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mobile cloud for Personalized Any-Media Services

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ZTE Communications 2012年第3期10卷 47-54页

作者： Bhumip Khasnabish Standards Development and Industry Relations ZTE USA Inc.MorristownNJ07960USA

In this paper, we define mobile cloud computing and describe how it can be used for delivering advanced any-media services to both nomadic and mobile users. We focus on service delivery that is localized and personalized and suggest that virtualization and tighter cross-layer communication allows for convergence and seamless transition of services. These are also creating new and never-before seen ways of developing and delivering personalized any-media services. We discuss current paradigms for implementing cloud-based any-media services that generate revenue. Future research topics and requirements for evolving network and service elements are also discussed.

关键词： mobile cloud virtualization cross-layer communications any-media service personalized services localized services

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mobile Content Distribution with Vehicular cloud in Urban VANETs

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China Communications 2016年第8期13卷 84-96页

作者： Haigang Gong Lingfei Yu Nianbo Liu Xue Zhang School of Computer Science and Engineering University of Electronic Science and Technology of China Big Data Research Center of UESTC University of Electronic Science and Technology of China Hangzhou College of Commerce Zhejiang Gongshang University

Plenty of multimedia contents such as traffic images, surveillance video, music and movie will flood into vehicular ad hoc networks. However, content distribution over VANETs is not a easy task, due to the high mobility of vehicles and intermittent connectivity. Infrastructure-based scheme can relieve the problem, but with a large amount of investment. In this paper, we propose a mobile content distribution scheme based on roadside parking cloud(RPC), which is formed by the parked car on the roadside, and mobile cloud(MC), which is formed by moving cars on the road. According to a trip history model, a mobile car can estimate its following trajectory. When it wants to download the content, gateway node of the RPC will work out a downloading schedule, which tells it how much chunks it can download from which RPCs. Moreover, the helper of the mobile car in mobile cloud would deliver specified chunks to it when there is lack of RPC in the following trip. Simulation results show that cloud-based scheme performs better than inter-vehicle communication approach and cluster-based scheme.

关键词： roadside parking cloud mobile cloud trip history model trajectory prediction content distribution

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A Specific-Targeting Asymmetric Group Key Agreement for cloud Computing

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Chinese Journal of Electronics 2018年第4期27卷 866-872页

作者： TAN Yu'an ZHENG Jiamin ZHANG Qikun ZHANG Xiaosong LI Yuanzhang ZHANG Quanxin School of Computer Science and Technology Beijing Institute of Technology Institute of Computer and Communication Engineering Zhengzhou University of Light Industry Department of Computer Science and Technology Tangshan University

When the information is exchanged and transmitted among the group members in mobile cloud environment, the group members may distribute different security domains. The information exchanged among group members may have different secret levels in this environment. When a person has some secret information, he only wants to share these information with some people who have the appropriate level of security permissions, other than all the members in the group. Aiming at these needs,we propose a flexible Asymmetric group key agreement(AGKA) protocol that information exchange and transmission are specific-targeting. The paper adopts bilinear mapping and two-way anonymous authentication technology to hide personal identity authentication information, and uses storage and computing migration technology to reduce the resource consumption of mobile terminal, and also proposes the secret key factor oriented extraction and combinations technology to achieve multi-level three-dimensional complex space security information exchange requirements and meet the lightweight computing. The analysis proves that the protocol has good safety performance and low resource consumption.

关键词： Asymmetric group key agreement(AGKA) Specific-targeting information exchange Key self-certified mobile cloud

Predicting Resource Availability in Local mobile Crowd Computing Using Convolutional GRU

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Computers, Materials & Continua 2022年第3期70卷 5199-5212页

作者： Pijush Kanti Dutta Pramanik Nilanjan Sinhababu Anand Nayyar Mehedi Masud Prasenjit Choudhury Department of Computer Science&Engineering National Institute of TechnologyDurgapurIndia Reliability Engineering Centre Indian Institute of Technology KharagpurIndia Graduate School Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing *** the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing *** a localMCC,the SMDs are typically connected to a local Wi-Fi *** and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational *** it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of *** address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is *** this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of *** experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access *** build the prediction model,we presented a novel feature extraction method to be applied to the time-series *** experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.

关键词： Resource selection resource availability mobile grid mobile cloud ad-hoc cloud crowd computing deep learning GRU CNN RNN