A first look at vehicle data collection via smartphone sensors

Michael Reininger, Seth Miller, Yanyan Zhuang, Justin Cappos
{"title":"A first look at vehicle data collection via smartphone sensors","authors":"Michael Reininger, Seth Miller, Yanyan Zhuang, Justin Cappos","doi":"10.1109/SAS.2015.7133607","DOIUrl":null,"url":null,"abstract":"Smartphones serve as a technical interface to the outside world. These devices have embedded, on-board sensors (such as accelerometers, WiFi, and GPSes) that can provide valuable information for investigating users' needs and behavioral patterns. Similarly, computers that are embedded in vehicles are capable of collecting valuable sensor data that can be accessed by smartphones through the use of On-Board Diagnostics (OBD) sensors. This paper describes a prototype of a mobile computing platform that provides access to vehicles' sensors by using smartphones and tablets, without compromising these devices' security. Data such as speed, engine RPM, fuel consumption, GPS locations, etc. are collected from moving vehicles by using a WiFi On-Board Diagnostics (OBD) sensor, and then backhauled to a remote server for both real-time and offline analysis. We describe the design and implementation details of our platform, for which we developed a library for in-vehicle sensor access and created a non-relational database for scalable backend data storage. We propose that our data collection and visualization tools are useful for analyzing driving behaviors; we also discuss future applications, security, and privacy concerns specific to vehicular networks.","PeriodicalId":384041,"journal":{"name":"2015 IEEE Sensors Applications Symposium (SAS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Sensors Applications Symposium (SAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAS.2015.7133607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 33

Abstract

Smartphones serve as a technical interface to the outside world. These devices have embedded, on-board sensors (such as accelerometers, WiFi, and GPSes) that can provide valuable information for investigating users' needs and behavioral patterns. Similarly, computers that are embedded in vehicles are capable of collecting valuable sensor data that can be accessed by smartphones through the use of On-Board Diagnostics (OBD) sensors. This paper describes a prototype of a mobile computing platform that provides access to vehicles' sensors by using smartphones and tablets, without compromising these devices' security. Data such as speed, engine RPM, fuel consumption, GPS locations, etc. are collected from moving vehicles by using a WiFi On-Board Diagnostics (OBD) sensor, and then backhauled to a remote server for both real-time and offline analysis. We describe the design and implementation details of our platform, for which we developed a library for in-vehicle sensor access and created a non-relational database for scalable backend data storage. We propose that our data collection and visualization tools are useful for analyzing driving behaviors; we also discuss future applications, security, and privacy concerns specific to vehicular networks.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
第一次看到通过智能手机传感器收集车辆数据
智能手机是与外界联系的技术接口。这些设备具有嵌入式机载传感器(如加速度计、WiFi和gps),可以为调查用户需求和行为模式提供有价值的信息。同样,嵌入车辆中的计算机能够收集有价值的传感器数据,这些数据可以通过使用车载诊断(OBD)传感器由智能手机访问。本文描述了一个移动计算平台的原型,该平台可以通过使用智能手机和平板电脑访问车辆的传感器,而不会损害这些设备的安全性。通过使用WiFi车载诊断(OBD)传感器,从行驶中的车辆收集速度、发动机转速、油耗、GPS位置等数据,然后传回远程服务器进行实时和离线分析。我们描述了我们平台的设计和实现细节,为此我们开发了一个用于车载传感器访问的库,并创建了一个用于可扩展后端数据存储的非关系数据库。我们认为,我们的数据收集和可视化工具对分析驾驶行为很有用;我们还讨论了未来的应用,安全和隐私问题,具体到车载网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Microimmune algorithm for sensor network localization Empirical evaluation of OI-MAC: Direct interconnection between wireless sensor networks for collaborative monitoring DiverNet — A network of inertial sensors for real time diver visualization Sensor fusion for intrusion detection under false alarm constraints Fault tolerant and scalable IoT-based architecture for health monitoring
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1