The Experts below are selected from a list of 1005 Experts worldwide ranked by ideXlab platform

David Simplot-ryl - One of the best experts on this subject based on the ideXlab platform.

  • An Energy Efficient Adaptive Hello Algorithm for Mobile Ad Hoc Networks
    2013
    Co-Authors: Nathalie Mitton, David Simplot-ryl
    Abstract:

    Hello Protocol or neighborhood discovery is essential in wireless ad hoc networks. It makes the rules for nodes to claim their existence/aliveness. In the presence of node mobility, no x optimal Hello frequency and optimal transmission range exist to maintain accurate neighborhood tables while reducing the energy consumption and bandwidth occupation. Thus a Turnover based Frequency and transmission Power Adaptation algorithm (TFPA) is presented in this paper. The method enables nodes in mobile networks to dynamically adjust both their Hello frequency and transmission range depending on the relative speed. In TFPA, each node monitors its neighborhood table to count new neighbors and calculate the turnover ratio. The relationship between relative speed and turnover ratio is formulated and optimal transmission range is derived according to battery consumption model to minimize the overall transmission energy. By taking advantage of the theoretical analysis, the Hello frequency is adapted dynamically in conjunction with the transmission range to maintain accurate neighborhood table and to allow important energy savings. The algorithm is simulated and compared to other state-of-the-art algorithms. The experimental results demonstrate that the TFPA algorithm obtains high neighborhood accuracy with low Hello frequency (at least 11% average reduction) and with the lowest energy consumption. Besides, the TFPA algorithm does not require any additional GPS-like device to estimate the relative speed for each node, hence the hardware cost is reduced.

  • Mobility Prediction Based Neighborhood Discovery in Mobile Ad Hoc Networks
    2011
    Co-Authors: Nathalie Mitton, David Simplot-ryl
    Abstract:

    Hello Protocol is the basic technique for neighborhood discovery in wireless ad hoc networks. It requires nodes to claim their existence/ aliveness by periodic 'Hello' messages. Central to a Hello Protocol is the determination of 'Hello' message transmission rate. No fixed optimal rate exists in the presence of node mobility. The rate should in fact adapt to it, high for high mobility and low for low mobility. In this paper, we propose a novel mobility prediction based Hello Protocol, named ARH (Autoregressive Hello Protocol). Each node predicts its own position by an ever-updated autoregression-based mobility model, and neighboring nodes predict its position by the same model. The node transmits 'Hello' message (for location update) only when the predicted location is too different from the true location (causing topology distortion), triggering mobility model correction on both itself and each of its neighbors. ARH evolves along with network dynamics, and seamlessly tunes itself to the optimal configuration on the fly using local knowledge only. Through simulation, we demonstrate the effectiveness and efficiency of ARH, in comparison with the only competitive Protocol TAP (Turnover based Adaptive Hello Protocol) [9]. With a small model order, ARH achieves the same high neighborhood discovery performance as TAP, with dramatically reduced message overhead (about 50% lower 'Hello' rate).

Nathalie Mitton - One of the best experts on this subject based on the ideXlab platform.

  • An Energy Efficient Adaptive Hello Algorithm for Mobile Ad Hoc Networks
    2013
    Co-Authors: Nathalie Mitton, David Simplot-ryl
    Abstract:

    Hello Protocol or neighborhood discovery is essential in wireless ad hoc networks. It makes the rules for nodes to claim their existence/aliveness. In the presence of node mobility, no x optimal Hello frequency and optimal transmission range exist to maintain accurate neighborhood tables while reducing the energy consumption and bandwidth occupation. Thus a Turnover based Frequency and transmission Power Adaptation algorithm (TFPA) is presented in this paper. The method enables nodes in mobile networks to dynamically adjust both their Hello frequency and transmission range depending on the relative speed. In TFPA, each node monitors its neighborhood table to count new neighbors and calculate the turnover ratio. The relationship between relative speed and turnover ratio is formulated and optimal transmission range is derived according to battery consumption model to minimize the overall transmission energy. By taking advantage of the theoretical analysis, the Hello frequency is adapted dynamically in conjunction with the transmission range to maintain accurate neighborhood table and to allow important energy savings. The algorithm is simulated and compared to other state-of-the-art algorithms. The experimental results demonstrate that the TFPA algorithm obtains high neighborhood accuracy with low Hello frequency (at least 11% average reduction) and with the lowest energy consumption. Besides, the TFPA algorithm does not require any additional GPS-like device to estimate the relative speed for each node, hence the hardware cost is reduced.

