Toward software updates in IoT environments: Why Existing P2P Systems are not Enough

International audienceThe number of connected devices around us is growing, and their embedded software is increasing in complexity. Applications need to be managed and updated throughout their life-cycle. Modern software orchestrators and management systems are mostly cen-tralised and expensive to scale to very large systems. We propose a decentralised approach to distribute software updates to Internet of Things (IoT) devices using Peer-to-Peer (P2P) transfers. We seek to improve the efficiency of software update propagation by adapting system behaviour to IoT constraints, such as device heterogeneity, unreliable network connectivity and applications specifics

Performance Evaluation of Channel Access Methods for Dedicated IoT Networks

International audienceNetworking technologies dedicated for the Internet of Things are different from the classical mobile networks in terms of architecture and applications. This new type of network is facing several challenges to satisfy specific user requirements. Sharing the communication medium between (hundreds of)thousands of connected nodes and one base station is one of these main requirements, hence the necessity to imagine new solutions, or to adapt existing ones, for medium access control. In this paper, we start by comparing two classical medium access control protocols, CSMA/CA and Aloha, in the context of Internet of Things dedicated networks. We continue by evaluating a specific adaptation of Aloha, already used in low-power wide areanetworks, where no acknowledgement messages are transmitted in the network. Finally, we apply the same concept to CSMA/CA, showing that this can bring a number of benefits. The results we obtain after a thorough simulation study show that the choice of the best protocol depends on many parameters (number of connected objects, traffic arrival rate, allowed retransmissionnumber), as well as on the metric of interest (e.g. packet reception probability or energy consumption)

LTE-A Random Access Channel Capacity Evaluation for M2M Communications

International audience—The densification of the urban population and the rise of smart cities applications foster the need for capillary networks collecting data from sensors monitoring the cities. Among the multiple networking technologies considered for this task, cellular networks, such as LTE-A, bring an ubiquitous coverage of most cities. It is therefore necessary to understand how to adapt LTE-A, and what should be the future 5G architecture, in order to provide efficient connectivity to Machine-to-Machine (M2M) devices alongside the main target of mobile networks, Human-to-Human devices. Indeed, cellular random access procedures are known to suffer from congestion in presence of a large number of devices, while smart cities scenarios expect huge density of M2M devices. Several solutions have been investigated for the enhancement of the current LTE-A access management strategy. In this paper, we contribute to the modeling and computation of the capacity of the LTE-A Random Access Channel (RACH) in terms of simultaneous successful access. In particular, we investigate the hypothesis of piggybacking the payload of Machine Type Communications from M2M devices within the RACH, and show that M2M densities considered realistic for smart cities applications are difficult to sustain by the current LTE-A architecture

Méthodes d'Accès au Canal pour les Réseaux Dédiés à l'Internet des Objets

Dedicated networks for the Internet of Things appeared with the promise of connecting thousands of nodes, or even more, to a single base station in a star topology. This new logic represents a fundamental change in the way of thinking about networks, after decades during which research work mainly focused on multi-hop networks.Internet of Things networks are characterized by long transmission range, wide geographic coverage, low energy consumption and low set-up costs. This made it necessary to adapt the protocols at different architectural layers in order to meet the needs of these networks.Several players compete in the Internet of Things market, each trying to establish the most efficient solution. These players are mostly focused on modifying the physical layer, on the hardware part or through proposing new modulations. However, with regard to the channel access control solution (known as the MAC protocol), all the solutions proposed by these players are based on classic approaches such as Aloha and CSMA.The objective of this thesis is to propose a dynamic MAC solution for networks dedicated to the Internet of Things. The proposed solution has the ability to adapt to network conditions. This solution is based on a machine learning algorithm that learns from network history in order to establish the relationship between network conditions, MAC layer parameters and network performance in terms of reliability and energy consumption. The solution also has the originality of making possible the coexistence of nodes using different MAC configurations within the same network. The results of simulations have shown that a MAC solution based on machine learning could take advantage of the good properties of different conventional MAC protocols. The results also show that a cognitive MAC solution always offers the best compromise between reliability and energy consumption, while taking into account the fairness between the nodes of the network. The cognitive MAC solution tested for high density networks has proven better scalability compared to conventional MAC protocols, which is another important advantage of our solution.Les réseaux dédiés pour l’Internet des Objets sont apparus avec la promesse de connecter des milliers de nœuds, voire plus, à une seule station de base dans une topologie en étoile. Cette nouvelle logique représente un changement fondamental dans la façon de penser les réseaux, après des décennies pendant lesquelles les travaux de recherche se sont focalisés sur les réseaux multi-sauts.Les réseaux pour l’Internet des Objets se caractérisent par la longue portée des transmissions, la vaste couverture géographique, une faible consommation d’énergie et un bas coût de mise en place. Cela a rendu nécessaire des adaptations à tous les niveaux protocolaires afin de satisfaire les besoins de ces réseaux.Plusieurs acteurs sont en concurrence sur le marché de l’Internet des Objets, essayant chacun d’établir la solution la plus efficiente. Ces acteurs se sont concentrés sur la modification de la couche physique, soit au niveau de la partie matérielle, soit par la proposition de nouvelles techniques de modulation. Toutefois, en ce qui concerne la solution de contrôle d’accès au canal (connue sous le nom de couche MAC), toutes les solutions proposées par ces acteurs se fondent sur des approches classiques, tel que Aloha et CSMA.L'objectif de cette thèse est de proposer une solution MAC dynamique pour les réseaux dédiés à l’Internet des Objets. La solution proposée a la capacité de s'adapter aux conditions du réseau. Cette solution est basée sur un algorithme d'apprentissage automatique, qui apprend de l'historique du réseau afin d'établir la relation entre les conditions du réseau, les paramètres de la couche MAC et les performances du réseau en termes de fiabilité et de consommation d'énergie. La solution possède également l'originalité de faire coexister des nœuds utilisant de différentes configurations MAC au sein du même réseau. Les résultats de simulations ont montré qu'une solution MAC basée sur l'apprentissage automatique pourrait tirer profit des avantages des différents protocoles MAC classiques. Les résultats montrent aussi qu'une solution MAC cognitive offre toujours le meilleur compromis entre fiabilité et consommation d'énergie, tout en prenant en compte l'équité entre les nœuds du réseau. La solution MAC cognitive testée pour des réseaux à haute densité a prouvé des bonnes propriétés de passage à l’échelle par rapport aux protocoles MACs classiques, ce qui constitue un autre atout important de notre solution