A Tensor Computation and Optimization Model for Cyber-Physical-Social Big Data

Wang, Xiaokang; Yang, Laurence T.; Chen, Xingyu; Han, Jian-Jun; Feng, Jun

doi:10.1109/tsusc.2017.2777503

Cited by 62 publications

(16 citation statements)

References 25 publications

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“…With the development of IoT and data computing technologies [16][17][18], researchers have access to achieve more data with various types and large amount. However, imbalanced data problem leads to artificial intelligence models built on these data which behave extremely poor in performance.…”

Section: Smote Methodsmentioning

confidence: 99%

SMOTE-Boost-based sparse Bayesian model for flood prediction

Ding

Feng

2020

J Wireless Com Network

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With a significant development of big data analysis and cloud-fog-edge computing, human-centered computing (HCC) has been a hot research topic worldwide. Essentially, HCC is a cross-disciplinary research domain, in which the core idea is to build an efficient interaction among persons, cyber space, and real world. Inspired by the improvement of HCC on big data analysis, we intend to involve related core and technologies to help solve one of the most important issues in the real world, i.e., flood prediction. To minimize the negative impacts brought by floods, researchers pay special attention to improve the accuracy of flood forecasting with quantity of technologies including HCC. However, historical flood data is essentially imbalanced. Imbalanced data causes machine learning classifiers to be more biased towards patterns with majority samples, resulting in poor classification of pattern with minority samples. In this paper, we propose a novel Synthetic Minority Over-sampling Technique (SMOTE)-Boost-based sparse Bayesian model to perform flood prediction with both high accuracy and robustness. The proposed model consists of three modules, namely, SMOTE-based data enhancement, AdaBoost training strategy, and sparse Bayes model construction. In SMOTE-based data enhancement, we adopt a SMOTE algorithm to effectively cover diverse data modes and generate more samples for prediction pattern with minority samples, which greatly alleviates the problem of imbalanced data by involving experts' analysis and users' intentions. During AdaBoost training strategy, we propose a specifically designed AdaBoost training strategy for sparse Bayesian model, which not only adaptively and inclemently increases prediction ability of Bayesian model, but also prevents its over-fitting performance. Essentially, the design of AdaBoost strategy helps keep balance between prediction ability and model complexity, which offers different but effective models over diverse rivers and users. Finally, we construct a sparse Bayesian model based on AdaBoost training strategy, which could offer flood prediction results with high rationality and robustness. We demonstrate the accuracy and effectiveness of the proposed model for flood prediction by conducting experiments on a collected dataset with several comparative methods.

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Section: Smote Methodsmentioning

confidence: 99%

SMOTE-Boost-based sparse Bayesian model for flood prediction

Ding

Feng

2020

J Wireless Com Network

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“…Data need to be offloaded to the remote cloud data centers [2][3][4][5]. A major benefit of big data is to provide timely information and proactive services for humans [6]. The advantages of cloud computing in data processing are obvious.…”

Section: Related Workmentioning

confidence: 99%

“…However, because of the rapid rise in the volume of data, faster and more efficient computational techniques are required. A major benefit of big data is to provide timely information and proactive services for humans [6]. The advantages of cloud computing in data processing are obvious.…”

Section: Introductionmentioning

confidence: 99%

An efficient method for meteorological nephogram recognition in cloud environment

Wang

Ren

et al. 2019

J Wireless Com Network

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Nephogram could provide important information for meteorological business, and nephogram recognition is a kind of challenge in the meteorological industry. In this paper, a selective neural network ensemble method based on K-means and Hadoop processing technology is proposed. This method combines the neural network, K-means clustering and AdaBoost in cloud environment. The experimental results show that the recognition precision of the method proposed by this paper is higher than that of traditional method in a stand-alone environment.

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“…W ITH the rapid proliferation of intelligent sensors and devices, cyber-physical systems (CPS), the integrated systems of cyber space and physical space, have attracted tremendous attention [1]. As the extension of the CPS, cyberphysical-social systems (CPSS) are the integrated systems of cyber space, physical space, and social space [2,3]. CPSS involve symbiotic networks of human (social networks), intelligent sensors, switches, routers, mobile phone terminals, and information networks.…”

Section: Introductionmentioning

confidence: 99%

Secure Outsourced Principal Eigentensor Computation for Cyber-Physical-Social Systems

Feng

Yang

Zhu

et al. 2021

IEEE Trans. Sustain. Comput.

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Cyber-physical-social systems (CPSS) are revolutionizing the relationships between humans, computers and things. Outsourcing computation to cloud can offer resources-constrained enterprises and consumers sustainable computing in CPSS. However, ensuring the security of data in such an outsourced environment remains a research challenge. Principal eigentensor computation has emerged as a powerful tool dealing with multidimensional cyber-physical-social systems data. In this paper, we present two novel secure principal eigentensor computation (SPEC) schemes for sustainable CPSS. To the best of our knowledge, this is the first effort to address SPEC over encrypted data in cloud without the interaction need between multiple users and cloud. More specifically, we leverage cloud server and trusted hardware component to design a collaborative cloud model. Using the model, we propose (1) a basic SPEC scheme based on homomorphic computing and (2) an efficient SPEC scheme that combines the advantages of homomorphic computing and garbled circuits, and exploits packing technology to reduce computational cost. Finally, we theoretically and empirically analyze the security and efficiency of our SPEC schemes. Findings demonstrate that the proposed schemes provide a secure and efficient way of outsourcing computation for CPSS. In addition, from the cloud user's perspective, our proposal is lightweight.

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A Tensor Computation and Optimization Model for Cyber-Physical-Social Big Data

Cited by 62 publications

References 25 publications

SMOTE-Boost-based sparse Bayesian model for flood prediction

SMOTE-Boost-based sparse Bayesian model for flood prediction

An efficient method for meteorological nephogram recognition in cloud environment

Secure Outsourced Principal Eigentensor Computation for Cyber-Physical-Social Systems

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