The holy grail of Artificial Intelligence (AI)---creating an agent (e.g., software or machine) that comes close to mimicking and (possibly) exceeding human intelligence---remains far off. But past years have seen breakthroughs in agents that can gain abilities from experience with the environment: providing significant advances in the society and the industries including health care, autonomous driving, recommender systems; and ultimately influencing many if not all aspects of everyday life. These advances are partly due to single-agent Deep Learning (DL) along with RL and Monte-Carlo Tree Search (MCTS), i.e., AI research subfields in which the agent can describe its world as a Markov decision process. Some stand-alone planning and RL algorithms are guaranteed to converge to the optimal behavior, as long as the environment, the agent is experiencing, is Markovian and stationary, but scalability remains a significant issue. DL along with RL and MCTS methods have emerged as a powerful combination to break the curse of dimensionality in the face of very large-scale domains at the expend of astronomical data and computational resources, but so far their applicability is mainly restricted to either single-agent domains or sequential games. Today, real-life applications widely use MASs, that is, groups of autonomous, interacting agents sharing a common environment, which they perceive through sensors and upon which they act with actuators. At home, in cities, and almost everywhere, a growing number of sensing and acting machines surround us, sometimes visibly (e.g., robots, drones, cars, power generators) but often imperceptibly (e.g., smartphones, televisions, vacuum cleaners, washing machines). Before long, through the emergence of a new generation of communication networks, most of these machines will be interacting with one another through the internet of things (IoT). Constantly evolving MASs will thus break new ground in coming years, pervading all areas of the society and the industries, including security, medicine, transport, and manufacturing. Although Markov decision processes provide a solid mathematical framework for single-agent planning and RL, they do not offer the same theoretical grounding in MASs. In contrast to single-agent systems, when multiple agents interact with one another, how the environment evolves depends not only upon the action of one agent but also on the actions taken by the other agents, rendering the Markov property invalid and the environment no longer stationary. Also, a centralized (single-agent) control authority is often inadequate because agents cannot (e.g., due to communication cost, latency or noise) or do not want (e.g., in competitive or strategic settings) to share all their information all the time. As a consequence, the increasing penetration of MASs in the society will require a paradigm shift---from single-agent to multi-agent planning and reinforcement learning algorithms---leveraging on recent breakthroughs. That leads us to the fundamental challenge this proposal addresses: the design of generic algorithms with provable guarantees that can efficiently compute rational strategies for a group of cooperating or competing agents in spite of stochasticity and sensing uncertainty, yet using the same algorithmic scheme. Such algorithms should adapt to changes in the environment; apply to different tasks, and eventually converge to a rational solution for the task at hand. But it needs not to exhibit the fastest convergence rates since there is no free lunch. Using the same algorithmic scheme for different problems eases knowledge transfer and dissemination in expert as well as practitioner communities.

One of the most representative technologies for long-range networks, LoRaWAN, has gained global momentum. Telecommunication operators like Bouygues Telecom, Orange, and KPN have deployed several thousand LoRaWAN gateways each to accelerate innovation in areas such as smart cities, environmental monitoring and intelligent transport systems. However, the capacity of LoRaWAN is considerably limited by the downlink communication (from the gateway to the end nodes), due to factors like gateway hardware properties, European regulations of the 868MHz open band, and current specifications of the LoRaWAN protocol. The goal of this project is to propose a new protocol that increases network capacity in the presence of downlink communication and that is energy efficient for the end nodes, while not hindering the reliability of uplink communication.

The DRON-MAP project focuses on the use of cooperative UAV networks for pollution plume monitoring in emergency situations (industrial accidents, natural disasters, deliberate terrorist releases, etc.). The deployment of a UAV network in these situations face different scientific and technical challenges such as taking into account the strong plume dynamics, the timely data analysis, the reliable communication and coordination between UAVs and the planning of optimal trajectories. The objective of DRON-MAP project is to address these challenges while proposing a new global and systemic approach. Based on reliable communications and coordination between drones, our approach will federate an instantaneous estimation and a prediction of the plume evolution with efficient anticipatory algorithms of optimal path planning. A network testbed of few communicating UAVs will be set up in order to assess real-world feasibility and performance at a small scale.

