1,150 research outputs found

    Cross-layer design of multi-hop wireless networks

    Get PDF
    MULTI -hop wireless networks are usually defined as a collection of nodes equipped with radio transmitters, which not only have the capability to communicate each other in a multi-hop fashion, but also to route each others’ data packets. The distributed nature of such networks makes them suitable for a variety of applications where there are no assumed reliable central entities, or controllers, and may significantly improve the scalability issues of conventional single-hop wireless networks. This Ph.D. dissertation mainly investigates two aspects of the research issues related to the efficient multi-hop wireless networks design, namely: (a) network protocols and (b) network management, both in cross-layer design paradigms to ensure the notion of service quality, such as quality of service (QoS) in wireless mesh networks (WMNs) for backhaul applications and quality of information (QoI) in wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of this Ph.D. dissertation, different network settings are used as illustrative examples, however the proposed algorithms, methodologies, protocols, and models are not restricted in the considered networks, but rather have wide applicability. First, this dissertation proposes a cross-layer design framework integrating a distributed proportional-fair scheduler and a QoS routing algorithm, while using WMNs as an illustrative example. The proposed approach has significant performance gain compared with other network protocols. Second, this dissertation proposes a generic admission control methodology for any packet network, wired and wireless, by modeling the network as a black box, and using a generic mathematical 0. Abstract 3 function and Taylor expansion to capture the admission impact. Third, this dissertation further enhances the previous designs by proposing a negotiation process, to bridge the applications’ service quality demands and the resource management, while using WSNs as an illustrative example. This approach allows the negotiation among different service classes and WSN resource allocations to reach the optimal operational status. Finally, the guarantees of the service quality are extended to the environment of multiple, disconnected, mobile subnetworks, where the question of how to maintain communications using dynamically controlled, unmanned data ferries is investigated