  • Mobility Prediction Based Neighborhood Discovery in Mobile Ad Hoc Networks
    2011
    Co-Authors: Nathalie Mitton, David Simplot-ryl
    Abstract:

    Hello Protocol is the basic technique for neighborhood discovery in wireless ad hoc networks. It requires nodes to claim their existence/ aliveness by periodic 'Hello' messages. Central to a Hello Protocol is the determination of 'Hello' message transmission rate. No fixed optimal rate exists in the presence of node mobility. The rate should in fact adapt to it, high for high mobility and low for low mobility. In this paper, we propose a novel mobility prediction based Hello Protocol, named ARH (Autoregressive Hello Protocol). Each node predicts its own position by an ever-updated autoregression-based mobility model, and neighboring nodes predict its position by the same model. The node transmits 'Hello' message (for location update) only when the predicted location is too different from the true location (causing topology distortion), triggering mobility model correction on both itself and each of its neighbors. ARH evolves along with network dynamics, and seamlessly tunes itself to the optimal configuration on the fly using local knowledge only. Through simulation, we demonstrate the effectiveness and efficiency of ARH, in comparison with the only competitive Protocol TAP (Turnover based Adaptive Hello Protocol) [9]. With a small model order, ARH achieves the same high neighborhood discovery performance as TAP, with dramatically reduced message overhead (about 50% lower 'Hello' rate).

Simplot-ryl David - One of the best experts on this subject based on the ideXlab platform.

  • Mobility Prediction Based Neighborhood Discovery in Mobile Ad Hoc Networks
    'Springer Science and Business Media LLC', 2011
    Co-Authors: Mitton Nathalie, Simplot-ryl David
    Abstract:

    Part 5: Mobility ModelingInternational audienceHello Protocol is the basic technique for neighborhood discovery in wireless ad hoc networks. It requires nodes to claim their existence/ aliveness by periodic 'Hello' messages. Central to a Hello Protocol is the determination of 'Hello' message transmission rate. No fixed optimal rate exists in the presence of node mobility. The rate should in fact adapt to it, high for high mobility and low for low mobility. In this paper, we propose a novel mobility prediction based Hello Protocol, named ARH (Autoregressive Hello Protocol). Each node predicts its own position by an ever-updated autoregression-based mobility model, and neighboring nodes predict its position by the same model. The node transmits 'Hello' message (for location update) only when the predicted location is too different from the true location (causing topology distortion), triggering mobility model correction on both itself and each of its neighbors. ARH evolves along with network dynamics, and seamlessly tunes itself to the optimal configuration on the fly using local knowledge only. Through simulation, we demonstrate the effectiveness and efficiency of ARH, in comparison with the only competitive Protocol TAP (Turnover based Adaptive Hello Protocol) [9]. With a small model order, ARH achieves the same high neighborhood discovery performance as TAP, with dramatically reduced message overhead (about 50% lower 'Hello' rate)

Mitton Nathalie - One of the best experts on this subject based on the ideXlab platform.

  • Mobility Prediction Based Neighborhood Discovery in Mobile Ad Hoc Networks
    'Springer Science and Business Media LLC', 2011
    Co-Authors: Mitton Nathalie, Simplot-ryl David
    Abstract:

    Part 5: Mobility ModelingInternational audienceHello Protocol is the basic technique for neighborhood discovery in wireless ad hoc networks. It requires nodes to claim their existence/ aliveness by periodic 'Hello' messages. Central to a Hello Protocol is the determination of 'Hello' message transmission rate. No fixed optimal rate exists in the presence of node mobility. The rate should in fact adapt to it, high for high mobility and low for low mobility. In this paper, we propose a novel mobility prediction based Hello Protocol, named ARH (Autoregressive Hello Protocol). Each node predicts its own position by an ever-updated autoregression-based mobility model, and neighboring nodes predict its position by the same model. The node transmits 'Hello' message (for location update) only when the predicted location is too different from the true location (causing topology distortion), triggering mobility model correction on both itself and each of its neighbors. ARH evolves along with network dynamics, and seamlessly tunes itself to the optimal configuration on the fly using local knowledge only. Through simulation, we demonstrate the effectiveness and efficiency of ARH, in comparison with the only competitive Protocol TAP (Turnover based Adaptive Hello Protocol) [9]. With a small model order, ARH achieves the same high neighborhood discovery performance as TAP, with dramatically reduced message overhead (about 50% lower 'Hello' rate)

Wuchi Feng - One of the best experts on this subject based on the ideXlab platform.

  • achieving faster failure detection in ospf networks
    International Conference on Communications, 2003
    Co-Authors: Mukul Goyal, K K Ramakrishnan, Wuchi Feng
    Abstract:

    A network running OSPF takes several tens of seconds to recover from a failure, using the current default parameter settings. The main component of this delay is the time required to detect a failure using the Hello Protocol. Reducing the value of the Hellointerval can speed up the failure detection time. However, too small a value of the Hellointerval can result in an increase in network congestion, potentially causing multiple consecutive Hellos to be lost. This can lead to a false breakdown of adjacencies between routers. Such false alarms not only disrupt network traffic by causing unnecessary routing changes, but also increase the processing load on the routers, which may potentially lead to routing instability. In this paper, we investigate the following question - what is the optimal value for the Hellointerval that will lead to fast failure detection in the network, while keeping occurrences of false alarms within acceptable limits? We examine the impact of both network congestion and the network topology on the optimal value for the Hellointerval. Additionally, we investigate the effectiveness of faster failure detection in achieving fast failure recovery in OSPF networks.