Optimal Collision/Conflict-free Distance-2 Coloring in Synchronous Broadcast/Receive Tree Networks

This article is on message-passing systems where communication is (a) synchronous and (b) based on the "broadcast/receive" pair of communication operations. "Synchronous" means that time is discrete and appears as a sequence of time slots (or rounds) such that each message is received in the very same round in which it is sent. "Broadcast/receive" means that during a round a process can either broadcast a message to its neighbors or receive a message from one of them. In such a communication model, no two neighbors of the same process, nor a process and any of its neighbors, must be allowed to broadcast during the same time slot (thereby preventing message collisions in the first case, and message conflicts in the second case). From a graph theory point of view, the allocation of slots to processes is know as the distance-2 coloring problem: a color must be associated with each process (defining the time slots in which it will be allowed to broadcast) in such a way that any two processes at distance at most 2 obtain different colors, while the total number of colors is "as small as possible". The paper presents a parallel message-passing distance-2 coloring algorithm suited to trees, whose roots are dynamically defined. This algorithm, which is itself collision-free and conflict-free, uses $\Delta + 1$ colors where $\Delta$ is the maximal degree of the graph (hence the algorithm is color-optimal). It does not require all processes to have different initial identities, and its time complexity is $O(d \Delta)$, where d is the depth of the tree. As far as we know, this is the first distributed distance-2 coloring algorithm designed for the broadcast/receive round-based communication model, which owns all the previous properties.Comment: 19 pages including one appendix. One Figur

Algorithmes distribués pour l'optimisation de déploiement des microrobots MEMS

MEMS microrobots are miniaturized elements that can capture and act on the environment. They have a small size, low memory capacity and limited energy capacity. These inexpensive devices can perform several missions and tasks in a wide range of applications such as locating odor, fighting against fires, medical service, surveillance, search, rescue and safety. To do these tasks and missions, they have to carry out protocols of redeployment to adapt to the working conditions. These algorithms should be efficient, scalable, robust and should only use local information. Redeployment for mobile MEMS microrobots currently requires a positioning system and a map (predefined positions) of the target shape. Traditional positioning solutions such as using GPS consumes a lot of energy and it is no applicable in the micro scale. Also, the use of an algorithmic solution positioning with multilateration techniques causes problems due to errors in the coordinates obtained. In the literature works, if we want a microrobots self-reconfiguring to a target shape consisting of P positions, each microrobot must have a storage capacity of at least P positions to save them. Therefore, if P equals to thousands or millions, every node must have a storage capacity of thousands or millions of positions. However, these algorithms are notscalable. In this thesis, we propose protocols of self-reconfiguration where nodes are not aware of their position in the plane and do not record the positions of the target shape. Therefore, the memory space required for each node is significantly reduced at a constant complexity. The purpose of these distributed algorithms is to optimize the logical topology of the network of mobile MEMS microrobots to seek a better complexity for message exchange and inexpensive communication.In this work, we show for the reconfiguration of a chain into a square, how to handle the dynamicity of the network to save energy, and we study how to use parallelism in motion to optimize the execution time and the number of movements. Furthermore, another solution is proposed where the initial physical topology may be any connected configuration. With thesesolutions the nodes can execute the algorithm regardless of where they are deployed, because the algorithm is independent of the map of the target shape. Furthermore, these solutions seek to achieve the shape of the target with a minimum amount of movement.Les microrobots MEMS sont des éléments miniaturisés qui peuvent capter et agir sur l'environnement. Leur taille est de l'ordre du millimètre et ils ont une faible capacité de mémoire et une capacité énergétique limitée. Les microrobots MEMS continuent d'accroître leur présence dans notre vie quotidienne. En effet, ils peuvent effectuer plusieurs missions et tâches dans une large gamme d'applications telles que la localisation d'odeur, la lutte contre les incendies, le service médical, la surveillance, le sauvetage et la sécurité. Pour faire ces taches et missions, ils doivent appliquer des protocoles de redéploiement afin de s'adapter aux conditions du travail. Ces algorithmes doivent être efficaces, évolutifs, robustes et ils doivent utiliser de préférence des informations locales. Le redéploiement pour les microrobots MEMS mobiles nécessite actuellement un système de positionnement et une carte (positions prédéfinies) de la forme cible. La solution traditionnelle de positionnement comme l'utilisation d'un GPS consommerait trop d'énergie. De plus, l'utilisation de solutions de positionnement algorithmique avec les techniques de multilatération pose toujours des problèmes à cause des erreurs dans les coordonnées obtenues.Dans la littérature, si nous voulons une auto-reconfiguration de microrobots vers une forme cible constituée de P positions, chaque microrobot doit avoir une capacité mémoire de P positions pour les sauvegarder. Par conséquent, si P est de l'ordre de milliers ou de millions, chaque noeud devra avoir une capacité de mémoire de positions en milliers ou millions. Parconséquent, ces algorithmes ne sont pas extensibles ou évolutifs. Dans cette thèse, on propose des protocoles de reconfiguration où les noeuds ne sont pas conscients de leurs positions dans le plan et n'enregistrent aucune position de la forme cible. En d'autres termes, les noeuds ne stockent pas au départ les coordonnées qui construisent la forme cible. Par conséquent, l'utilisation de mémoire pour chaque noeud est réduite à une complexité constante. L'objectif desalgorithmes distribués proposés est d'optimiser la topologie logique du réseau des microrobots afin de chercher une meilleure complexité pour l'échange de message et une communication peu coûteuse. Ces solutions sont complètement distribués. On montre pour la reconfiguration d'une chaîne à un carré comment gérer la dynamicité du réseau pour sauvegarder l'énergie, on étudie comment utiliser le parallélisme de mouvements pour optimiser le temps d'exécution et lenombre de mouvements. Ainsi, on propose une autre solution où la topologie physique initiale peut être n'importe quelle configuration initiale. Avec ces solutions, les noeuds peuvent exécuter l'algorithme indépendamment du lieu où ils sont déployés, parce que l'algorithme est indépendant de la carte de la forme cible. En outre, ces solutions cherchent à atteindre la forme de la cible avec une quantité minimale de mouvement