    Routage adaptatif et stabilité dans les réseaux maillés sans fil

    Full text link
    GrĂące Ă  leur flexibilitĂ© et Ă  leur facilitĂ© d’installation, les rĂ©seaux maillĂ©s sans fil (WMNs) permettent un dĂ©ploiement d’une infrastructure Ă  faible coĂ»t. Ces rĂ©seaux Ă©tendent la couverture des rĂ©seaux filaires permettant, ainsi, une connexion n’importe quand et n’importe oĂč. Toutefois, leur performance est dĂ©gradĂ©e par les interfĂ©rences et la congestion. Ces derniers causent des pertes de paquets et une augmentation du dĂ©lai de transmission d’une façon drastique. Dans cette thĂšse, nous nous intĂ©ressons au routage adaptatif et Ă  la stabilitĂ© dans ce type de rĂ©seaux. Dans une premiĂšre partie de la thĂšse, nous nous intĂ©ressons Ă  la conception d’une mĂ©trique de routage et Ă  la sĂ©lection des passerelles permettant d’amĂ©liorer la performance des WMNs. Dans ce contexte nous proposons un protocole de routage Ă  la source basĂ© sur une nouvelle mĂ©trique. Cette mĂ©trique permet non seulement de capturer certaines caractĂ©ristiques des liens tels que les interfĂ©rences inter-flux et intra-flux, le taux de perte des paquets mais Ă©galement la surcharge des passerelles. Les rĂ©sultats numĂ©riques montrent que la performance de cette mĂ©trique est meilleure que celle des solutions proposĂ©es dans la littĂ©rature. Dans une deuxiĂšme partie de la thĂšse, nous nous intĂ©ressons Ă  certaines zones critiques dans les WMNs. Ces zones se trouvent autour des passerelles qui connaissent une concentration plus Ă©levĂ© du trafic ; elles risquent de provoquer des interfĂ©rences et des congestions. À cet Ă©gard, nous proposons un protocole de routage proactif et adaptatif basĂ© sur l’apprentissage par renforcement et qui pĂ©nalise les liens de mauvaise qualitĂ© lorsqu’on s’approche des passerelles. Un chemin dont la qualitĂ© des liens autour d’une passerelle est meilleure sera plus favorisĂ© que les autres chemins de moindre qualitĂ©. Nous utilisons l’algorithme de Q-learning pour mettre Ă  jour dynamiquement les coĂ»ts des chemins, sĂ©lectionner les prochains nƓuds pour faire suivre les paquets vers les passerelles choisies et explorer d’autres nƓuds voisins. Les rĂ©sultats numĂ©riques montrent que notre protocole distribuĂ©, prĂ©sente de meilleurs rĂ©sultats comparativement aux protocoles prĂ©sentĂ©s dans la littĂ©rature. Dans une troisiĂšme partie de cette thĂšse, nous nous intĂ©ressons aux problĂšmes d’instabilitĂ© des rĂ©seaux maillĂ©s sans fil. En effet, l’instabilitĂ© se produit Ă  cause des changements frĂ©quents des routes qui sont causĂ©s par les variations instantanĂ©es des qualitĂ©s des liens dues Ă  la prĂ©sence des interfĂ©rences et de la congestion. Ainsi, aprĂšs une analyse de l’instabilitĂ©, nous proposons d’utiliser le nombre de variations des chemins dans une table de routage comme indicateur de perturbation des rĂ©seaux et nous utilisons la fonction d’entropie, connue dans les mesures de l’incertitude et du dĂ©sordre des systĂšmes, pour sĂ©lectionner les routes stables. Les rĂ©sultats numĂ©riques montrent de meilleures performances de notre protocole en comparaison avec d’autres protocoles dans la littĂ©rature en termes de dĂ©bit, dĂ©lai, taux de perte des paquets et l’indice de Gini.Thanks to their flexibility and their simplicity of installation, Wireless Mesh Networks (WMNs) allow a low cost deployment of network infrastructure. They can be used to extend wired networks coverage allowing connectivity anytime and anywhere. However, WMNs may suffer from drastic performance degradation (e.g., increased packet loss ratio and delay) because of interferences and congestion. In this thesis, we are interested in adaptive routing and stability in WMNs. In the first part of the thesis, we focus on defining new routing metric and gateway selection scheme to improve WMNs performance. In this context, we propose a source routing protocol based on a new metric which takes into account packet losses, intra-flow interferences, inter-flow interferences and load at gateways together to select best paths to best gateways. Simulation results show that the proposed metric improves the network performance and outperforms existing metrics in the literature. In the second part of the thesis, we focus on critical zones, in WMNs, that consist of mesh routers which are located in neighborhoods of gateways where traffic concentration may occur. This traffic concentration may increase congestion and interferences excessively on wireless channels around the gateways. Thus, we propose a proactive and adaptive routing protocol based on reinforcement learning which increasingly penalizes links with bad quality as we get closer to gateways. We use Q-learning algorithm to dynamically update path costs and to select the next hop each time a packet is forwarded toward a given gateway; learning agents in each mesh router learn the best link to forward an incoming packet and explore new alternatives in the future. Simulation results show that our distributed routing protocol is less sensitive to interferences and outperforms existing protocols in the literature. In the third part of this thesis, we focus on the problems of instability in WMNs. Instability occurs when routes flapping are frequent. Routes flapping are caused by the variations of link quality due to interferences and congestion. Thus, after analyzing factors that may cause network instability, we propose to use the number of path variations in routing tables as an indicator of network instability. Also, we use entropy function, usually used to measure uncertainty and disorder in systems, to define node stability, and thus, select the most stable routes in the WMNs. Simulation results show that our stability-based routing protocol outperforms existing routing protocols in the literature in terms of throughput, delay, loss rate, and Gini index

    Optimization and Learning in Energy Efficient Cognitive Radio System

    Get PDF
    Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network

    Routing Protocols for Underwater Acoustic Sensor Networks: A Survey from an Application Perspective

    Get PDF
    Underwater acoustic communications are different from terrestrial radio communications; acoustic channel is asymmetric and has large and variable end‐to‐end propagation delays, distance‐dependent limited bandwidth, high bit error rates, and multi‐path fading. Besides, nodes’ mobility and limited battery power also cause problems for networking protocol design. Among them, routing in underwater acoustic networks is a challenging task, and many protocols have been proposed. In this chapter, we first classify the routing protocols according to application scenarios, which are classified according to the number of sinks that an underwater acoustic sensor network (UASN) may use, namely single‐sink, multi‐sink, and no‐sink. We review some typical routing strategies proposed for these application scenarios, such as cross‐layer and reinforcement learning as well as opportunistic routing. Finally, some remaining key issues are highlighted
    • 

    corecore