Network-Assisted Mobile Computing with Optimal Uplink Query Processing

Chan, Carri W.; Bambos, Nicholas; Singh, Jatinder

doi:10.1109/tmc.2012.79

Cited by 5 publications

(2 citation statements)

References 39 publications

(30 reference statements)

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“…e experimental results show that the model has high prediction accuracy. Chan et al proposed an optimization algorithm based on color grouping for the data layer [11]. e model classifies different kinds of data streams, marked with different colors.…”

Section: Related Workmentioning

confidence: 99%

Application of SDN Network Traffic Prediction Based on Speech Recognition in Educational Information Optimization Platform

Zhen

Yang

2022

Computational Intelligence and Neuroscience

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This paper constructs a SDN network traffic prediction model based on speech recognition and applies it to the educational information optimization platform. By analyzing the influencing factors of SDN network equipment, communication links, and network traffic, this paper constructs the initial index set of SDN network traffic situation. In the data plane of SDN, the queue management algorithm is used to control the flow. On this basis, an IRS mechanism is proposed based on the advantages of SDN centralized control and the difference of transmission performance requirements between large and small streams. For the transmission of large traffic, IRS adopts greedy routing and multipath routing based on the remaining bandwidth to make the traffic evenly distributed in the network, and IRS adds the scheduling strategy based on IP addressing to avoid packet disorder. Simulation results show that the effectiveness of this algorithm can reach 95.67% at the highest, and the MSE convergence is 0.0021 at the lowest. At the same time, this method completes the quantitative evaluation of SDN network traffic situation, effectively solves the problem that SDN traffic situation labels cannot be determined, and opens a new vision of global state observation for SDN network management. This research can provide some technical support for the educational information optimization platform.

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Section: Related Workmentioning

confidence: 99%

Application of SDN Network Traffic Prediction Based on Speech Recognition in Educational Information Optimization Platform

Zhen

Yang

2022

Computational Intelligence and Neuroscience

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“…Different from traditional wireless data transmission network, mobile communication network is one of the distributed networks with high capacity and high speed. The precise location and mining of its nodes would improve the structure of mobile information network, resulting in better quality of service of the network [1–3].…”

Section: Introductionmentioning

confidence: 99%

Simulation research for telecommunication data mining based on mobile information node

Yang

2018

IET softw.

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As the mobile information nodes change greatly, the mobile data is rather vague and noisy, making more dimensions for the input information in the data mining based on traditional correlation mapping. The great number of dimensions complicates the network structure, which lowers the efficiency of data mining. To improve accuracy, based on mobile information node, it sets the two-layer neural network with non-linear connection weight as the information distinguishing system, in which the relation between any two figures in two data sets would be described. The association attribute groups would be shown in the form of correlation coefficient matrix, while coefficients of difference in the form of the reciprocal of correlation coefficient matrix. Then combine neural network and rough set (RS), analysing the change of mobile information node from moving direction and distance and simplifying the sample set for neural network learning with RS. At the same time, the input and output data is normalised and the redundant data and redundant attributes deleted to get a simplified attribute set. Finally, the authors learn and train with the simplified sample set to ensure the qualified mining accuracy. The result in the simulation experiment would efficiently improve the mining accuracy and efficiency. 1 Introduction These years, as mobile network updates on a fast basis, new demand emerges for the coverage and reliability of mobile network signals. To achieve effective information communication, people are pursuing a more integrated, more minimised, and mobile communication network with overall coverage. All the mobile communication devices must be digitalised and can communicate in a fast and accurate way applying CSTCDMA such as the technology of time division multiple address (TDMA). Different from traditional wireless data transmission network, mobile communication network is one of the distributed networks with high capacity and high speed. The precise location and mining of its nodes would improve the structure of mobile information network, resulting in better quality of service of the network [1-3]. Network information can be transmitted from one node to another. To ensure the correct destination, the one and only network address of the information node must be located [4]. However, just like the changing mobile network and devices, information nodes are changing greatly, which lowers the efficiency of data mining in ultra-large database like those in banks or organisations and complicates the network structure. Mobile communication network is the representation of developed computer network and information processing technology, an integrated subject consisting of transducer, network technology, communication technology, signals, and information processing [5-7]. It is one special type of AdHoc network, in which the nodes distribute in a random way and the data under this circumstance can easily be vague and noisy. Traditional data mining based only on correlation rules [8] would create more dimensions for inform...

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