High-performance flow classification using hybrid clusters in software defined mobile edge computing

Abbasi, Mahdi; Shokrollahi, Azad; Khosravi, Mohammad R.; Menon, Varun G.

doi:10.1016/j.comcom.2020.07.002

Cited by 23 publications

(9 citation statements)

References 30 publications

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“…*e capsule network can retain most of the feature information while fitting compared to the convolutional neural network, where the network layer retains more feature information. So, capsule network-based population collaborative intelligence data classification model for image classification improves the accuracy of population collaborative intelligence data classification [19]. *e process of the data classification algorithm based on capsule network is as follows: R data objects are randomly read from the database, t objects are randomly selected in this dataset of packets of n data objects, and these t objects are also the initial clustering centers we select.…”

Section: Capsule Network-based Data Classification Modelmentioning

confidence: 99%

A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Ren

Liu

2022

Journal of Electrical and Computer Engineering

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Internet of things (IoT) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for IoT applications. Data is the foundation and core of cloud IoT platform applications, and massive multisource heterogeneous IoT data aggregation and storage have basic requirements such as real-time, security, and scalability. This paper focuses on the aggregation and storage methods of massive heterogeneous cloud IoT data, solving the multisource data aggregation problem caused by inconsistent protocols and the heterogeneous data storage problem caused by inconsistent data types. A heterogeneous network protocol adaptation and data aggregation method is proposed for the multisource data aggregation problem caused by protocol inconsistency. A protocol adaptation layer is set up in the IoT virtual gateway to achieve compatibility with multiple types of data aggregation protocols, ensuring adaptive access to different types of IoT nodes, and on this basis, data is transmitted to the cloud IoT platform through a unified interface, shielding the variability of IoT sensing devices. Given the problems of device forgery and malicious tampering in the data aggregation process, we propose a fast authentication and data storage method for IoT devices based on “API key” and implant the device authentication API key into the protocol adaptation layer of the virtual gateway to realize the source authentication of IoT nodes and ensure the authenticity of data.

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Section: Capsule Network-based Data Classification Modelmentioning

confidence: 99%

A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Ren

Liu

2022

Journal of Electrical and Computer Engineering

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“…6. In structured aggregation, the network is modeled as a tree [89]- [92], chains [93]- [96] or in hierarchical clusters [81], [87], [97], [98]. The unstructured approach does not depend on any specific tree or a cluster structure to carry out aggregation.…”

Section: A Taxonomy 1) Data Reduction Schemesmentioning

confidence: 99%

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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“…Most of the existing studies discuss privacy issues and provide some best practices that need to be followed, but a detailed framework for privacy preservation is missing. On the other hand, energy optimization is another problem in 5G that need to be addressed, but there exist only a few studies that discuss energy problem in IIoT and 5G realm, and their main focus is on energy efficiency rather than optimization [39][40]. At the same time, a few more recent studies are focusing to the user privacy issues linked with their daily lives [41][42][43], while at home, using 5G based gadgets and while travelling.…”

Section: B Energy Optimization In 5gmentioning

confidence: 99%

Privacy Protection and Energy Optimization for 5G-Aided Industrial Internet of Things

et al. 2020

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The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today's fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling.

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High-performance flow classification using hybrid clusters in software defined mobile edge computing

Cited by 23 publications

References 30 publications

A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Privacy Protection and Energy Optimization for 5G-Aided Industrial Internet of Things

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