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The use of Internet of Things (IoT) is continuously growing, and its application areas henceforth reach all societal and economical fields. The number of connected IoT devices is increasing exponentially, and so is the amount of their exchanged data. To ensure the security and privacy of these data, several Group Key Management (GKM) schemes were proposed. These schemes assume the presence of a centralized Key Manager (KM), which is regarded as the central component of these schemes. However, the huge responsibilities carried out by the KM require that it meets several performance and security criteria. Moreover, the central role it plays in the GKM makes it a classic single point of failure. Solving this issue in an energy efficient way remains a difficult challenge. In this paper, we propose a highly flexible and decentralized election-based protocol for Key Management. We introduce new mechanisms to ensure the technical reliability of our scheme in terms of performance and security. We also describe how our solution tackles the issue of single point of failure, by introducing a simple failure recovery procedure. Our simulations show that our solution have a low energy consumption.

Mobile data collection is a very efficient solution to gather information from spatially distributed IoT nodes. In order to enhance the efficiency of mobile data collection, the trajectory planning of the mobile node has been widely studied. In the literature, most of the solutions proposed use static and non learning-based approaches. In our paper, we opted for a learning based approach in which we study a trajectory planning problem in mobile data collection for IoT where IoT nodes are organized in clusters. A relay node is chosen from each cluster in order to collect data from IoT nodes and transmit it to the mobile node in its range. In order to plan the trajectory of the mobile node, we train a deep Q-learning network (DQN) with combined experience replay. This solution will allow us to maximize the amount of data collected and reduce the energy consumption. It will also allow us to adapt the trajectory of the mobile node to the environment parameters without doing expensive recomputations and learning.

The Internet of Things (IoT) is an emerging technology that aims to extend connectivity to all everyday devices. One of the main challenges that are slowing down its development is how to secure the Device-to-Device communication. Among all the security issues, the Key Management (KM) is one of the most challenging. The difficulty lies in the fact that most of the IoT devices suffer from a lack of resources. Although different protocols were proposed, most of them do not consider the dynamic nature of the IoT. Other solutions rely on a centralized entity to distribute the new keys upon a change in the network. However, this entity becomes a single point of failure and the main target of attacks. We propose a novel blockchain-based decentralized KM protocol. In addition to being resilient, scalable and dynamic, our solution uses the blockchain technology to securely distribute the KM on several entities.

The Internet of Things (IoT) is a network made up of a large number of devices which are able to automatically communicate to computer systems, people and each other providing various services for the benefit of society. These devices have the particularity of being heterogeneous and so have different capabilities in terms of storage, computing, communication and energy. One of the main challenges facing the IoT is how to secure communication between these heterogeneous devices. Among all the issues, the Group Key Management is one of the most difficult. Although different approaches have been proposed to solve it, very few of them consider the heterogeneous nature of the IoT. We propose then a highly scalable Multi-Group Key Management protocol for IoT that ensures the forward and backward secrecy, efficiently recovers from collusion attacks, guarantees the secure coexistence of several services in a single network and balances the loads between its heterogeneous devices according to their capabilities. The evaluation of our solution shows that it is efficient for large-scale heterogeneous networks even if they contain highly resource-constrained devices.

Internet of Every things (IoE) is a network that integrates a variety of heterogeneous nodes, such as connected portable devices, connected cars, smart home appliances, humans, etc. to Internet. The constituents of IoE are heterogeneous, distributed by nature and also autonomous in most of cases, which meet the criteria of what we call Systems of Systems (SoS). These smart objects communicate and collaborate between each other in dynamic environments which are subject to several security attacks. In this paper, we propose a hierarchical based blockchain trust management architecture with mobility support in highly distributed IoE based Systems of Systems. This architecture deals efficiently with scalability issues. It allows smart objects to disseminate the trustworthiness of the service providers they interact with them to the blockchain. This later makes the access to this information possible from anywhere and ensures a global view of trustworthiness information. Thanks to the blockchain technology, our architecture outperforms the existing trust management solutions especially in terms of scalability and mobility support.

International audience; MEMS micro robots are low-power and low memory capacity devices that can sense and act. One of the most challenges in MEMS micro robot applications is the self-reconfiguration, especially when the efficiency and the scalability of the algorithm are required. In the literature, if we want a self-reconfiguration of micro robots to a target shape consisting of P positions, each micro robot should have a memory capacity of P positions. Therefore, if P equals to millions, each node should have a memory capacity of millions of positions. Therefore, this is not scalable. In this paper, nodes do not record any position, we present a self-reconfiguration method where a set of micro robots are unaware of their current position and do not have the map of the target shape. In other words, nodes do not store the positions that build the target shape. Consequently, memory usage for each node is reduced to O(1). An algorithm of self-reconfiguration to optimize the communication is deeply studied showing how to manage the dynamicity (wake up and sleep of micro robots) of the network to save energy. Our algorithm is implemented in Meld, a declarative language, and executed in a real environment simulator called DPRSim.

International audience; MEMS microrobots are low-power and low-memory capacity devices that can sense and act. One of the most difficult challenges in MEMS microrobot applications is the selfreconfiguration, especially when the efficiency, parallelism and the scalability of the algorithm are required. Self-reconfiguration with shared map does not scale. Because the map (predefined position of the target shape) consists of P positions, and each node must have a memory capacity, at least, of P positions. Therefore, if P is very high, the self-reconfiguration will be not feasible. In this paper, we present an efficient reconfiguration algorithm, without predefined positions of the target shape, which reduces memory usage to O(1). This algorithm ensures the networks connectivity throughout all its execution time. This solution improves the execution time and the number of movements by using movement of different microrobots at the same time that we call parallelism of movement. Our algorithm is implemented in Meld, a declarative language, and executed in a real environment simulator called DPRSim.