Cellular networks have been continuously reshaping communication in our society, through the rapid evolution of standards, products, and use cases. However, the careful planning and deployment strategies, implemented by mobile operators in order to provide coverage and data services to users, were constants during all these decades. But recent evolutions in terms of communication equipment miniaturization, network virtualization and autonomous vehicles are challenging this vision. In this context, the DEMON project proposes a major evolution of the cellular network architectures, by using mobile base stations. The main objective of the DEMON project is to provide dedicated solutions for this new architecture, demonstrating the advantages of a self-deployable approach, detecting key challenges in this new paradigm and addressing these challenges

The emerging Internet of Things (IoT) is expected to host billions of devices that regularly report sensor readings by using long or short-range radio channels. Generated content items and also collateral metadata are not well protected today because (i) channel encryption is commonly intercepted at gateways, (ii) identifiers reveal communication partners and contexts, and (iii) cryptographic protection embedded in the low-end transmission infrastructure remains too weak to resist attacks. In the current IoT, sensing sources lose control over their data often even before it reaches its destinations. In PIVOT, we start from two fundamental observations. First, a privacy-friendly IoT requires to protect content objects by themselves, in addition to commonly deployed channel encryption. Content disclosure can thus be attributed to designated receivers. Second, names can serve as the principle interface to access IoT data, eliding source identities. Hence, individual endpoint identifiers will disappear from public Internet metadata. The innovation in PIVOT is to address the prevalent privacy and security issues in IoT by proposing content object security principles that build on privacy-friendly names, while remaining globally and seamlessly interoperable between IoT devices regardless of networks to which they may connect. PIVOT will focus on four core goals: 1. A crypto framework for privacy-friendly service primitives on ultra-constrained IoT devices. 2. Minimal trust anchor provisioning on IoT devices to enable object security. 3. Protocols that integrate decentralized object security. 4. Multi-stakeholder name management that preserves privacy requirements and generates, allocates and resolves names globally regardless of the IoT applications or networks. A demonstrator based on RIOT will verify our solutions in Ultra-Constrained IoT networks such as LoRaWAN. PIVOT follows the perspective of “immediate action is required”. This implies that we will extend existing architectures and protocol standards conjoined with standardization bodies while introducing new first-hand primitive where necessary. Our ambitious roadmap will allow for incremental deployment of the PIVOT solutions, which is the most promising path to quick adoption. The consortium reflects the entire innovation chain and is centred around three established strong German-French collaborations. First, the popular RIOT IoT operating system (OS) was co-founded and is jointly managed by Freie Universität Berlin, INRIA, and HAW Hamburg. Over the past seven years, this team has successfully established a global community for an open IoT. As of today, RIOT has a 5% global IoT OS market share. Second, a strong binational LoRa development community has been established. Afnic, Lobaro, HAW Hamburg, and FU Berlin are part of it. Particular contributions to IoT privacy come from INSA/INRIA in this context. Third, international Internet standardisation is in focus of FU Berlin, HAW Hamburg, and Afnic—they jointly work in the IETF for more than 10 years. In addition to common technical interests, all partners of the German-French team are united in their dedication to an open IoT with the full freedom of data and rights of privacy. Weak IoT security and privacy established their roots in economic factors caused by the tension between costs of security measures and gain from data capitalization. The integration of the four PIVOT goals—based on existing protocols, open software, and open standards—will disengage from economic tensions, reduce cost, promote business compliant to European privacy standards, and regain data sovereignty to the public in the long term. With its open sustainable perspective PIVOT will contribute to changing the trend of Internet consolidation by allowing faster adoption of security and privacy solutions to the IoT, thereby fostering open, trustworthy digitization of our societies.