/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

/pdf/deep-learning-in-mobile-and-wireless-networking-a-survey-ct8cy2kuqi.pdf

Deep Learning in Mobile and Wireless Networking: A Survey

YouTube has become the most successful Internet website providing a new generation of short video sharing service since its establishment in early 2005. YouTube has a great impact on Internet traffic nowadays, yet itself is suffering from a severe problem of scalability. Therefore, understanding the characteristics of YouTube and similar sites is essential to network traffic engineering and to their sustainable development. To this end, we have crawled the YouTube site for four months, collecting more than 3 million YouTube videos' data. In this paper, we present a systematic and in-depth measurement study on the statistics of YouTube videos. We have found that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length and access pattern, to their growth trend and active life span. We investigate the social networking in YouTube videos, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices have clear small-world characteristics. This indicates that the videos have strong correlations with each other, and creates opportunities for developing novel techniques to enhance the service quality.

/pdf/statistics-and-social-network-of-youtube-videos-4g8j2q74fy.pdf

Statistics and Social Network of YouTube Videos

The Health Insurance Portability and Accountability Act, also known as HIPAA, was first delivered to congress in 1996 and consisted of just two Titles. It was designed to protect health insurance coverage for workers and their families while between jobs. It establishes standards for electronic health care transactions and addresses the issues of privacy and security when dealing with Protected Health Information (PHI). HIPAA is applicable only in the United States of America.

The Health Insurance Portability and Accountability Act.

Cloud-based vehicular networks are a promising paradigm to improve vehicular services through distributing computation tasks between remote clouds and local vehicular terminals. To further reduce the latency and the transmission cost of the computation off-loading, we propose a cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks. In this framework, we study the effectiveness of the computation transfer strategies with vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication modes. Considering the time consumption of the computation task execution and the mobility of the vehicles, we present an efficient predictive combination-mode relegation scheme, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions. Illustrative results indicate that our proposed scheme greatly reduces the cost of computation and improves task transmission efficiency.

Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading

Mobile cloud computing is an emerging technology for facilitating complex application execution on smartphones. Cloud services are utilized not only to speed up the running of mobile applications but to save energy for smartphones as well. In this paper, we propose to combine the vehicular cloud with the infrastructure-based cloud to expand the current available resources for task requests from smartphones. In our proposed architecture, the vehicular cloud acts as a cloud service provider for smartphones. Moreover, we propose a flexible offloading strategy (FOS) to carry out task migration. The vehicular cloud is able to discover and utilize the underutilized resources in vehicles to accomplish application offloading for smartphones. The FOS estimates the efficiency of various cloud service providers based on current resource conditions and then selects the suitable cloud service provider to perform the requested task. Experimental results show that the proposed approach can improve the performance of mobile applications on smartphones in terms of task response time and energy consumption.

Toward Vehicle-Assisted Cloud Computing for Smartphones

Electronic medical record (EMR) is a crucial form of healthcare data, currently drawing a lot of attention. Sharing health data is considered to be a critical approach to improve the quality of healthcare service and reduce medical costs. However, EMRs are fragmented across decentralized hospitals, which hinders data sharing and puts patients' privacy at risks. To address these issues, we propose a blockchain based privacy-preserving data sharing for EMRs, called BPDS. In BPDS, the original EMRs are stored securely in the cloud and the indexes are reserved in a tamper-proof consortium blockchain. By this means, the risk of the medical data leakage could be greatly reduced, and at the same time, the indexes in blockchain ensure that the EMRs can not be modified arbitrarily. Secure data sharing can be accomplished automatically according to the predefined access permissions of patients through the smart contracts of blockchain. Besides, the joint-design of the CP-ABE-based access control mechanism and the content extraction signature scheme provides strong privacy preservation in data sharing. Security analysis shows that BPDS is a secure and effective way to realize data sharing for EMRs.

BPDS: A Blockchain Based Privacy-Preserving Data Sharing for Electronic Medical Records

Through time, we have seen mobile phones transform into multifaceted devices, adapted to meet and exceed our everyday needs. These needs range from something as personal as a health care manager to something as purely analytical as an environment monitor. In effect, mobile phones have come into our lives, making life easier, smarter, and more efficient. In this article we discuss mobile sensing and cloud computing separately and in detail, then combine the two concepts to form the singular idea of mobile cloud sensing. We will also give an intuitive architectural description of mobile cloud sensing, along with discussions about each of its individual building blocks. There are limitations to mobile cloud sensing today, but with the emergence of 5G coupled with the analysis of big data, we can address the current issues at hand. We believe that with the advent of mobile cloud sensing, 5G, and big data analysis, our lives will continue to see an increase in overall quality.

Hongli Zhang

Papers

Toward Vehicle-Assisted Cloud Computing for Smartphones

BPDS: A Blockchain Based Privacy-Preserving Data Sharing for Electronic Medical Records

Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world

Feature selection for optimizing traffic classification

Intrusion detection alarms reduction using root cause analysis and clustering