Distributed and Dynamic Map-less Self-reconfiguration for Microrobot Networks

International audienceMEMS 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

Internet of things security: A top-down survey

International audienceInternet of Things (IoT) is one of the promising technologies that has attracted a lot of attention in both industrial and academic fields these years. It aims to integrate seamlessly both physical and digital worlds in one single ecosystem that makes up a new intelligent era of Internet. This technology offers a huge business value for organizations and provides opportunities for many existing applications such as energy, healthcare and other sectors. However, as new emergent technology, IoT suffers from several security issues which are most challenging than those from other fields regarding its complex environment and resources-constrained IoT devices. A lot of researches have been initiated in order to provide efficient security solutions in IoT, particularly to address resources constraints and scalability issues. Furthermore, some technologies related to networking and cryptocurrency fields such as Software Defined Networking (SDN) and Blockchain are revolutionizing the world of the Internet of Things thanks to their efficiency and scalability. In this paper, we provide a comprehensive top down survey of the most recent proposed security and privacy solutions in IoT. We discuss particularly the benefits that new approaches such as blockchain and Software Defined Networking can bring to the security and the privacy in IoT in terms of flexibility and scalability. Finally, we give a general classification of existing solutions and comparison based on important parameters

Distributed algorithms for optimizing the deployment of MEMS microrobots

Les microrobots MEMS sont des éléments miniaturisés qui peuvent capter et agir sur l'environnement. Leur taille est de l'ordre du millimètre et ils ont une faible capacité de mémoire et une capacité énergétique limitée. Les microrobots MEMS continuent d'accroître leur présence dans notre vie quotidienne. En effet, ils peuvent effectuer plusieurs missions et tâches dans une large gamme d'applications telles que la localisation d'odeur, la lutte contre les incendies, le service médical, la surveillance, le sauvetage et la sécurité. Pour faire ces taches et missions, ils doivent appliquer des protocoles de redéploiement afin de s'adapter aux conditions du travail. Ces algorithmes doivent être efficaces, évolutifs, robustes et ils doivent utiliser de préférence des informations locales. Le redéploiement pour les microrobots MEMS mobiles nécessite actuellement un système de positionnement et une carte (positions prédéfinies) de la forme cible. La solution traditionnelle de positionnement comme l'utilisation d'un GPS consommerait trop d'énergie. De plus, l'utilisation de solutions de positionnement algorithmique avec les techniques de multilatération pose toujours des problèmes à cause des erreurs dans les coordonnées obtenues.Dans la littérature, si nous voulons une auto-reconfiguration de microrobots vers une forme cible constituée de P positions, chaque microrobot doit avoir une capacité mémoire de P positions pour les sauvegarder. Par conséquent, si P est de l'ordre de milliers ou de millions, chaque noeud devra avoir une capacité de mémoire de positions en milliers ou millions. Parconséquent, ces algorithmes ne sont pas extensibles ou évolutifs. Dans cette thèse, on propose des protocoles de reconfiguration où les noeuds ne sont pas conscients de leurs positions dans le plan et n'enregistrent aucune position de la forme cible. En d'autres termes, les noeuds ne stockent pas au départ les coordonnées qui construisent la forme cible. Par conséquent, l'utilisation de mémoire pour chaque noeud est réduite à une complexité constante. L'objectif desalgorithmes distribués proposés est d'optimiser la topologie logique du réseau des microrobots afin de chercher une meilleure complexité pour l'échange de message et une communication peu coûteuse. Ces solutions sont complètement distribués. On montre pour la reconfiguration d'une chaîne à un carré comment gérer la dynamicité du réseau pour sauvegarder l'énergie, on étudie comment utiliser le parallélisme de mouvements pour optimiser le temps d'exécution et lenombre de mouvements. Ainsi, on propose une autre solution où la topologie physique initiale peut être n'importe quelle configuration initiale. Avec ces solutions, les noeuds peuvent exécuter l'algorithme indépendamment du lieu où ils sont déployés, parce que l'algorithme est indépendant de la carte de la forme cible. En outre, ces solutions cherchent à atteindre la forme de la cible avec une quantité minimale de mouvement.MEMS microrobots are miniaturized elements that can capture and act on the environment. They have a small size, low memory capacity and limited energy capacity. These inexpensive devices can perform several missions and tasks in a wide range of applications such as locating odor, fighting against fires, medical service, surveillance, search, rescue and safety. To do these tasks and missions, they have to carry out protocols of redeployment to adapt to the working conditions. These algorithms should be efficient, scalable, robust and should only use local information. Redeployment for mobile MEMS microrobots currently requires a positioning system and a map (predefined positions) of the target shape. Traditional positioning solutions such as using GPS consumes a lot of energy and it is no applicable in the micro scale. Also, the use of an algorithmic solution positioning with multilateration techniques causes problems due to errors in the coordinates obtained. In the literature works, if we want a microrobots self-reconfiguring to a target shape consisting of P positions, each microrobot must have a storage capacity of at least P positions to save them. Therefore, if P equals to thousands or millions, every node must have a storage capacity of thousands or millions of positions. However, these algorithms are notscalable. In this thesis, we propose protocols of self-reconfiguration where nodes are not aware of their position in the plane and do not record the positions of the target shape. Therefore, the memory space required for each node is significantly reduced at a constant complexity. The purpose of these distributed algorithms is to optimize the logical topology of the network of mobile MEMS microrobots to seek a better complexity for message exchange and inexpensive communication.In this work, we show for the reconfiguration of a chain into a square, how to handle the dynamicity of the network to save energy, and we study how to use parallelism in motion to optimize the execution time and the number of movements. Furthermore, another solution is proposed where the initial physical topology may be any connected configuration. With thesesolutions the nodes can execute the algorithm regardless of where they are deployed, because the algorithm is independent of the map of the target shape. Furthermore, these solutions seek to achieve the shape of the target with a minimum amount of movement

