Cloudassisted QoE guarantee scheme based on adaptive crosslayer perceptron of artificial neural network for mobile Internet
 Zhou Silin^{1}Email author
Received: 13 November 2015
Accepted: 6 January 2016
Published: 15 January 2016
Abstract
For improving the system performance of mobile Internet, how to provide the Quality of Experience (QoE) guarantee is an important factor. First, based on artificial neural network and adaptive crosslayer perceptron, we studied the cloudassisted QoE guarantee mechanism. Then, according to the power, we divided the distance and perceptron layers of mobile Internet and cloud into three levels. We showed the state information definition of the mobile node on the basis of the adaptive adjustment perceptron layers. Thirdly, the perceptron network topology would be updated according to the customer service, which would be updated based on the perceptron learning rule for improving the training practice efficiency. The above scheme would guarantee the QoE effectively. The experimental results show that the proposed QoE guarantee mechanism has obvious advantages in terms of throughput, efficiency, and reliability.
Keywords
1 Introduction
Mobile crowding networks can fully study the underlying data service node of the mobile communication system, which could provide better quality assurance for data communications and make full use of the sensing region mobile node communication resources, and is used in various fields, such as metro networks [1], organelle networks [2], and aqueous hydroxyapatitegelatin networks [3]. However, how to motivate users to actively join the mobileaware network [4] and update the network mobile node [5] becomes the key issue.
One the hand, Oh SangHoon [6] proposed an algorithmiclevel approach, which used the multilayer perceptrons with higher order error functions. The class imbalance problem in the context of multilayer perceptron (MLP) neural networks was investigated by Castro Cristiano et al. [7]. Chaudhuri et al. [8] developed a multilayer perceptron model and compared the forecast quality with other neural networks. Mwale et al. [9] applied a combination of selforganizing maps (SOM) and multilayer perceptron artificial neural networks to the Lower Shire floodplain of Malawi for flow and waterlevel forecasting, which was used to extract features from the raw data. The use of multilayer perceptron neural networks to invert dispersion curves obtained via multichannel analysis of surface waves (MASW) for shear Swave velocity profile was proposed by Caylak and Kaftan [10]. Ouadfeul et al. [11] implanted a tentative prediction of daily geomagnetic field and storms by analyzing the International RealTime Magnetic Observatory Network data using the artificial neural network.
On the other hand, the improvement achieved in estimating the volume of clay in the Shurijeh Reservoir Formation is described in article [12], which dealt with an application to a gasproducing well and another nonproducing well in a joint field between Iran and Turkmenistan. Taravat et al. [13] introduced and evaluated a multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI images. The radial basis function network and multilayer perceptron networks were investigated in article [14] for modeling urban change. Fan et al. [15] set up a multilayer perceptron neural network prediction model based on phase reconstruction, which is for carbon price to characterize its strong nonlinearity.
Thirdly, Patel Krishna et al. [16] investigate rewardloss neural response differences among 42 current cocaine users, 35 former cocaine users, and 47 healthy subjects with a functional magnetic resonance imaging monetary incentive delay task. A secure usercentric and socialaware reputationbased incentive scheme for DTNs was proposed in article [17]. The symbiotic architecture called cognitive relaying with frequency incentive for multiple primary users (CRFIM) was studied by Nadkar et al. [18]. The design challenges of incentive mechanisms for encouraging user engagement in userprovided networks were analyzed by Iosifidis et al. [19]. The Quality of Experience (QoE) guarantee was designed for cybersecurity [20].
However, the above research results ignored the relationship between the crowding network topology and perceptron architecture of mobile nodes. Additionally, the QoE guarantee scheme was not researched in depth. Based on the results of the above researches, the cloudassisted QoE guarantee mechanism based on adaptive crosslayer perceptron of artificial neural network was proposed for mobile crowding networks.
The rest of the paper is organized as follows. Section 2 describes the adaptive crosslayer perceptron with artificial neural network. In Section 3, we design the cloudassisted QoE guarantee mechanism based on adaptive crosslayer perceptron. Simulation results are given in Section 4. Finally, we conclude the paper in Section 5.
2 Adaptive crosslayer perceptron with artificial neural network
According to the deployment of n mobile nodes in the mobile crowding network, the mobile node state is defined as M (P, D, L _{N}), where P denotes the transmit power, D represents the distance, and L _{N} represents the perception layer of the mobile node.
Here, let N _{RT} denote the retransmission time. P _{e} denotes the packet error rate. T _{sys} denotes the system delay. P _{sys} denotes the total power of system. TH_{sys} denotes the throughput of system. DP_{size} denotes the packet size.
Here, let V denote the moving speed. The k * p matrix denotes the relationship between the p groups of mobile nodes after second division based on the k group with power. d _{max} denotes the maximum distance between the two mobile nodes. d _{ j → BS} denotes the distance between the mobile node and base station.
Here, f _{T} denotes the signal fusion which is obtained from the transmission power, the distance, and the number of the sensor layer. The fusion is used to evaluate the training effect. Let S _{M1} denote the signal of mobile node. e _{k} denotes the learning error. E[e _{total}] denotes the total learning error of mobile crowding networks.
Here, h _{d} denotes the channel fading factor between mobile node and user.
Here, h _{ s → user} denotes the channel fading factor of neighbor node and receiver. P _{a} denotes the sending power of neighbor nodes. x _{a} denotes the sending signal. n _{e} denotes the number of the neighbor node receiving the feedback signal from the perceptron.
Here, P and W are the input vector and target vector, respectively. np denotes the perceptron before optimization. \( \overline{\mathrm{n}}\overline{\mathrm{p}} \) denotes the perceptron after optimization.
