The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform

John Murphy - One of the best experts on this subject based on the ideXlab platform.

  • TRAWL – A Traffic Route Adapted Weighted Learning Algorithm
    2011
    Co-Authors: Enda Fallon, Liam Murphy, John Murphy
    Abstract:

    Media Independent Handover (MIH) is an emerging standard which supports the communication of network-critical events to upper layer mobility protocols. One of the key features of MIH is the event service, which supports predictive network degradation events that are triggered based on link layer metrics. For set Route vehicles, the constrained nature of movement enables a degree of network performance prediction. We propose to capture this performance predictability through a Traffic Route Adapted Weighted Learning (TRAWL) algorithm. TRAWL is a feed forward neural network whose output layer is configurable for both homogeneous and heterogeneous networks. TRAWL uses an unsupervised back propagation learning mechanism, which captures predictable network behavior while also considering dynamic performance characteristics. We evaluate the performance of TRAWL using a commercial metropolitan heterogeneous network. We show that TRAWL has significant performance improvements over existing MIH link triggering mechanisms.

  • WWIC - TRAWL: a Traffic Route adapted weighted learning algorithm
    Lecture Notes in Computer Science, 2011
    Co-Authors: Enda Fallon, Liam Murphy, John Murphy
    Abstract:

    Part 1: Mobility and LTE NetworksInternational audienceMedia Independent Handover (MIH) is an emerging standard which supports the communication of network-critical events to upper layer mobility protocols. One of the key features of MIH is the event service, which supports predictive network degradation events that are triggered based on link layer metrics. For set Route vehicles, the constrained nature of movement enables a degree of network performance prediction. We propose to capture this performance predictability through a Traffic Route Adapted Weighted Learning (TRAWL) algorithm. TRAWL is a feed forward neural network whose output layer is configurable for both homogeneous and heterogeneous networks. TRAWL uses an unsupervised back propagation learning mechanism, which captures predictable network behavior while also considering dynamic performance characteristics. We evaluate the performance of TRAWL using a commercial metropolitan heterogeneous network. We show that TRAWL has significant performance improvements over existing MIH link triggering mechanisms

Enda Fallon - One of the best experts on this subject based on the ideXlab platform.

  • TRAWL – A Traffic Route Adapted Weighted Learning Algorithm
    2011
    Co-Authors: Enda Fallon, Liam Murphy, John Murphy
    Abstract:

    Media Independent Handover (MIH) is an emerging standard which supports the communication of network-critical events to upper layer mobility protocols. One of the key features of MIH is the event service, which supports predictive network degradation events that are triggered based on link layer metrics. For set Route vehicles, the constrained nature of movement enables a degree of network performance prediction. We propose to capture this performance predictability through a Traffic Route Adapted Weighted Learning (TRAWL) algorithm. TRAWL is a feed forward neural network whose output layer is configurable for both homogeneous and heterogeneous networks. TRAWL uses an unsupervised back propagation learning mechanism, which captures predictable network behavior while also considering dynamic performance characteristics. We evaluate the performance of TRAWL using a commercial metropolitan heterogeneous network. We show that TRAWL has significant performance improvements over existing MIH link triggering mechanisms.

  • WWIC - TRAWL: a Traffic Route adapted weighted learning algorithm
    Lecture Notes in Computer Science, 2011
    Co-Authors: Enda Fallon, Liam Murphy, John Murphy
    Abstract:

    Part 1: Mobility and LTE NetworksInternational audienceMedia Independent Handover (MIH) is an emerging standard which supports the communication of network-critical events to upper layer mobility protocols. One of the key features of MIH is the event service, which supports predictive network degradation events that are triggered based on link layer metrics. For set Route vehicles, the constrained nature of movement enables a degree of network performance prediction. We propose to capture this performance predictability through a Traffic Route Adapted Weighted Learning (TRAWL) algorithm. TRAWL is a feed forward neural network whose output layer is configurable for both homogeneous and heterogeneous networks. TRAWL uses an unsupervised back propagation learning mechanism, which captures predictable network behavior while also considering dynamic performance characteristics. We evaluate the performance of TRAWL using a commercial metropolitan heterogeneous network. We show that TRAWL has significant performance improvements over existing MIH link triggering mechanisms

Liam Murphy - One of the best experts on this subject based on the ideXlab platform.

