A reconstructing approach to end‐to‐end network traffic based on multifractal wavelet model

Nie, Laisen; Jiang, Dingde; Guo, Lei; Xu, Zhengzheng

doi:10.1002/dac.2558

Cited by 6 publications

(6 citation statements)

References 37 publications

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“…Each sensor node only needs to report its initial position and orientation and receives the adjusted sensing orientation. And there is not any network traffic problem [14][15][16] in this simulation network. The simulation parameters of the three coverage-enhancing algorithms are shown in Table I.…”

Section: Parameters Of Simulation Scenementioning

confidence: 95%

Genetic simulated annealing‐based coverage‐enhancing algorithm for multimedia directional sensor networks

Zhang

Chen

Jia

2014

Int J Communication

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SUMMARYMultimedia directional sensor network is one kind of directional sensing systems, whose coverage scheme is quite different from the omnidirectional sensing system. And it is often used in atrocious environmental surveillance, such as nuclear contaminative areas, where people can hardly arrive. In this paper, a genetic simulated annealing-based coverage-enhancing algorithm (GSACEA) is proposed as a coverageenhancing method in multimedia directional sensor networks. Firstly, GSACEA combines the genetic algorithm and simulated annealing algorithm into an algorithm with new architecture. Then, the proposed GSACEA is applied for the purpose of coverage-enhancing in the case of directional sensor networks with rotational direction-adjustable model. Finally, after series actions of genetic simulated annealing, the proposed method can find the approximate solution to the best area coverage rate. And according to the results of simulations, which compared the proposed method with several other classic coverage-enhancing methods in directional sensor networks, it could be concluded that GSACEA can achieve the highest area coverage rate of directional sensor networks and reduce the iterative computing times simultaneously.

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Section: Parameters Of Simulation Scenementioning

confidence: 95%

Genetic simulated annealing‐based coverage‐enhancing algorithm for multimedia directional sensor networks

Zhang

Chen

Jia

2014

Int J Communication

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“…Figure depicts the framework of our method. According to our framework, we first select a subset of the measured OD flows depending on the Bernoulli random matrix and power laws , and then, a linear system will be built via these OD flows. The obtained linear system is ill‐posed and underconstrained.…”

Section: Our Methodologymentioning

confidence: 99%

“…the measured OD flows depending on the Bernoulli random matrix and power laws [19][20][21], and then, a linear system will be built via these OD flows. The obtained linear system is ill-posed and underconstrained.…”

Section: Our Methodologymentioning

confidence: 99%

A compressive sensing‐based approach to end‐to‐end network traffic reconstruction utilising partial measured origin‐destination flows

Nie

Jiang

Guo

2014

Trans Emerging Tel Tech

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Traffic matrices describe the volume of traffic flowing from origin nodes to destination nodes. As one of the most crucial input parameters for network activities, it is employed to perform a great number of network management tasks. However, traffic matrix estimation is unavailable and has a highly ill‐posed nature, which means that it is significantly difficult to ensure the accuracy for estimating traffic matrices. Thus, it is a challenge to obtain the precise end‐to‐end network traffic. In this paper, we investigate the problem of network traffic estimation and propose a novel compressive sensing‐based approach using partial measured origin‐destination (OD) flows. As a purely data‐driven method, we first propose a strategy to select a small subset of the measured OD flows. We then put forward an optimal greedy adaptive dictionary learning algorithm in order to make the traffic matrix sparse. Furthermore, we model the traffic matrix estimation problem by a fundamental inference problem, which is ill‐posed and obeys the constraints of compressive sensing. In other words, the estimation model is a convex optimisation problem. In the process, we validate the performance of our method by real traffic data. Simulation results indicate that our method can trace the shape of each OD flow much more precisely. Copyright © 2014 John Wiley & Sons, Ltd.

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“…Although the methods proposed above can improve the multicast performance of wireless multi-hop networks, they are significantly difficult to make the optimal multicast communication with minimum energy consumption. Especially due to dynamic network traffic [18,19], to perform high energy-efficient multicast has a larger challenge. Compared to these methods, our approach builds multicast paths from a global perspective.…”

Section: Introductionmentioning

confidence: 99%

A reconstructing approach to end‐to‐end network traffic based on multifractal wavelet model

Cited by 6 publications

References 37 publications

Genetic simulated annealing‐based coverage‐enhancing algorithm for multimedia directional sensor networks

Genetic simulated annealing‐based coverage‐enhancing algorithm for multimedia directional sensor networks

A compressive sensing‐based approach to end‐to‐end network traffic reconstruction utilising partial measured origin‐destination flows

A multicast delivery approach with minimum energy consumption for wireless multi-hop networks

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