Fast and robust self-organization for micro-electro-mechanical robotic systems

International audienceMicrorobots are low-power and low-capacity memory devices that can sense and act. They perform various missions and tasks in a wide range of applications including odor localization, firefighting, medical service, surveillance and security, search and rescue. To achieve these tasks nodes should reconfigure their physical topology to another target organization. The self-organization is one of the most challenging tasks in MEMS applications. In this paper, we propose a distributed and efficient parallel self-organization protocol for chains of MEMS nodes. This protocol is memory-efficient because it does not use the predefined positions of the target shape, which reduces the memory usage to a constant complexity. Our algorithm is implemented in a real environment simulator called DPRSim, the Dynamic Physical Rendering Simulator

Secure permutation routing protocol in multi-hop wireless sensor networks

International audienceno abstrac

On the Mapless Reconfiguration for Modular Robots: Results and Future Directions

International audienceModular robots represent a major technological challenges in the last decades because of expectations on their massive use in our lifetime, particularly in order to improve the well-being of people. Indeed, modular robots are gaining increasing attention due to their use in various missions and tasks in a wide range of applications, including location, the fight against fires, medical services, surveillance and security, and search and rescue. They can perform tasks in unstructured environments, complex, dynamic and unknown. To carry out these missions robots must apply reconfiguration protocols to adapt to working conditions. These reconfiguration protocols must deal with the limitations of nodes including those relating to memory and energy resources. Also, these algorithms should be effective, scalable, secure and robust, using only local information. In this paper, we provide a review on the recent protocols that achieve the self-reconfigurations without predefined positions of the target shape.}, keywords={microrobots;MEMS microrobots;energy resources;fire fighting;mapless reconfiguration;medical services;memory resources;modular robots;reconfiguration protocols;search and rescue;security;self-reconfigurations;surveillance;unstructured environments;Heuristic algorithms;Prediction algorithms;Protocols;Robot kinematics;Robustness;Shape;MEMS;Mapless;Modular robots;reconfiguratio

Machine Learning-Based Communication Collision Prediction and Avoidance for Mobile Networks

International audienc

Efficient Parallel Self-reconfiguration Algorithm for MEMS Microrobots

International audienceIn this paper we propose a distributed and efficient parallel self-reconfiguration algorithm for MEMS microrobots. MEMS microrobots perform various missions and tasks in a wide range of applications including odor localization, firefighting, medical service, surveillance and security, and search and rescue. To achieve these tasks the self-reconfiguration for MEMS microrobots is required. The self-reconfiguration with shared map does not scale. Because with the map (predefined positions of the target shape) each node should store all predefined positions of the target shape, therefore this is not always possible as MEMS nodes have a low-memory capacity. In this paper, we present an efficient self-reconfiguration algorithm without predefined positions of the target shape, which reduces the memory usage to a constant complexity. This algorithm improves the energy consumption by minimizing the amount of displacement and the number of messages