Mobidata Workshops最新文献

Mining Spatial-Temporal geoMobile Data via Feature Distributional Similarity Graph 基于特征分布相似图的时空地车数据挖掘

Mobidata Workshops Pub Date : 2016-06-30 DOI: 10.1145/2935755.2935760

Arvind Narayanan, Saurabh Verma, Zhi-Li Zhang

引用次数: 2

Oscillation Resolution for Massive Cell Phone Traffic Data 海量手机流量数据的振荡分辨率

Mobidata Workshops Pub Date : 2016-06-30 DOI: 10.1145/2935755.2935759

Ling Qi, Yuanyuan Qiao, F. Abdesslem, Zhanyu Ma, Jie Yang

{"title":"Oscillation Resolution for Massive Cell Phone Traffic Data","authors":"Ling Qi, Yuanyuan Qiao, F. Abdesslem, Zhanyu Ma, Jie Yang","doi":"10.1145/2935755.2935759","DOIUrl":"https://doi.org/10.1145/2935755.2935759","url":null,"abstract":"Cellular towers capture logs of mobile subscribers whenever their devices connect to the network. When the logs show data traffic at a cell tower generated by a device, it reveals that this device is close to the tower. The logs can then be used to trace the locations of mobile subscribers for different applications, such as studying customer behaviour, improving location-based services, or helping urban planning.\u0000 However, the logs often suffer from an oscillation phenomenon. Oscillations may happen when a device, even when not moving, does not only connect to the nearest cell tower, but is instead unpredictably switching between multiple cell towers because of random noise, load balancing, or simply dynamic changes in signal strength. Detecting and removing oscillations are a challenge when analyzing location data collected from the cellular network.\u0000 In this paper, we propose an algorithm called SOL (Stable, Oscillation, Leap periods) aimed at discovering and reducing oscillations in the collected logs. We apply our algorithm on real datasets which contain about 18.9~TB of traffic logs generated by more than 3~million mobile subscribers covering about 21000 cell towers and collected during 27~days from both GSM and UMTS networks in northern China. Experimental results demonstrate the ability and effectiveness of SOL to reduce oscillations in cellular network logs.","PeriodicalId":215467,"journal":{"name":"Mobidata Workshops","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131617046","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: 18

The Need to Account for Geographical Diversities in Mobile Data Research 在移动数据研究中考虑地理多样性的必要性

Mobidata Workshops Pub Date : 2016-06-30 DOI: 10.1145/2935755.2935761

Akhil Mathur, F. Kawsar

引用次数: 0

Indoor Location Error-Detection via Crowdsourced Multi-Dimensional Mobile Data 基于众包多维移动数据的室内定位错误检测

Mobidata Workshops Pub Date : 2016-06-30 DOI: 10.1145/2935755.2935762

Savina Singla, Archan Misra

{"title":"Indoor Location Error-Detection via Crowdsourced Multi-Dimensional Mobile Data","authors":"Savina Singla, Archan Misra","doi":"10.1145/2935755.2935762","DOIUrl":"https://doi.org/10.1145/2935755.2935762","url":null,"abstract":"We explore the use of multi-dimensional mobile sensing data as a means of identifying errors in one or more of those data streams. More specifically, we look at the possibility of identifying indoor locations with likely incorrect/stale Wi-Fi fingerprints, by using concurrent readings from Wi-Fi and barometer sensors from a collection of mobile devices. Our key contribution is a novel two-step process: (i) using longitudinal, crowd-sourced readings of (possibly incorrect) Wi-Fi location estimates to statistically estimate the barometer calibration offset of individual mobile devices, and (ii) then, using such offset-corrected barometer readings from devices (that are supposedly collocated) to identify likely errors in indoor localization. We evaluate this approach using data collected from 104 devices collected on the SMU campus over a period of 61 days: our results show that (i) 49% of the devices had barometer offsets that result in errors in floor-level estimation, and (iii) 46% of the Wi-Fi location estimates were potentially incorrect. By identifying specific locations with unusually high fraction of incorrect location estimates, we attempt to more accurately pinpoint the areas that need re-fingerprinting.","PeriodicalId":215467,"journal":{"name":"Mobidata Workshops","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129007711","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

The Wireless Shark: Identifying WiFi Devices Based on Probe Fingerprints 无线鲨鱼:基于探针指纹识别WiFi设备

Mobidata Workshops Pub Date : 2016-06-30 DOI: 10.1145/2935755.2935757

Otto Waltari, J. Kangasharju

{"title":"The Wireless Shark: Identifying WiFi Devices Based on Probe Fingerprints","authors":"Otto Waltari, J. Kangasharju","doi":"10.1145/2935755.2935757","DOIUrl":"https://doi.org/10.1145/2935755.2935757","url":null,"abstract":"Due to the broadcast nature of WiFi communication anyone with suitable hardware is able to monitor surrounding traffic. However, a WiFi device is able to listen to only one channel at any given time. The simple solution for capturing traffic across multiple channels involves channel hopping, which as a side effect reduces dwell time per channel. Hence monitoring with channel hopping does not produce a comprehensive view of the traffic across all channels at a given time.\u0000 In this paper we present an inexpensive multi-channel WiFi capturing system (dubbed the wireless shark\") and evaluate its performance in terms of traffic cap- turing efficiency. Our results confirm and quantify the intuition that the performance is directly related to the number of WiFi adapters being used for listening. As a second contribution of the paper we use the wireless shark to observe the behavior of 14 different mobile devices, both in controlled and normal office environments. In our measurements, we focus on the probe traffic that the devices send when they attempt to discover available WiFi networks. Our results expose some distinct characteristics in various mobile devices' probing behavior.","PeriodicalId":215467,"journal":{"name":"Mobidata Workshops","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134068182","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: 10

Routing Over Multi-Hop Wireless Network of Mobile Computers 移动计算机的多跳无线网络路由

Mobidata Workshops Pub Date : 2001-03-01 DOI: 10.1007/978-0-585-29603-6_6

C. Perkins, P. Bhagwat

引用次数: 149

Experience with Disconnected Operation in a Mobile Computing Environment 有在移动计算环境下进行非连接操作的经验

Mobidata Workshops Pub Date : 1993-08-02 DOI: 10.1007/978-0-585-29603-6_20

M. Satyanarayanan, James J. Kistler, L. Mummert, M. Ebling, Puneet Kumar, Qi Lu

引用次数: 155

The Parctab Ubiquitous Computing Experiment 普适计算实验

Mobidata Workshops Pub Date : 1900-01-01 DOI: 10.1007/978-0-585-29603-6_2

R. Want, Bill N. Schilit, N. Adams, R. Gold, K. Petersen, David Goldberg, J. R. Ellis, M. Weiser