Performance analysis for a stabilized multi-channel slotted ALOHA algorithm

We study slotted ALOHA with multiple random access channels, the so called multi-channel ALOHA (MC-ALOHA). It is well known that single-channel ALOHA (SC-ALOHA) is unstable. Not surprisingly, MC-ALOHA is also unstable. A stabilization algorithm for MC-ALOHA has been proposed in [10], in which the pseudo-Bayesian algorithm in SC-ALOHA was extended to achieve stabilized MC-ALOHA. The idea is to estimate the number of attempting users so that user transmission probability can be adjusted accordingly. In this paper, we give a theoretical analysis on the algorithm performance for cases with limited and unlimited number of users by assuming perfect estimate. The theoretical results are validated by simulation, which shows the stabilization algorithm performs close to a system with perfect estimate. The simulation results also show that the performance of the stabilized algorithm is much better than the non-stabilized algorithm. With the stabilized algorithm, the system is always stable when the new packet arrival rate is less than system capacity. Even when the arrival rate is higher than capacity, system throughput can still be guaranteed. © 2003 IEEE.published_or_final_versio

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)

On Optimizing the Backoff Interval for Random Access Schemes

To improve the channel throughput and the fairness of random access channels, we propose a new backoff algorithm, namely, the sensing backoff algorithm (SBA). A novel feature of the SBA scheme is the sensing mechanism, in which every node modifies its backoff interval according to the results of the sensed channel activities. In particular, every active node sensing the successful transmission decreases its backoff interval by an additive factor of the transmission time of a packet. In order to find the optimum parameters for the SBA scheme, we have studied the optimum backoff intervals as a function of different number of active nodes (N) in a single transmission area with pure ALOHA-type channels.We have found that the optimum backoff interval should be 4N times the transmission time of a packet when the random access channel operates under a pure ALOHA scheme. Based on this result, we have numerically calculated the optimum values of the parameters for SBA, which are independent of N. The SBA scheme operates close to the optimum backoff interval. Furthermore, its operation does not depend on the knowledge of N. The optimum backoff interval and the SBA scheme are also studied by simulative means. It is shown that the SBA scheme out-performs other backoff schemes, such as binary exponential backoff (BEB) and multiplicative increase linear decrease (MILD). As a point of reference, the SBA scheme offers a channel capacity of 0.19 when N is 10, while the MILD scheme can only offer 0.125. The performance gain is about 50%

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