DOI QR코드

DOI QR Code

Spatio-temporal Sensor Data Processing Techniques

  • Kim, Jeong-Joon (Dept. of Computer Science & Engineering, Korea Polytechnic University)
  • Received : 2017.04.07
  • Accepted : 2017.06.04
  • Published : 2017.10.31

Abstract

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of Internet of Things (IoT) technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatialtemporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

Keywords

References

  1. A. Raza, "Working with spatio-temporal data type," in Proceeding of the 22nd Congress of the International Society for Photogrammetry and Remote Sensing, Melbourne, Australia, 2012, pp. 5-10.
  2. A. D'Ulizia, F. Ferri, and P. Grifoni, "Moving GeoPQL: a pictorial language towards spatio-temporal queries," GeoInformatica, vol. 16, no. 2, pp. 357-389, 2012. https://doi.org/10.1007/s10707-011-0135-6
  3. International Organization for Standardization, Data Elements and Interchange Formats-Information Interchange-Representation of Dates and Times, ISO 8601:2004, 2004.
  4. S. H. Kim, D. H. Kim, and H. D. Park, "A tracking service of animal situation using RFID, GPS, and sensor," Journal of the Institute of Internet, Broadcasting and Communication, vol. 9, no. 5, pp. 79-84, 2009.
  5. S. R. Madden, M. J. Franklin, J. M. Hellerstein, and W. Hong, "TinyDB: an acquisitional query processing system for sensor networks," ACM Transactions on Database Systems, vol. 30, no. 1, pp. 122-173, 2005. https://doi.org/10.1145/1061318.1061322
  6. P. Levis, S. Madden, J. Polastre, R. Szewczyk, K. Whitehouse, A. Woo, et al, "TinyOS: an operating system for wireless sensor networks," in Ambient Intelligence. New York, NY: Springer, 2005, pp. 115-148.
  7. Open Geospatio Consortium, OpenGIS Implementation Specification for Geographic Information -Simple Feature Access - Part 1: Common Architecture (version 1.2.1), 2011.
  8. Open Geospatio Consortium, OpenGIS Implementation Standard for Geographic Information -Simple Feature Access - Part 2: SQL Option (version 1.2.1), 2011.
  9. P. D. Felice, M. Ianni, and L. Pomante, "A spatial extension of TinyDB for wireless sensor networks," in Proceeding of IEEE Symposium on Computers and Communications, Marrakech, Morocco, 2008, pp. 1076-1082.
  10. D. O. Kim, L. Liu, I. S. Shin, J. J. Kim, and K. J. Han, "Spatial TinyDB: a spatial sensor database system for the USN environment," International Journal of Distributed Sensor Networks, vol. 9, no. 8, article no. 512368, 2013.
  11. N. M. Laxaman, M. D. J. S. Goonathillake, and K. D. Zoysa, "Tikiridb: shared wireless sensor network database for multi-user data access," in Proceeding of Conference on Computer Society of Sri Lanka, Colombo, Sri Lanka, 2010.

Cited by

  1. An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects vol.10, pp.8, 2018, https://doi.org/10.3390/su10082614