2018 19th IEEE International Conference on Mobile Data Management (MDM)最新文献_第4页

Sensor Cloud: Sensing-as-a-Service Paradigm 传感器云:传感即服务范式

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00014

S. Madria

引用次数: 6

FMS: Managing Crowdsourced Indoor Signals with the Fingerprint Management Studio FMS:使用指纹管理工作室管理众包室内信号

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00054

Marileni Angelidou, Constantinos Costa, Artyom Nikitin, D. Zeinalipour-Yazti

引用次数: 3

Improved Localisation Using Spatio-Temporal Data from Cellular Network 基于蜂窝网络时空数据的改进定位

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00022

S. Luo, Y. Ng, Terence Zheng Wei Lim, Cliff Choon Hua Tan, Nannan He, Giuseppe Manai, Y. Li

{"title":"Improved Localisation Using Spatio-Temporal Data from Cellular Network","authors":"S. Luo, Y. Ng, Terence Zheng Wei Lim, Cliff Choon Hua Tan, Nannan He, Giuseppe Manai, Y. Li","doi":"10.1109/MDM.2018.00022","DOIUrl":"https://doi.org/10.1109/MDM.2018.00022","url":null,"abstract":"Localisation of mobile devices has been a topic of academic research and industry practice for solving various application problems, examples can be footfall counting and profiling used for location based digital or physical advertising, crowd monitoring for public security, emergency handling, transport measurement and management, etc. Often the solutions for localisation require large scale networks hardware and/or software upgrades, which can be very costly. However, we note the fact that many commercial use cases actually do not require very high resolution of localisation and satisfactory level of accuracy may be sufficient for attaining business decision quality. A reasonable trade-off between achieving business value and minimising additional costs on network equipment purchase and maintenance is to build solutions that rely only on telco-network data and utilize data mining methods to improve the localisation accuracy of mobile devices that carried by subscribers. In this work, we aim to achieve acceptable accuracy for localisation at the resolution of region of interest (ROI), the exact shape of which is defined according to business requirements. One example is the geographical division of planning sub-zone in Singapore. We make use of the Global Positioning System (GPS) locations extracted from mobile broadband log that contain the longitude and latitude of the subscriber to annotate the telco-network data. We experimented with three learning models: maximum likelihood estimation, dominant serving ROI, and random forest, along with the baseline of localisation based on cellular tower locations. The experiment results demonstrate the effectiveness of the proposed models and demonstrate accuracy improvement from baseline of 37.8% (naive cellular tower localisation) to 78.4% (random forest classification).","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"a3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130723096","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

Next Check-in Location Prediction via Footprints and Friendship on Location-Based Social Networks 在基于位置的社交网络上通过足迹和友谊进行位置预测

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00044

Yijun Su, Xiang Li, Wei Tang, Ji Xiang, Yuanye He

引用次数: 13

Load-Balanced Task Allocation for Improved System Lifetime in Mobile Crowdsensing 基于负载均衡任务分配的移动群体感知系统寿命改进

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00040

Garvita Bajaj, Pushpendra Singh

引用次数: 5

A Cost-Aware Incentive Mechanism in Mobile Crowdsourcing Systems 移动众包系统中的成本意识激励机制

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00042

Ellen Mitsopoulou, Ioannis Boutsis, V. Kalogeraki, Jia Yuan Yu

{"title":"A Cost-Aware Incentive Mechanism in Mobile Crowdsourcing Systems","authors":"Ellen Mitsopoulou, Ioannis Boutsis, V. Kalogeraki, Jia Yuan Yu","doi":"10.1109/MDM.2018.00042","DOIUrl":"https://doi.org/10.1109/MDM.2018.00042","url":null,"abstract":"The rapid growth of ubiquitous mobile smart devices has led to the creation of a new era of mobile crowdsourcing applications, where human workers participate and perform tasks in exchange of a monetary reward. Such crowdsourcing systems can play a vital role during emergency events, where fast and accurate responses are needed. However, a commonly ignored aspect is how the price (i.e. the reward paid to workers) must be set in order for the system to meet two important requirements: (i) to timely receive an adequate number of responses which is crucial during emergencies, and (ii) to meet budget constraints. In the majority of the existing systems, the price per task is set up-front and remains unchanged for all upcoming tasks, leading to either higher monetary cost than necessary or to significantly larger latency than expected. In this work, we provide a formulation based on Kalman Filters that enables the system to estimate the user/worker behavior, i.e., the likelihood over time for a user to provide answers for a specific reward. Specifically, we focus on the problem of developing an adaptive pricing policy to incentivize the users to rapidly provide their responses. Our mechanism can be adjusted dynamically to bridge the gap among the users' behavior and the system's needs so as to maximize the overall utility of the system. We simulate our model and through extensive experimental evaluation we show how our system performs and provides benefits to both the users and the system operator.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124442142","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

