A Smart Collaborative Authentication Framework for Multi-Dimensional Fine-Grained Control

Ai, Zhengyang; Liu, Ying; Liu, Chang; Lin, Fuhong; Song, Fei

doi:10.1109/access.2019.2962247

Cited by 15 publications

(9 citation statements)

References 43 publications

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“…Compared with candidate algorithms, the simulation results show that the proposed intelligent adaptive algorithm can achieve superior performance in terms of weighted reward, service latency, and power consumption. In future work, we will further research other problems in the cloud-aware MFC network, which is related to security and applicability [26][27][28]. Figure 17: The curve of average power consumption of user device with respect to episode.…”

Section: Discussionmentioning

confidence: 99%

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks

Qin

Zhou

et al. 2020

Wireless Communications and Mobile Computing

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With the increasing popularity of terminals and applications, the corresponding requirements of services have been growing significantly. In order to improve the quality of services in resource restrained user devices and reduce the large latency of service migration caused by long distance in cloud computing, mobile fog computing (MFC) is presented to provide supplementary resources by adding a fog layer with several servers near user devices. Focusing on cloud-aware MFC networks with multiple servers, we formulate a problem with the optimization objective to improve the quality of service, relieve the restrained resource of user device, and balance the workload of participant server. In consideration of the data size of remaining task, the power consumption of user device, and the appended workload of participant server, this paper designs a machine learning-based algorithm which aims to generate intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. Based on the proposed algorithm and software-defined networking technology, the tasks can be executed cooperatively by the user device and the servers in the MFC network. Besides, we conducted some experiments to verify the algorithm effectiveness under different numerical parameters including task arrival rate, avaliable server workload, and wireless channel condition. The simulation results show that the proposed intelligent adaptive algorithm achieves a superior performance in terms of latency and power consumption compared to candidate algorithms.

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Section: Discussionmentioning

confidence: 99%

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks

Qin

Zhou

et al. 2020

Wireless Communications and Mobile Computing

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“…Some researchers have also begun to adopt advanced technologies or strategies to address security mechanisms in industrial cooperation. Ai, Z. et al considering that potential attacking opportunities triggered by this progress are severely impacting the security fortress of current networks, especially in the edge access part, propose a Multi-dimensional Fine-grained Control (MFC) framework to strengthen safety and reliability in Radio Access Networks (RANs) [33]. An identifier mapping mechanism was designed to achieve network isolation, and the MFC framework is established to describe the model structure and implementation processes.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing

Ran

Bai

2020

Sensors

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In this paper, we propose an optimization framework of vehicular fog computing and a cooperation vehicular network model. We aim to improve the performance of vehicular fog computing and solve the problem that the data of the vehicle collaborative network is difficult to obtain. This paper applies the hypergraph theory to study the underlying structure, considering the social characteristics of the vehicles and vehicle communication. Since the vehicles join the network in accordance with the Poisson process law, the model is analyzed by using Poisson stochastic process and mean field theory. This paper uses MATLAB to simulate the evolution process of cooperative networks. The results show that the vehicle's super-degree in vehicular fog computing has scale-free characteristics. Through this model, the vehicle cooperation situation can be analyzed, and the vehicle dynamics can be accurately predicted to further improve the performance of vehicular fog computing. The model can be transformed into some complex network models by adjusting the parameters. It has strong universality and has certain reference significance for the research on the related characteristics of VANETs and the theoretical research of the cooperative network.2 of 17 to higher network delays. For vehicles that are using autonomous driving in the Internet of Vehicles, high latency is likely to cause serious traffic accidents. Cellular network can provide limited enhanced communication, mainly controlled by network operators, which is inefficient from the application point of view [8]. Mobile cloud computing can bring rich computing resources to mobile users [9], but it is costly and time consuming to upload real-time information [10].Fog computing can effectively solve the above problems, by deploying a fog server with storage and computing near the user to receive and process user data [11]. The deployment location of the fog server is closer to the user, which can save a lot of data transmission time. The deployment of fog computing nodes in the connected vehicle network has become a popular trend in the research of VANETs. We finally proposed a novel concept of vehicular fog computing (VFC), which uses vehicles as infrastructure to make full use of vehicular communication and computing resources. Specifically, VFC is an architecture that has cloud-based features and provides data, computing, storage and application services to end users. VFC is also differentiated from other existing technologies by being close to end users, and having dense geographical distribution and support for mobility; its bottom layer consists of cooperative vehicles, so that the vehicle is both a fog computing provider and a vehicular fog computing user. It can use computing resources nearby and reduce the use of communication resources. In the fog computing architecture, vehicles and mobile devices are end users, and there is a fog server layer between these edge users and the cloud. The low-level equipment of vehicular fog computing vehicles has high-speed moving ch...

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“…The creation of the IoT would have been much more complicated if a big data structure had not been followed since the latter allows analyzing vast amounts of data [35][36][37]. The multifaceted nature of big data is mainly due to the unstructured design of most of the data produced by modern technologies, such as web registers, radio frequency identification (RFID), sensors embedded in devices, machinery, vehicles, Internet searches, social networks, laptops, smartphones, GPS devices, and call center records [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

An overview of big data analysis

Arena

Pau

2020

Bulletin EEI

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Big data represents one of the most profound and most pervasive evolutions in the digital world. Examples of big data come from Internet of Things (IoT) devices, as well as smart cars, but also the use of social networks, industries, and so on. The sources of data are numerous and continuously increasing, and, therefore, what characterizes big data is not only the volume but also the complexity due to the heterogeneity of information that can be obtained. The fastest growth in spending on big data technologies is happening within banking, healthcare, insurance, securities and investment services, and telecommunications. Remarkably, three of those industries lie within the financial sector, which has many particularly serviceable use cases for big data analytics, such as fraud detection, risk management, and customer service optimization. In fact, the definition of big data analysis refers to the process that encompasses the gathering and analysis of big data to obtain useful information for the business. This paper focuses on delivering a short review concerning the current technologies, future perspectives, and the evaluation of some use cased associated with the analysis of big data.

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A Smart Collaborative Authentication Framework for Multi-Dimensional Fine-Grained Control

Cited by 15 publications

References 43 publications

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks

Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing

An overview of big data analysis

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