  • TRAWL – A Traffic Route Adapted Weighted Learning Algorithm
    2011
    Co-Authors: Enda Fallon, Liam Murphy, John Murphy
    Abstract:

    Media Independent Handover (MIH) is an emerging standard which supports the communication of network-critical events to upper layer mobility protocols. One of the key features of MIH is the event service, which supports predictive network degradation events that are triggered based on link layer metrics. For set Route vehicles, the constrained nature of movement enables a degree of network performance prediction. We propose to capture this performance predictability through a Traffic Route Adapted Weighted Learning (TRAWL) algorithm. TRAWL is a feed forward neural network whose output layer is configurable for both homogeneous and heterogeneous networks. TRAWL uses an unsupervised back propagation learning mechanism, which captures predictable network behavior while also considering dynamic performance characteristics. We evaluate the performance of TRAWL using a commercial metropolitan heterogeneous network. We show that TRAWL has significant performance improvements over existing MIH link triggering mechanisms.

  • WWIC - TRAWL: a Traffic Route adapted weighted learning algorithm
    Lecture Notes in Computer Science, 2011
    Co-Authors: Enda Fallon, Liam Murphy, John Murphy
    Abstract:

    Part 1: Mobility and LTE NetworksInternational audienceMedia Independent Handover (MIH) is an emerging standard which supports the communication of network-critical events to upper layer mobility protocols. One of the key features of MIH is the event service, which supports predictive network degradation events that are triggered based on link layer metrics. For set Route vehicles, the constrained nature of movement enables a degree of network performance prediction. We propose to capture this performance predictability through a Traffic Route Adapted Weighted Learning (TRAWL) algorithm. TRAWL is a feed forward neural network whose output layer is configurable for both homogeneous and heterogeneous networks. TRAWL uses an unsupervised back propagation learning mechanism, which captures predictable network behavior while also considering dynamic performance characteristics. We evaluate the performance of TRAWL using a commercial metropolitan heterogeneous network. We show that TRAWL has significant performance improvements over existing MIH link triggering mechanisms

Sadao Obana - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Traffic Route Control in QoS Provisioning for Cognitive Radio Technology with Heterogeneous Wireless Systems
    IEICE Transactions on Communications, 2009
    Co-Authors: Toshiaki Yamamoto, Tetsuro Ueda, Sadao Obana
    Abstract:

    As one of the dynamic spectrum access technologies, “cognitive radio technology,” which aims to improve the spectrum efficiency, has been studied. In cognitive radio networks, each node recognizes radio conditions, and according to them, optimizes its wireless communication Routes. Cognitive radio systems integrate the heterogeneous wireless systems not only by switching over them but also aggregating and utilizing them simultaneously. The adaptive control of switchover use and concurrent use of various wireless systems will offer a stable and flexible wireless communication. In this paper, we propose the adaptive Traffic Route control scheme that provides high quality of service (QoS) for cognitive radio technology, and examine the performance of the proposed scheme through the field trials and computer simulations. The results of field trials show that the adaptive Route control according to the radio conditions improves the user IP throughput by more than 20% and reduce the one-way delay to less than 1/6 with the concurrent use of IEEE802.16 and IEEE802.11 wireless media. Moreover, the simulation results assuming hundreds of mobile terminals reveal that the number of users receiving the required QoS of voice over IP (VoIP) service and the total network throughput of FTP users increase by more than twice at the same time with the proposed algorithm. The proposed adaptive Traffic Route control scheme can enhance the performances of the cognitive radio technologies by providing the appropriate communication Routes for various applications to satisfy their required QoS.

