2014 IEEE 15th International Conference on Mobile Data Management最新文献_第6页

MadApp: A Middleware for Opportunistic Data in Mobile Web Applications MadApp:移动Web应用程序中机会数据的中间件

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.27

V. Srinivasan, C. Julien

{"title":"MadApp: A Middleware for Opportunistic Data in Mobile Web Applications","authors":"V. Srinivasan, C. Julien","doi":"10.1109/MDM.2014.27","DOIUrl":"https://doi.org/10.1109/MDM.2014.27","url":null,"abstract":"Mobile computing increasingly often entails applications that embody opportunistic or delay-tolerant communication, and while much work has focused on refining and optimizing the technical underpinnings for providing delay-tolerant communication constructs, there is almost a complete lack of support for integrating opportunistic communication functionality at the application level. This paper introduces MadApp, an application-level development framework that provides tailored abstractions and support infrastructure for creating dynamic web pages that can incorporate received content from various opportunistic communication channels on-the-fly. We describe multiple application scenarios in which these constructs can seamlessly apply, and provide a complete conceptual and concrete architecture and implementation for MadApp. We evaluate MadApp's support for opportunistic mobile computing web applications using two different mobility trace data sets collected from the real-world. This paper demonstrates that MadApp enables opportunistic mobile computing applications to begin to leverage the significant advances in delay-tolerant communication research, opening doors for even more dynamic and adaptive applications.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117225906","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: 4

SimMiner: A Tool for Discovering User Similarity by Mining Geospatial Trajectories SimMiner:一个通过挖掘地理空间轨迹来发现用户相似性的工具

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.53

Naveen Nandan, David Sinjaya

引用次数: 4

Design and Implementation of the Privacy Management Platform 隐私管理平台的设计与实现

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.14

Christoph Stach, B. Mitschang

引用次数: 19

Understanding Sustainable Mobility: The Potential of Electric Vehicles 理解可持续交通:电动汽车的潜力

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.63

Michelle Scott, D. Hopkins, J. Stephenson

{"title":"Understanding Sustainable Mobility: The Potential of Electric Vehicles","authors":"Michelle Scott, D. Hopkins, J. Stephenson","doi":"10.1109/MDM.2014.63","DOIUrl":"https://doi.org/10.1109/MDM.2014.63","url":null,"abstract":"Rising awareness of the environmental impacts of dominant mobility practices lead to the development of the sustainable mobility paradigm. This paradigm advocates three features of a mobility system: 1. A reduced need to travel, 2. Modal shift towards more sustainable options, and 3. Reduced vehicle kilometres travelled. In this paper, two sets of data are presented to explore the potential of electric vehicles to contribute to a more sustainable mobility system. First, data from an international Delphi of transport experts shows how a sustainable future can be characterised by different features: efficient internal combustion engine vehicles, electric vehicles, and reduced personal car ownership. Thus electric vehicles are presented as both an opportunity and a threat in relation to sustainable mobility. A second body of empirical material is drawn from interviews with electric vehicle owners, and discusses the drivers and barriers to ownership. Interestingly, participants suggest changing mobility practices associated with electric vehicle ownership, evidenced by decreasing kilometres travelled. The paper concludes by suggesting that there may be potential for electric vehicles to contribute to a sustainable mobility future through modified mobility practices and renewable energy sources in New Zealand.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128178968","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: 6

Effective Mobile Context Pattern Discovery via Adapted Hierarchical Dirichlet Processes 基于自适应层次Dirichlet过程的有效移动上下文模式发现

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.24

Jiangchuan Zheng, Siyuan Liu, L. Ni

{"title":"Effective Mobile Context Pattern Discovery via Adapted Hierarchical Dirichlet Processes","authors":"Jiangchuan Zheng, Siyuan Liu, L. Ni","doi":"10.1109/MDM.2014.24","DOIUrl":"https://doi.org/10.1109/MDM.2014.24","url":null,"abstract":"The extraction of macroscopic mobile context reflecting users' personal and social behavior patterns from smartphone sensor data (e.g., GPS/Bluetooth signals) is crucial in building intelligent pervasive systems. Hierarchical Dirichlet Processes (HDP), a well known Bayesian nonparametrics model for grouped data, is a promising option to achieve this objective due to its ability of discovering high-level semantics behind raw signals and establishing connections between individuals. However, applying HDP in a straightforward manner may not work as it does not take certain unique characteristics in mobile context into account. Particularly, while traditional HDP typically models a single aspect (e.g., Word), the characterization of a mobile context normally involves multiple heterogeneous aspects (e.g., Time, location, Bluetooth proximity). In addition, the presence of multiple aspects dictates a flexible way of clustering users and organizing mobile contexts in a hierarchical manner in serving different pervasive applications, a feature that traditional HDP lacks. Therefore, in this paper, we propose several extensions on traditional HDP to adapt it to the task of mobile context discovery. The key features in our extensions are: i) fusing multiple aspects naturally in HDP to achieve effective extraction of complex mobile context, ii) treating different aspects heterogeneously (globally or personally) in HDP to enable flexible user behavior clustering at various granularities in accordance with applications' needs, and iii) organizing mobile contexts in a hierarchical manner for natural behavior representation and overcoming data sparsity. Based on the experiments in a popular real-world mobile data set, we illustrate the ability of the framework in extracting useful mobile contexts such as characterizing personal life routines, discovering dominant temporal habits in a population, and inferring social group patterns, as well as its potential in improving individual mobility prediction under data sparsity.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"6 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131451261","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: 12

