Semantic framework of internet of things for smart cities: Case studies

Sensors (Switzerland)

Volumn 16, Issue 9, 2016, Pages

  Zhang, Ningyu ^a   Chen, Huajun ^a   Chen, Xi ^a   Chen, Jiaoyan ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Energy management; Internet of things; Smart city; Traffic pattern

Indexed keywords

ARTIFICIAL INTELLIGENCE; ENERGY MANAGEMENT; INTERNET; LEARNING SYSTEMS; PATTERN RECOGNITION; SEMANTIC WEB; SEMANTICS;

INTERNET OF THINGS (IOT); IOT APPLICATIONS; MACHINE LEARNING TECHNOLOGY; POTENTIAL VALUES; SEMANTIC FRAMEWORK; SENSOR TECHNOLOGIES; SMART CITIES; TRAFFIC PATTERN;

INTERNET OF THINGS;

EID: 84987711793     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s16091501     Document Type: Article

References (35)

1. Evans, D. The Internet of Things How the Next Evolution of the Internet is Changing Everything. Available online: https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf (accessed on 9 September 2016).
2. Standards for M2M and the Internet of Things. Available online: http://www.onem2m.org/ (accessed on 9 September 2016).
3. Christian, B.; Tom, H.; Tim, B. Linked Data: The Story So Far. In Semantic Services, Interoperability and Web Applications: Emerging Concepts; IGI Global: Hershey, PA, USA, 2009; pp. 205-227.
4. Vrandečić, D.; Krötzsch, M. Wikidata: A free collaborative knowledgebase. Commun. ACM 2014, 57, 78-85.
5. Lefort, L.; Henson, C.; Taylor, K.; Barnaghi, P.; Compton, M.; Corcho, O.; García C.R.; Graybeal, J.; Herzog, A.; Janowicz, K.; et al. Semantic Sensor Network XG Final Report; W3C Incubator Group Report; 28 June 2011. Available online: https://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/ (accessed on 9 September 2016).
6. Gyrard, A.; Bonnet, C.; Boudaoud, K. Enrich machine-to-machine data with semantic web technologies for cross-domain applications. In Proceedings of the 2014 IEEEWorld Forum on Internet of Things (WF-IoT), Seoul, Korea, 6-8 March 2014; pp. 559-564.
7. Li, Z.; Chu, C.; Yao, W.; Richard A. Ontology-Driven Event Detection and Indexing in Smart Spaces. In Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing (ICSC), Pittsburgh, PA, USA, 22-24 September 2010; pp. 285-292.
8. Willner, A.; Papagianni, C.; Giatili, M. The Open-Multinet Upper Ontology Towards the Semantic-Based Management of Federated Infrastructures. In Proceedings of the TridentCom, Vancouver, BC, Canada, 24-25 June 2015; pp. 1-10.
9. Gyrard, A.; Serrano, M. A Unified Semantic Engine for Internet of Things and Smart Cities: From Sensor Data to End-Users Applications. In Proceedings of the IEEE International Conference on Data Science and Data Intensive Systems, Sydney, Australia, 11-13 December 2015; pp. 718-725.
10. Chen, X.; Chen, H.; Zhang, N.; Huang, J.; Zhang, W. Large-scale real-time semantic processing framework for Internet of Things. Int. J. Distrib. Sens. Netw. 2015, 2015, 2.
11. Yu, Z.; Liciac, C.; Ouric, W.; Hai, Y. Urban Computing: Concepts, Methodologies, and Applications. ACM Trans. Intell. Syst. Technol. 2014, 5, 1-55.
12. Lee, D.; Wang, H.; Cheu, R.; Teo, S. Taxi dispatch system based on current demands and real-time traffic conditions. Transp. Res. Rec. J. Transp. Res. Board 2004, 1882, 193-200.
13. Zheng, Y.; Chen, X.; Jin, Q.; Chen, Y.; Qu, X.; Liu, X.; Chang, E.; Ma, W.; Rui, Y.; Sun, W. A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality; Microsoft Tech Report; 2014. Available online: http://research.microsoft.com/apps/pubs/default.aspx (accessed on 6 September 2016).
