Framework for Smart City Applications Based on Participatory Sensing

被引:0
作者
Szabo, R. [1 ,2 ]
Farkas, K. [1 ,2 ]
Ispany, M. [3 ]
Benczur, A. A. [3 ,5 ]
Batfai, N. [3 ]
Jeszenszky, P. [3 ]
Laki, S. [1 ,4 ]
Vagner, A. [3 ]
Kollar, L. [3 ]
Sidlo, Cs. [3 ,5 ]
Besenczi, R. [3 ]
Smajda, M. [3 ]
Koever, G. [3 ]
Szincsak, T. [3 ]
Kadek, T. [3 ]
Kosa, M. [3 ]
Adamko, A. [3 ]
Lendak, I. [1 ]
Wiandt, B. [2 ]
Tomas, T. [1 ,2 ]
Nagy, A. Zs. [1 ,2 ]
Feher, G. [1 ,2 ]
机构
[1] Interuniv Ctr Telecommun & Informat, Debrecen, Hungary
[2] Budapest Univ Technol & Econ, Budapest, Hungary
[3] Univ Debrecen, Debrecen, Hungary
[4] Eotvos Lorand Univ, Budapest, Hungary
[5] Hungarian Acad Sci, MTA SZTAKI, Inst Comp Sci & Control, Budapest, Hungary
来源
2013 IEEE 4TH INTERNATIONAL CONFERENCE ON COGNITIVE INFOCOMMUNICATIONS (COGINFOCOM) | 2013年
关键词
Smart City; Participatory Sensing; XMPP; Public Transport; Soccer; Smart Campus;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Smart cities offer services to their inhabitants which make everyday life easier beyond providing a feedback channel to the city administration. For instance, a live timetable service for public transportation or real-time traffic jam notification can increase the efficiency of travel planning substantially. Traditionally, the implementation of these smart city services require the deployment of some costly sensing and tracking infrastructure. As an alternative, the crowd of inhabitants can be involved in data collection via their mobile devices. This emerging paradigm is called mobile crowd-sensing or participatory sensing. In this paper, we present our generic framework built upon XMPP (Extensible Messaging and Presence Protocol) for mobile participatory sensing based smart city applications. After giving a short description of this framework we show three use-case smart city application scenarios, namely a live transit feed service, a soccer intelligence agency service and a smart campus application, which are currently under development on top of our framework.
引用
收藏
页码:295 / 300
页数:6
相关论文
共 14 条
[1]   Predicting Software Anomalies using Machine Learning Techniques [J].
Alonso, Javier ;
Belanche, Lluis ;
Avresky, Dimiter R. .
2011 10TH IEEE INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS (NCA), 2011,
[2]  
[Anonymous], 2004, Linux J.
[3]  
Batfai N., 2013, CROWD CLOUD IN PRESS
[4]   Mobile Crowdsensing: Current State and Future Challenges [J].
Ganti, Raghu K. ;
Ye, Fan ;
Lei, Hui .
IEEE COMMUNICATIONS MAGAZINE, 2011, 49 (11) :32-39
[5]  
Google Inc, GEN TRANS FEED SPEC
[6]  
Lakshman Avinash., 2009, Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures, SPAA '09, P47
[7]  
Leibiusky Jonathan., 2012, Getting Started with Storm
[8]  
Millard P., 2010, XEP0060 XMPP
[9]  
Neumeyer L., 2010, Proceedings 2010 10th IEEE International Conference on Data Mining Workshops (ICDMW 2010), P170, DOI 10.1109/ICDMW.2010.172
[10]  
Pang LX, 2011, LECT NOTES ARTIF INT, V7121, P237