PopTour: Discovering Journey Group T-Patterns from Instagram Trajectories to Recommend Hot Travel Routes PopTour:从Instagram轨迹中发现旅行组t型模式，推荐热门旅游路线

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.56

Shuangyu Yu, Yaxin Yu, Yulong Li, Xin Liu

{"title":"PopTour: Discovering Journey Group T-Patterns from Instagram Trajectories to Recommend Hot Travel Routes","authors":"Shuangyu Yu, Yaxin Yu, Yulong Li, Xin Liu","doi":"10.1109/MDM.2014.56","DOIUrl":"https://doi.org/10.1109/MDM.2014.56","url":null,"abstract":"Instagram is an popular photo-sharing smart phone application of social network and is widely used among tourists to record their journey information such as location, time and content. Consequently, huge volume of spatio-temporal data are generated in the form of trajectories. Discovering useful patterns from these trajectories can reveal valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called Journey Group (JG), which is a group trajectory pattern reflecting a large number of users who walk through a common trajectory and depart from the trajectory for several times allowed. In this paper, we focus on data generated by Instagram to discover the JG Trajectory Patterns i.e., JG T-Patterns, from travel trajectories. Previous researches on T-Patterns mining concentrate on GPS-based data, which is different from Instagram data, a kind of UGC-based (User Generated Content based) data. GPS-based data is dense because it is often generated automatically by moving devices in a certain pace, while UGC-based data is sparse because data is generated randomly by the uploading of users. Aiming at this, a novel JG T-Patterns mining strategy from UGC-based data is also proposed. Finally, a demo for discovering hot travel routes based on JG T-Patterns from Instagram trajectories, named Pop Tour, is implemented and experimental results show that Pop Tour is an effective system.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132961939","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: 3

Keyword Based Semantic Search for Mobile Data 基于关键字的移动数据语义搜索

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.36

Jihoon Ko, Sangjin Shin, Sungkwang Eom, Minjae Song, Jooik Jung, Donghoon Shin, Kyong-Ho Lee, Yongil Jang

引用次数: 4

Efficient and Secure Code Dissemination in Sensor Clouds 传感器云中高效安全的代码传播

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.19

Vimal Kumar, S. Madria

{"title":"Efficient and Secure Code Dissemination in Sensor Clouds","authors":"Vimal Kumar, S. Madria","doi":"10.1109/MDM.2014.19","DOIUrl":"https://doi.org/10.1109/MDM.2014.19","url":null,"abstract":"In this paper, we present an efficient and secure code dissemination technique aimed at sensor clouds. Previous code dissemination techniques were geared toward traditional wireless sensor networks. They did not take into account, the dynamic nature of a sensor cloud, where the applications running on the motes may not just be updated but changed completely in successive code disseminations. The technique presented in this paper is based upon the observation that a large amount of code is common between applications in wireless sensor networks. Our technique first discovers the code common across various wireless sensor applications. It then distributes this code in the form of functions a priori into the network. During code dissemination, these common functions are picked up by the sensors from the network. Only a part of the code needs to be transmitted from the base station. This reduces the overall transmitted code and hence the energy consumption. Since, security is important in sensor clouds, we further present a security scheme based on proxy reencryption to provide confidentiality and integrity of the code. We have implemented our scheme using two different proxy reencryption algorithms, on Mica2 and TelosB mote platforms to measure its energy consumption. We have also evaluated our scheme in terms of disseminated code size and bandwidth usage to illustrate its efficiency compared to a popular secure code dissemination technique, Seluge.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132221587","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

edPAS: Event-Based Dynamic Parking Allocation System in Vehicular Networks 基于事件的车辆网络动态泊车分配系统

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.72

Kshama Raichura, Nilesh Padhariya

引用次数: 8

Pattern-Wise Trust Assessment of Sensor Data 基于模式的传感器数据信任评估

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.22

Robert Gwadera, Mehdi Riahi, K. Aberer

{"title":"Pattern-Wise Trust Assessment of Sensor Data","authors":"Robert Gwadera, Mehdi Riahi, K. Aberer","doi":"10.1109/MDM.2014.22","DOIUrl":"https://doi.org/10.1109/MDM.2014.22","url":null,"abstract":"One of the most important tasks of a sensor network (SN) is to detect occurrences of interesting events in the monitored environment. However, data measured by SN is often affected by errors. We investigate the problem of assessing trustworthiness (trust) of a sensor value (tested value) in the presence of events and errors. A usual approach is to express the trust as a deviation of the tested value from a reference value (a normal value). State of the art approaches aim at defining the reference value in terms of a context consisting of values of spatially proximate sensors that are correlated with the tested value. However, they trade accuracy for simplicity and use a fixed context consisting of values of a fixed neighborhood (e.g., All values within a circular neighborhood of radius r). Therefore, such a fixed context fails in most practical cases by under or overestimating the reference values. We present the first pattern-wise method (PW) for trust assessment of sensor data that addresses the limitations of the state of the art approaches by departing from the idea of the fixed neighborhood. We consider a variable neighborhood that consists of an arbitrary subset of the spatially proximate sensors. We define the context as a frequent spatial pattern consisting of values of the variable neighborhood that frequently co-occurs with the tested value in the stream of sensor values. We define the trust as a belief (probability) that the tested value is correct given selected features of a frequent pattern consisting of the context and the tested value. We compute trust as the output of the logistic regression, where the input variables consist of the following features of the pattern: (I) the relative frequency, (II) the conditional probability of the tested value given the context and (III) the size of the variable neighborhood. Experimental results confirmed superiority of the proposed method over the state of the art method.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133497029","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