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A Comprehensive Approach to Participatory Sensing of Weather Information via Mobile Devices

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Amr Elsaadany
10.5120/ijca2017915557

Amr Elsaadany. A Comprehensive Approach to Participatory Sensing of Weather Information via Mobile Devices. International Journal of Computer Applications 175(5):55-60, October 2017. BibTeX

@article{10.5120/ijca2017915557,
	author = {Amr Elsaadany},
	title = {A Comprehensive Approach to Participatory Sensing of Weather Information via Mobile Devices},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2017},
	volume = {175},
	number = {5},
	month = {Oct},
	year = {2017},
	issn = {0975-8887},
	pages = {55-60},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume175/number5/28488-2017915557},
	doi = {10.5120/ijca2017915557},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Data sensing techniques are becoming widely used in various applications including forecasting systems. Accurate forecasting systems must rely on multiple input data sources. In this paper, the techniques used in developing accurate weather reporting systems are reviewed and the strength of multiple data sensing techniques is utilized to conceptualize a new system architecture that aims at accurate weather forecasting. The new model is based on four main components; environmental sensing component, user submitted reports, social networks forecast, and external sensors components. The resulting system produces more accurate reports than systems that do not rely on multiple input sources.

References

  1. Wankhede, P., Sharma, R., Pote, C. 2014. A Review on Weather Forecasting Systems Using Different Techniques and Web Alerts. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 2.
  2. developer.apple.com, Apple Researchkit. [Online]. Available: http://developer.apple.com/ [Accessed: 15- Jul- 2016].
  3. Niforatos, E. et al. 2014. Atmos: A Hybrid Crowdsourcing Approach to Weather Estimation. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct Publication - UbiComp.
  4. LaLone, N. et al. 2015. Harnessing Twitter and Crowdsourcing to Augment Aurora Forecasting. Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing - CSCW'15 Companion.
  5. Butgereit, L. 2014. Crowdsourced weather reports: An implementation of the μ model for spotting weather information in Twitter. IST-Africa Conference Proceedings.
  6. Overeem, A. et al. 2013. Crowdsourcing Urban Air Temperatures from Smartphone Battery Temperatures. Geophysical Research Letters 40.15: 4081-4085.
  7. Hasenfratz, D., et al. 2012. Participatory Air Pollution Monitoring Using Smartphones. 2nd International Workshop on Mobile Sensing.
  8. Sivaraman, V., et al. 2013. Hazewatch: A Participatory Sensor System for Monitoring Air Pollution in Sydney. 38th Annual IEEE Conference on Local Computer Networks – Workshops.
  9. Dalğın, S. and A. Doğru, O. 2015. Investigation of the Usability of Mobile Sensors for Weather Forecasting. International Journal of Environment and Geoinformatics.
  10. Yan, T., Kumar, V. and Ganesan, D. 2010. CrowdSearch- Exploiting Crowds for Accurate Real-time Image Search on Mobile Phones. Proceedings of the 8th international conference on Mobile systems, applications, and services - MobiSys.
  11. Xiao, Y. et al. 2013. Lowering the Barriers to Large-Scale Mobile Crowdsensing. Proceedings of the 14th Workshop on Mobile Computing Systems and Applications - HotMobile.
  12. Zhang, D. et al. 2014. Crowdrecruiter: Selecting Participants for Piggyback Crowdsensing under Probabilistic Coverage Constraint. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp.
  13. Ra, M. et al. 2012. Medusa: A Programming Framework for Crowd-Sensing Applications. Proceedings of the 10th international conference on Mobile systems, applications, and services - MobiSys.
  14. Mohan, P., Padmanabhan, V., and Ramjee, R. 2008. Nericell: Rich Monitoring of Road and Traffic Conditions using Mobile Smartphones. Proceedings of the 6th ACM conference on Embedded network sensor systems - SenSys.
  15. Zhou, P., Zheng, Y., and Li, M. 2012. How Long To Wait? Predicting Bus Arrival Time with Mobile Phone based Participatory Sensing. Proceedings of the 10th international conference on Mobile systems, applications, and services - MobiSys.
  16. Rana, R. et al. 2010. Ear-Phone: An End-to-End Participatory Urban Noise Mapping System. Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks - IPSN.

Keywords

Sensors networks; crowd sensing; mobile sensing; participatory sensing; weather forecast