From Fig. 3, we found that the fitness is smaller than one with the withoutadaptive update. As shown in Fig. 4, after the adaptive adjustment and updating of the sensor, the effect is better and the dividing line is clear. These results show that the proposed algorithm can be used to mobile crowding network, which has the advantages of high reliability, fast convergence, and global optimization.
3 Cloudassisted QoE guarantee mechanism of mobile internet
Based on the current demand for data services, the mobile nodes of mobile crowding network can be adaptively adjusted, which would guarantee the Quality of Experience of mobile nodes. Therefore, it is particularly important to study cloudassisted QoE guarantee mechanism with service awareness and dynamic update.
Here, based on the data loss and weight factor, the power, location information, and the layer numbers should be updated in the next round, by broadcasting the full effective incentive information in mobile crowding networks. At the same time, the data transmission error should be mapped to the distance and layer number of the perceptron, which is used to avoid the waste of resources caused by data loss of the mobile node. This scheme could provide the high resource utilization rate and avoid the abandonment of the data service provider.
Here, PE(p,w,L _{N}) denotes the mobile nodes of mobile crowding networks. Let net denote the perceptron networks. The state of the mobile node is updated from the space and the layer number of the perceptron. The perceptron is updated by the q iteration. The mobile state of users are random. The mobile data service nodes could be different in each round. For guaranteeing the system performance of the mobile crowding network, based on updating the perceptron network, the mobile nodes should be an effective incentive which should satisfy the conditions and performance requirements.
 1.
The perceptron transfer function can only accept unilateral incentives, not global optimization.
 2.
With nonlinear problems and classification, the efficiency is low.
 3.
About the face of longtime data transmission service, the number of learning iterations is higher, and the training effect is poor.
 4.
When the input vector and the target vector are not clear, the performance is not stable and it is easy to fluctuate.
Here, f _{IN} denotes the input vector, which could be obtained by the XOR operation of the system power and total power of user mobile node after k division. f _{OUT} denotes the target vector of arriving at the station.

Algorithm: CAQGACL

Input: M(P, d, Ln),{α, β, γ},{p,w}, n, m, k
 1
computing the value of T_{sys},P_{sys},TH_{sys}
 2
completing k group division with power
 3
completing p group division based on primary division
 4
completing thr third division based on input vector, layer number of perceptron
 5
computing the value of x_{S},y_{n}
 6
while i < =n
 7
completing the perceptron initialization of a mobile node
 8
i++
 9
end
 10
while i < =k, and j < =p and l < =L_{N}
 11
completing the initialization of net = newp[i, j, l]
 12
i++, j++, l++;
 13
end
 14
Developing a perceptron learning rule as PW
 15
while i < =k
 16
Δw = w(k, p, L _{ N }) + Δw _{ Task } β
 17
ΔL _{ N } = ΔL _{ N − Task } + λE[e _{ Task }]
 18
updating the perceptron network: \( net={\displaystyle \sum_{i=1}^qP{E}_i\left(p,w,{L}_N\right)} \)
 19
i++
 20
end
 21
end
4 Performance evaluation
Within a 20 km * 10 km wide rectangular area of mobile Internet, 3 base stations are deployed and 50 mobile nodes move randomly from 8 different angles to the region. The moving speed is from 1 to 5 km/h. There are five clouds in the assisted platform. The experimental time is 50 min, the step size of the usermoving nodes in the region is 5, and the number of rectangular area users reached the maximum at 50 min every 10 min. In order to analyze and verify the proposed userincentive mechanism in a mobile crowding network, we compared the throughput rate, execution efficiency, and the symbol error rate of the proposed scheme with the singlelayer perceptron of QoE guarantee mechanism denoted as QGSP.
Figure 7b shows the result of execution efficiency with mobile node scale. The execution efficiency of the proposed CAQGACL is about two times of the QGSP. When the mobile node number is more than 20, execution efficiency reached 95 %, which further increased to 100 %. However, the execution efficiency of QGSP is always hovering at 70 %, and the jitter is serious. Execution efficiency is guaranteed by the user’s incentive mechanism in which the crosslayer interaction is updated in real time and the adaptive adjustment mechanism of the multilayer perceptron.
Figure 7c gives the reliability performance of the two mechanisms with time. We found that the symbol error of QGSP decreased first and then rapidly increased before 15 min, which maintained a high symbol error rate. This is because the static sensor structure of QGSP cannot perceive the mobile node realtime status and the topology of the dynamic network, which resulted in a large number of users exiting the cooperative data transmission. However, the proposed CAQGACL considered the realtime status of the base station, a mobile node, and the task of initiating nodes through a threegrade classification, which has the efficient implementation of realtime optimization and updating perceptron network. Hence, the proposed scheme is more effective to motivate the user node and provide reliable data services.
5 Conclusions
The artificial neural network is applied to the mobile swarm intelligence network, and a highly effective reliable cloudassisted QoE guarantee mechanism is studied. First, the crowding networks are divided with three levels, which are the power of the mobile nodes, the space location, and the number of the sensor layer. Secondly, the creation process of the sensor network was proposed, including the sensor initialization, the perceptron learning, and the perceptron training. After the completion of the cloudassisted QoE guarantee, the mobile crowding network is divided into active user nodes and alternative user nodes. According to the user’s needs, the realtime state of the perceptron network, and crowding network, the cloudassisted QoE guarantee mechanism is put forward. Simulation results show that the proposed mechanism can not only improve the execution efficiency but also reduce the false symbol rate while maintaining high throughput rate.
Declarations
Acknowledgements
This work is supported in part by the scientific research project of Changshu Institute of Technology (QZ1403).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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