  • CrownCom - A proposal of adaptive Traffic Route control scheme in QoS provisioning for cognitive radio technology with heterogeneous wireless systems
    2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2009
    Co-Authors: Toshiaki Yamamoto, Tetsuro Ueda, Sadao Obana
    Abstract:

    As advanced integrated network architecture, “cognitive radio technologies,” which aim to improve the spectrum efficiency, have been studied. In the cognitive radio networks, each node recognizes radio conditions, and according to them, optimizes their wireless communication Routes with the integration of the heterogeneous wireless systems not only by switching over them but also aggregating and utilizing them simultaneously. The Adaptive control of switchover use and concurrent use of various wireless systems will offer a stable and flexible wireless communication. In this paper, we propose the adaptive Traffic Route control scheme that provides high quality of service (QoS) for cognitive radio technology, and examine the performance of the proposed scheme through the field trials and computer simulations. The experimental results of field trials show that the adaptive Route control according to the radio conditions improves the user IP throughput by more than 20% and reduce the one-way delay to less than 1/6 with the concurrent use of IEEE802.16 and IEEE802.11 wireless media. Moreover, the simulation results assuming hundreds of mobile terminals reveal that the number of users that satisfy the required QoS of voice over IP (VoIP) service and the total network throughput of FTP users increase by more than twice at the same time with the proposed algorithm. The proposed adaptive Traffic Route control scheme can provide the cognitive radio technologies the appropriate communication qualities to meet various demands for application QoS.

Toshiaki Yamamoto - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Traffic Route Control in QoS Provisioning for Cognitive Radio Technology with Heterogeneous Wireless Systems
    IEICE Transactions on Communications, 2009
    Co-Authors: Toshiaki Yamamoto, Tetsuro Ueda, Sadao Obana
    Abstract:

    As one of the dynamic spectrum access technologies, “cognitive radio technology,” which aims to improve the spectrum efficiency, has been studied. In cognitive radio networks, each node recognizes radio conditions, and according to them, optimizes its wireless communication Routes. Cognitive radio systems integrate the heterogeneous wireless systems not only by switching over them but also aggregating and utilizing them simultaneously. The adaptive control of switchover use and concurrent use of various wireless systems will offer a stable and flexible wireless communication. In this paper, we propose the adaptive Traffic Route control scheme that provides high quality of service (QoS) for cognitive radio technology, and examine the performance of the proposed scheme through the field trials and computer simulations. The results of field trials show that the adaptive Route control according to the radio conditions improves the user IP throughput by more than 20% and reduce the one-way delay to less than 1/6 with the concurrent use of IEEE802.16 and IEEE802.11 wireless media. Moreover, the simulation results assuming hundreds of mobile terminals reveal that the number of users receiving the required QoS of voice over IP (VoIP) service and the total network throughput of FTP users increase by more than twice at the same time with the proposed algorithm. The proposed adaptive Traffic Route control scheme can enhance the performances of the cognitive radio technologies by providing the appropriate communication Routes for various applications to satisfy their required QoS.

  • CrownCom - A proposal of adaptive Traffic Route control scheme in QoS provisioning for cognitive radio technology with heterogeneous wireless systems
    2009 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2009
    Co-Authors: Toshiaki Yamamoto, Tetsuro Ueda, Sadao Obana
    Abstract:

    As advanced integrated network architecture, “cognitive radio technologies,” which aim to improve the spectrum efficiency, have been studied. In the cognitive radio networks, each node recognizes radio conditions, and according to them, optimizes their wireless communication Routes with the integration of the heterogeneous wireless systems not only by switching over them but also aggregating and utilizing them simultaneously. The Adaptive control of switchover use and concurrent use of various wireless systems will offer a stable and flexible wireless communication. In this paper, we propose the adaptive Traffic Route control scheme that provides high quality of service (QoS) for cognitive radio technology, and examine the performance of the proposed scheme through the field trials and computer simulations. The experimental results of field trials show that the adaptive Route control according to the radio conditions improves the user IP throughput by more than 20% and reduce the one-way delay to less than 1/6 with the concurrent use of IEEE802.16 and IEEE802.11 wireless media. Moreover, the simulation results assuming hundreds of mobile terminals reveal that the number of users that satisfy the required QoS of voice over IP (VoIP) service and the total network throughput of FTP users increase by more than twice at the same time with the proposed algorithm. The proposed adaptive Traffic Route control scheme can provide the cognitive radio technologies the appropriate communication qualities to meet various demands for application QoS.