Proceedings of the 2015 Workshop on Mobile Big Data最新文献

Controlling the Spreads of Infectious Disease and Scare via Utilizing Location and Social Networking Information 利用地理位置和社交网络信息控制传染病和恐慌的传播

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757386

Wei Cheng, F. Chen, Xiuzhen Cheng

引用次数: 1

A Mobile Cloud Computing Middleware for Low Latency Offloading of Big Data 一种面向大数据低时延卸载的移动云计算中间件

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757390

Bo Yin, Wenlong Shen, L. Cai, Y. Cheng

{"title":"A Mobile Cloud Computing Middleware for Low Latency Offloading of Big Data","authors":"Bo Yin, Wenlong Shen, L. Cai, Y. Cheng","doi":"10.1145/2757384.2757390","DOIUrl":"https://doi.org/10.1145/2757384.2757390","url":null,"abstract":"Recent years have witnessed an explosive growth of mobile applications. Thanks to improved network connectivity, it becomes a promising enabling solution to offload computation-intensive applications to the resource abundant public cloud to further augment the capacity of resource-constrained devices. As mobile applications usually have QoS requirements, it is critical to provide low latency services to the mobile users while maintain low leasing cost of cloud resources. However, the resources offered by cloud vendors are usually charged based on a time quanta while the offloading demand for heavy-lifting computation may occur infrequently on mobile devices. This mismatch would demotivate users to resort to public cloud for computation offloading. In this paper, we design a computation offloading middleware which bridges the aforementioned gap between cloud vendors and mobile clients, providing offloading service to multiple users with low cost and delay. The proposed middleware has two key components: Task Scheduler and Instance Manager. The Task Scheduler dispatches the received offloading tasks to execute in the instances reserved by the Instance Manager. Based on the arrival pattern of offloading tasks, the Instance Manager dynamically changes the number of instances to ensure certain service grade of mobile users. Our proposed mechanisms are validated through numerical results. It is shown that a lower average delay can be achieved through proposed scheduling heuristic, and the number of reserved instances well adapts to the offloading demands.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124104971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

A Survey of Fog Computing: Concepts, Applications and Issues 雾计算综述:概念、应用和问题

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757397

Shanhe Yi, Cheng Li, Qun A. Li

引用次数: 1222

Session details: Social Networks, Sensor Networks, and Smartphone Systems 会议细节:社交网络，传感器网络和智能手机系统

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/3260492

John Wang

引用次数: 0

Research on 3D Localization Algorithm of Wireless Sensor Networks in Underground Coal Mine 煤矿井下无线传感器网络三维定位算法研究

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757393

Feng Wang, Cong Wang, Z. Wang, Xueying Zhang, Chao Shang

引用次数: 2

A Measurement-based Study on Application Popularity in Android and iOS App Stores 基于测量的Android和iOS应用商店应用受欢迎程度研究

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757392

Wei Liu, Ge Zhang, Jun Chen, Y. Zou, Wenchao Ding

{"title":"A Measurement-based Study on Application Popularity in Android and iOS App Stores","authors":"Wei Liu, Ge Zhang, Jun Chen, Y. Zou, Wenchao Ding","doi":"10.1145/2757384.2757392","DOIUrl":"https://doi.org/10.1145/2757384.2757392","url":null,"abstract":"Mobile application stores (appstores) are emerging digital distribution platforms with explosive growth. Although there have been some observations on the mobile application (app) popularity in Android appstores, there is no report on the app popularity in iOS appstores. What's more, the details about user downloads and app popularity, such as the composition of downloads traffic and the migration of user interests, are untouched yet. In this paper, we unreel these issues based on five-month measurements of four third-party appstores (two for Android and two for iOS respectively). Our main results include: 1) The app popularity distributions of third-party Android appstores are different from those of iOS third-party appstores. There is an exponential cut-off observed besides the Zipf-like distribution in the app popularity distribution of Android appstores. 2) In both Android and iOS families of appstores, the major part of downloads traffic is contributed by the large-size apps, counting 80% or more in the volume of total downloads traffic. 3) There is less rank variance of the most popular apps in the iOS appstores than those in the Android appstores. About 52% of the top 100 iOS apps observed in one month are still in the rank of top 100 in the following four months.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130325721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Session details: Other Applications 会话详细信息:其他应用程序

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/3260495

Yu Cheng

引用次数: 0

Application of multiple orthogonal window spectrum estimation in speaker recognition 多重正交窗谱估计在说话人识别中的应用

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757394

Bai Jing, Zhang Yiran, Yin Cong

引用次数: 0

SH-CRAN: Hierarchical Framework to Support Mobile Big Data Computing in a Secure Manner SH-CRAN:安全支持移动大数据计算的分层框架

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/2757384.2757388

Qiang Liu, E. Ngai, Xiping Hu, Zhengguo Sheng, Victor C. M. Leung, Jianping Yin

{"title":"SH-CRAN: Hierarchical Framework to Support Mobile Big Data Computing in a Secure Manner","authors":"Qiang Liu, E. Ngai, Xiping Hu, Zhengguo Sheng, Victor C. M. Leung, Jianping Yin","doi":"10.1145/2757384.2757388","DOIUrl":"https://doi.org/10.1145/2757384.2757388","url":null,"abstract":"The heterogeneous cloud radio access network (H-CRAN) has been emerging as a cost-effective solution supporting huge volumes of mobile traffic in the big data era. This paper investigates potential security challenges on H-CRAN and analyzes their likelihoods and difficulty levels. Typically, the security threats in H-CRAN can be categorized into three groups, i.e., security threats towards remote radio heads (RRHs), those towards the radio cloud infrastructure and towards backhaul networks. To overcome challenges made by the security threats, we propose a hierarchical security framework called Secure H-CRAN (SH-CRAN) to protect the H-CRAN system against the potential threats. Specifically, the architecture of SH-CRAN contains three logically independent secure domains (SDs), which are the SDs of radio cloud infrastructure, RRHs and backhauls. The notable merits of SH-CRAN include two aspects: (i) the proposed framework is able to provide security assurance for the evolving H-CRAN system, and (ii) the impacts of any failure are limited in one specific component of H-CRAN. The proposed SH-CRAN can be regarded as the basis of the future security mechanisms of mobile bag data computing.","PeriodicalId":330286,"journal":{"name":"Proceedings of the 2015 Workshop on Mobile Big Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131006822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Session details: Cloud and Fog Computing 会话详细信息:云和雾计算

Proceedings of the 2015 Workshop on Mobile Big Data Pub Date : 2015-06-21 DOI: 10.1145/3260494

Wei Cheng

引用次数: 0