Scalable Data Storage Design for Nonstationary IoT Environment With Adaptive Security and Reliability

Tchernykh, Andrei; Babenko, Mikhail; Chervyakov, Nikolay I.; Miranda-López, Vanessa; Avetisyan, Arutyun; Drozdov, Alexander Yu.; Rivera-Rodríguez, Raúl; Радченко, Глеб; Du, Z.

doi:10.1109/jiot.2020.2981276

Cited by 52 publications

(47 citation statements)

References 75 publications

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“…Shi et al [18] have analyzed the system performance as well as the budget control in multi-cloud on a global scale. Tchernykh et al [19] have presented a multicloud storage architecture that consolidates multiple systems with various failure detection/recovery tools. They have proposed a multi-objective optimization mechanism to allocate workloads.…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Task Allocation for Multi-Cloud Networks

Mishra

Alsayat

et al. 2020

IEEE Access

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In recent years, the growth rate of Cloud computing technology is increasing exponentially, mainly for its extraordinary services with expanding computation power, the possibility of massive storage, and all other services with the maintained quality of services (QoSs). The task allocation is one of the best solutions to improve different performance parameters in the cloud, but when multiple heterogeneous clouds come into the picture, the allocation problem becomes more challenging. This research work proposed a resource-based task allocation algorithm. The same is implemented and analyzed to understand the improved performance of the heterogeneous multi-cloud network. The proposed task allocation algorithm (Energy-aware Task Allocation in Multi-Cloud Networks (ETAMCN)) minimizes the overall energy consumption and also reduces the makespan. The results show that the makespan is approximately overlapped for different tasks and does not show a significant difference. However, the average energy consumption improved through ETAMCN is approximately 14%, 6.3%, and 2.8% in opposed to the random allocation algorithm, Cloud Z-Score Normalization (CZSN) algorithm, and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS), respectively. An observation of the average SLAviolation of ETAMCN for different scenarios is performed.

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Section: Related Workmentioning

confidence: 99%

Energy-Aware Task Allocation for Multi-Cloud Networks

Mishra

Alsayat

et al. 2020

IEEE Access

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“…An alternative solution is modular error correction codes constructed using the RRNS [15]. An additional advantage of RRNS for the design of distributed storage systems is that it is a secret sharing scheme that provides data security [16].…”

Section: B Error Correction Codementioning

confidence: 99%

“…The first goal is to increase the data encoding and decoding speeds from a weighted number system to RRNS and vice versa [39] [14] [16] [46]. The second aim is related to the reduction in the computational complexity of the error correction algorithm [37] [38] [17].…”

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confidence: 99%

2Lbp-RRNS: Two-Levels RRNS With Backpropagation for Increased Reliability and Privacy-Preserving of Secure Multi-Clouds Data Storage

et al. 2020

Self Cite

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Cloud storage as service is the mainstream technology used to retain digital data. However, there are significant risks for confidentiality, integrity, and availability violation associated with the loss of information, denial of access, technical failures, etc. In this article, we propose a two-level 2Lbp-RRNS scheme based on a Redundant Residue Number System with a backpropagation and hamming distance mechanisms for increasing reliability of a configurable and secure multi-cloud data storage. We provide a theoretical analysis of the 2Lbp-RRNS solution as an extension of the classical 2L-RRNS and a variant of fully homomorphic encryption for privacy-preserving, parallel processing, and scalability. We formulate, explain, and prove its main properties to extend existing knowledge within the limits of the critical bounding RRNS assumptions. We show that 2Lbp-RRNS can identify and recover more errors than traditional 2L-RRNS. We provide the upper bounds of the traditional threshold 2L-RRNS and our solution to estimate the number of detectable and correctable errors. We study various data access scenarios and show that it detects 1.58x and corrects 3.37x more errors than 2L-RRNS, on average. We also provide efficient implementations of encoding and decoding algorithms MRC8, and MRC16 based on the Mixed-Radix system, Finite Ring Neuronal Network, and signed binary window method. We evaluate encoding/decoding speeds using three algorithms: Mignotte, MRC8, and MRC16. The experimental system includes seven cloud storages: DropBox, GoogleDrive, OneDrive, Sharefile, Box, Egnyte, and Salesforce. To assess the efficiency of the system on real data, we vary scenarios of the first and second levels. The results show that our solution outperforms MRC8 by 2.53x (1.78x), and Mignotte by 4.83x (11.43x) for the encoding (decoding) speed, respectively.

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“…The potential opportunities offered by the abundance of sensors, actuators and communications in the Internet of Things (IoT) environment produces massive and non-stationary input (image) data [ 1 ], which demands real-time, online and adaptive analysis approaches [ 2 ]. More specifically, real-time and dynamic image classification has manifested as an imperative requirement for analyzing the several critical applications [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Jameel

Hashmani

Rehman

et al. 2020

Sensors

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In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.

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Scalable Data Storage Design for Nonstationary IoT Environment With Adaptive Security and Reliability

Cited by 52 publications

References 75 publications

Energy-Aware Task Allocation for Multi-Cloud Networks

Energy-Aware Task Allocation for Multi-Cloud Networks

2Lbp-RRNS: Two-Levels RRNS With Backpropagation for Increased Reliability and Privacy-Preserving of Secure Multi-Clouds Data Storage

An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

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