts that could occur in moral autonomous agents or between moral autonomous agents and humans on two chosen applicative domains: robotics and privacy management. For instance, in the robotic domain, eThicAa should be able to manage the ethical conflicts between one artificial agent and one human operator. To this end, we will consider a UAV (Unmanned Air Vehicle) jointly operated by a human operator and an artificial agent. Assuming that the UAV is in an emergency situation and must be crashed, the only two options being either very near the operator’s headquarters (where many operator’s colleagues work) or very near a small village, which decisions must be made by the autonomous agent?

The case of privacy management will consider ethical conflicts between multiple artificial agents and human users, we will consider a social network where the privacy policies of the accounts owned by humans are controlled by moral autonomous agents. Assuming that two users feud and broadcast some private data about the other one in a common circle of friends, what should be the privacy policy of the society of agents including the agents owned by those feuding users?

From the implementation and experimentation of those scenarios, eThicAa aims at providing a formal representation of ethical conflicts and of the objects on which they are about. The project also aims at designing explanation algorithms for the human user and autonomous agents’ arguments and values to make informed ethical decisions. Consequently the outcome of eThicAa will be a framework and recommendations to design moral artificial agents, i.e. how their autonomous functions should be controlled to make them act according to context-dependent moral rules and to deal with ethical conflicts involving other artificial or humans agents, whether moral or not.

More information on ETHICAA

SEAS logo


Environmental, economic and sustainability challenges of continuously increasing energy consumption are present all over the world. Meeting the challenges requires crossindustry cooperation and the means for consumers to influence their energy consumption in terms of the quantity and type of energy consumed. The SEAS project will address the problem of inefficient and unsustainable energy consumption, which is due to a lack of sufficient means to control, monitor, estimate and adapt the energy use of systems versus the dynamic use situations and circumstances influencing the energy use. The objective of the SEAS project is to enable energy, ICT and automation systems to collaborate at consumption sites, and to introduce dynamic and refined ICT-based solutions to control, monitor and estimate energy consumption. An additional aim is to explore business models and solutions that will enable energy market participants to incorporate micro-grid environments and active customers.

More information on SEAS

MOOCTAB: Massive Online Open Course on Tablets

Massive Online Open Course on Tablets

MOOCTAB aims at creating a tablet based platform dedicated to the lifelong learning (primary, secondary, higher, and continuous) using an on-demand MOOC platform with following characteristics:

-       It is based on existing open source MOOC platforms (for example EdX for high school and continuous education) but new functionalities will be added;

-       Data will be stored on a local secured cloud (usage of European Cloud servers, main European provider) as such keeping independence;

-       MOOC will be used through tablets with an intuitive interface and a secured connection (and sometimes after payment, depending on use case) thanks to a strong management of material fleet and identification usage adapted to each use case;

-       This platform will be quite open to allow addition of other use case afterward, whether it will be in e-education or other application such as documents secured management.

see also the



  • French ANR CONTINT2013 program

  • - Connected Intelligence theme of the LaHC, UJM Saint Etienne (coordinator),
  • - DICE INRIA/CITI lab at INSA Lyon,
  • - Casa Team of the IRISA lab,
  • - LEMNA, Mines de Nantes
  • - ChronoCourse

  • - Connected Intelligence theme of the LaHC, UJM Saint Etienne




This project is financed by the French ANR CONTINT2013 program.

The objective of this project is to help the Collaborative Creation of Contents and Publishing using Opportunistic networks to support the emergence of Ephemeral and Spontaneous Social Networks. It aims at proposing a set of scientific and software innovative solutions for the provision of services with intermittent connectivity, the definition of an infrastructure fo the collaborative management of services in the context of Ephemeral and Spontaneous Social Networks, and an analysis of the value adapted to this context. see the official C3PO web site.

Water-M logo

Water-M: Unified Intelligent WATER Management

Water-M is an ITEA3 European project with 21 partners from Finland, France, Romania and Turkey. The goal of Water-M project is to develop an architecture for intelligent management of water distribution. The project takes into account the current and prospective needs of all types of actors: managers, delegates, administrators, legislators, user communities, and consumers in the residential, industry and the Tertiary sector.

Only 2.5% of the world’s water is fresh water. In recent decades, the human population has increased by a factor of 3, but at the same time water demand has increased by a factor of 6. Water is a finite resource that should be carefully managed. However, more than 50% of the world’s population lives in areas with a water sustainability problem. In this regard, water industries are using SCADA technologies to support their business processes, but this is clearly not enough. To solve the water sustainability problem, which is compounded by the water process complexity, a major upheaval of the water industry is needed with the introduction of novel concepts, such as GIS integration, quality management programs or real-time data management. In this context, ICT technologies are needed to drive these challenges. The scope of the Water-M project enables the creation of new products and services to build a unified water business model that will benefit European Union water stakeholders. The Water-M project combines real-time monitoring and operational control, service-oriented approaches and event driven mechanisms in the water management domain.

For more information:


WDAqua (Answering Questions using Web Data) is a Marie Skłodowska-Curie Innovative Training Network (ITN).

Smart infrastructures and citizens’ participation in the digital society are increasingly data-driven. Sharing, connecting, managing, analysing and understanding data on the Web will enable better services for citizens, communities and industry. However, turning web data into successful services for the public and private sector requires skilled web and data scientists, and it still requires further research. WDAqua aims at advancing the state of the art by intertwining training, research and innovation efforts, centred around data-driven question answering. Question answering is immediately useful to a wide audience of end users, and we will demonstrate this in settings including e-commerce, public sector information, publishing and smart cities. Steps to answering a question are (1) understanding a spoken question, (2) analysing the question’s text, (3) finding data to answer the question, and (4) presenting the answer(s). Every individual research project in WDAqua connects at least two of these steps.

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