Privacy‐Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

Gong, Wei; Qi, Lianyong; Xu, Yanwei

doi:10.1155/2018/3075849

Cited by 155 publications

(103 citation statements)

References 45 publications

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“…Some of the interesting problems are how to adjust subsequence length/ step length and regularize a network and how to use LSTM-FCN, ALSTM-FCN [9], hierarchies of feature [19], or time aware [20,21] to improve the performance of time series classification. In addition, we will try to apply the method of classification service in distributed cloud/fog environment [22][23][24][25][26][27][28][29][30][31][32] in the future.…”

Section: Discussionmentioning

confidence: 99%

Time series classification based on statistical features

Lei

2020

J Wireless Com Network

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This paper presents a statistical feature approach in fully convolutional time series classification (TSC), which is aimed at improving the accuracy and efficiency of TSC. This method is based on fully convolutional neural networks (FCN), and there are the following two properties: statistical features in data preprocessing and finetuning strategies in network training. The key steps are described as follows: firstly, by the window slicing principle, dividing the original time series into multiple equal-length subsequences; secondly, by extracting statistical features on each subsequence, in order to form a new sequence as the input of the neural network, and training neural network by the fine-tuning idea; thirdly, by evaluating the classification performance about test sets; and finally, by comparing the sample sequence complexity and network classification loss accuracy with the FCN using the original sequence. Our experimental results show that the proposed method improved the classification effects of FCN and the residual network (ResNet), which means that it has a generalization ability to the network structures.

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Section: Discussionmentioning

confidence: 99%

Time series classification based on statistical features

Lei

2020

J Wireless Com Network

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“…In our previous work [19,20,21], LSH is combined with user-based CF to make privacy-preserving service recommendation. Likewise, in [22], LSH is combined with item-based CF to build service index table with little privacy and then make service recommendation based on the service index table.…”

Section: Related Workmentioning

confidence: 99%

An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment

Meng

Zhang

et al. 2018

Sensors

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Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach.

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“…With the advantage of the wide measurement range, high spatial and temporal resolution and the real-time data transmission, the ground radar has been widely applied in meteorological industry, including precipitation estimation [13][14][15]. The traditional method employed in precipitation estimation is Z-R relationship model which utilizes the radar echo intensity and rainfall intensity to establish an equation to calculate the precipitation [16][17][18]. The practical Z-R relationship is determined by the distribution of the droplet spectrum while the distribution is restricted by a lot of factors, which means that a constant Z-R relationship in a specific region would bring a large deviation on the precipitation estimation when applied in another region.…”

Section: Introductionmentioning

confidence: 99%

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Tian¹,

Lei²,

Liu³

et al. 2020

J Cloud Comp

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Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.

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Privacy‐Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

Cited by 155 publications

References 45 publications

Time series classification based on statistical features

Time series classification based on statistical features

An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

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