Telco Big Data: Current State & Future Directions 电信大数据:现状与未来方向

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00016

Constantinos Costa, D. Zeinalipour-Yazti

{"title":"Telco Big Data: Current State & Future Directions","authors":"Constantinos Costa, D. Zeinalipour-Yazti","doi":"10.1109/MDM.2018.00016","DOIUrl":"https://doi.org/10.1109/MDM.2018.00016","url":null,"abstract":"A Telecommunication company (Telco) is traditionally only perceived as the entity that provides telecommunication services, such as telephony and data communication access to users. However, the radio and backbone infrastructure of such entities spanning densely most urban spaces and widely most rural areas, provides nowadays a unique opportunity to collect immense amounts of data that capture a variety of natural phenomena on an ongoing basis, e.g., traffic, commerce and mobility patterns and user service experience. The ability to perform analytics on the generated big data within tolerable elapsed time and share it with key smart city enablers (e.g., municipalities, public services, startups, authorities, and companies), elevates the role of Telcos in the realm of future smart cities from pure network access providers to information providers. In this talk, we overview the state-of-the-art in Telco big data analytics by focusing on a set of basic principles, namely: (i) real-time analytics and detection; (ii) experience, behavior and retention analytics; (iii) privacy; and (iv) storage. We also present experiences from developing an innovative such architecture and conclude with open problems and future directions.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126570147","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: 8

Hierarchical Regions of Interest 感兴趣的等级区域

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00025

P. Järv, T. Tammet, Marten Tall

引用次数: 5

Accurate Fuel Estimates Using CAN Bus Data and 3D Maps 准确的燃料估计使用CAN总线数据和3D地图

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00045

O. Andersen, K. Torp

{"title":"Accurate Fuel Estimates Using CAN Bus Data and 3D Maps","authors":"O. Andersen, K. Torp","doi":"10.1109/MDM.2018.00045","DOIUrl":"https://doi.org/10.1109/MDM.2018.00045","url":null,"abstract":"The focus on reducing CO2 emissions from the transport sector is larger than ever. Increasingly stricter reductions on fuel consumption and emissions are being introduced by the EU, e.g., to reduce the air pollution in many larger cities. Large sets of high-frequent GPS data from vehicles already exist. However, fuel consumption data is still rarely collected even though it is possible to measure the fuel consumption with high accuracy, e.g., using an OBD-II device and a smartphone. This paper, presents a method for comparing fuel-consumption estimates using the SIDRA TRIP model with real fuel measures to determine if the fuel-consumption model is sufficiently accurate. The model is implemented using a 2D, a simple 3D, and a high-precision (H3D) road map of Denmark. The original 2D map is lifted to a 3D map using a Digital Elevation Model (DEM). Results show that introducing a 3D map improves the accuracy of fuel-consumption estimates with up to 40% on hilly roads. There is only very little improvement of the high-precision (H3D) map over the simple 3D map. The fuel consumption estimates are most accurate on flat terrain with average fuel estimates of up to 99% accuracy. The fuel estimates are most inaccurate uphill/downhill and when the vehicles accelerate at speeds above 50 km/h.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"16 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133473753","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

Stochastic Shortest Path Finding in Path-Centric Uncertain Road Networks 以路径为中心的不确定道路网络的随机最短路径查找

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00020

Georgi Andonov, B. Yang

引用次数: 9