14. Shang, J.; Zheng, Y.; Tong, W.; Chang, E.; Yu, Y. Inferring gas consumption and pollution emission of vehicles throughout a city. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 24-27 August 2014; pp. 1027-1036.
15. Yuan, J.; Zheng, Y.; Xie, X. Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, 13-16 August 2012; pp. 186-194.
16. Fu, Y.; Xiong, H. Modeling of Geographic Dependencies for Real Estate Ranking on Site Selection. In Proceedings of the IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, USA, 14-17 November 2015; pp. 1506-1513.
17. Lee, R.; Sumiya, K. Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. In Proceedings of the 2nd ACM SIGSPATIAL InternationalWorkshop on Location Based Social Networks, San Jose, CA, USA, 3-5 November 2010; pp. 1-10.
18. Fogue, M.; Sanguesa, J.; Naranjo, F.; Gallardo, J.; Garrido, P.; Martinez, F. Non-emergency patient transport services planning through genetic algorithms. Expert Syst. Appl. 2016, 61, 262-271.
19. OpenStreetMap. Available online: http://www.openstreetmap.org (accessed on 9 September 2016).
20. Google Map API. Available online: http://developers.google.com/maps (accessed on 9 September 2016).
21. Baidu Map API. Available online: http://developer.baidu.com/map/reference (accessed on 9 September 2016).
22. Yago 2. Available online: http://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago (accessed on 9 September 2016).
23. Geoname. Available online: http://www.geonames.org (accessed on 9 September 2016).
24. WikiData. Available online: https://www.wikidata.org (accessed on 9 September 2016).
25. Forsyth, D.A.; Ponce, J. Computer Vision: A Modern Approach; Prentice Hall Professional Technical Reference: Upper Saddle River, NJ, USA, 2002; pp. 1-828.
26. Yu, Z.; Furui, L.; Hsun-Ping, H. U-Air: When urban air quality inference meets big data. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, 11-14 August 2013; pp. 1436-1444.
27. Pennington, J.; Socher, R.; Manning, C.D. Glove: Global Vectors for Word Representation. In Proceedings of the Conference on Empirical Methods on Natural Language Processing, Doha, Qatar, 25-29 October 2014; pp. 1532-1543.
28. Ngiam, J.; Khosla, A.; Kim, M.; Nam, J.; Lee, H.; Ng, A.Y. Multimodal Deep Learning. In Proceedings of the International Conference on Machine Learning, Bellevue, WA, USA, 28 June-2 July 2011; pp. 689-696.
29. Zhuang, F.; Cheng, X.; Pan, S.J.; Yu, W.; He, Q.; Shi, Z. Transfer Learning with Multiple Sources via Consensus Regularized Autoencoders. In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery; Springer: Berlin, Germany, 2014; pp. 417-431.
30. Schäfer, R.-P.; Thiessenhusen, K.-U.; Wagne, P. A traffic information system by means of real-time floating-car data. ITS World Congr. 2002, 11, 14-22.
31. Yuan, J.; Zheng, Y.; Zhang, C.; Xie, W.; Sun, G.; Huang, Y. T-drive: Driving directions based on taxi trajectories. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA, 2-5 November 2010; pp. 99-108.
32. Ntziachristos, L.; Samara, Z. Computer Programme to Calculate Emissions from Road Transport Methodology and Emission Factors; European Environment Agency-European Topic Center on Air Emission: Copenhagen, Denmark, 2000; pp. 1-86.
33. Gühnemann, A.; Schäfer, R.-P.; Thiessenhusen, K.-U.; Wagner, P. Monitoring Traffic and Emissions by Floating Car Data; Institute of Transport Research: Berlin-Adlershof, Germany, 2004; pp. 1-19.
34. Calafate, C.; Soler, D.; Cano, J.; Manzoni, P. Traffic Management as a Service: The Traffic Flow Pattern Classification Problem. Math. Probl. Eng. 2015, 2015, 1-14.
35. Geoparser. Available online: https://github.com/ropenscilabs/geoparser (accessed on 9 September 2016).