KIPS Transactions on Computer and Communication Systems (정보처리학회논문지:컴퓨터 및 통신 시스템)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

An Efficient Spatial Query Processing in Wireless Networks

무선 네트워크 환경에서 효율적인 공간 질의 처리

  • Received : 2019.05.30
  • Accepted : 2019.08.21
  • Published : 2019.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

In recent mobile environments, query processing costs have been rapidly increasing as users request large amounts of queries. In addition, the server's performance is increasing for many users to handle high-capacity queries, but the workload is increasing continuously. To solve these problems, we use the wireless broadcasting environment. However, in a existing wireless broadcasting environment, servers have a problem sending all the objects they manage to their clients. Therefore, we propose a new R-Bcast combining the advantages of demand-based and wireless broadcasting. R-Bcast is a technique that protects query information and reduces query processing time. Experiments have proved that R-Bcast is superior to conventional techniques.

최근 모바일 환경에서 사용자들이 대용량의 질의를 요청함에 따라 질의 처리 비용이 급격히 증가하고 있다. 서버는 대용량의 질의를 처리하기 위하여 서버의 성능이 향상되고 있지만 하드웨어 측면의 향상보다 작업 부하가 더욱 증가하고 있는 실정이다. 이러한 문제를 해결하기 위하여 우리는 무선 방송환경을 활용한다. 그러나 기존의 무선방송 환경에서 서버는 자신이 관리하는 객체들을 모두 클라이언트에게 전송하는 문제점이 존재한다. 따라서 우리는 요구기반 방식과 무선방송 방식의 장점을 취합한 새로운 R-Bcast를 제안한다. R-Bcast는 질의자의 정보를 보호하면서 질의처리 시간을 줄일 수 있는 기법이다. 실험을 통해 R-Bcast가 기존 기법보다 우수함을 증명했다.

Keywords

References

  1. G. Chatzimilioudis, C. Costa, D. Zeinalipour-Yazti, W.-C. Lee, and E. Pitoura, "Distributed In-Memory Processing of All k Nearest Neighbor Queries," IEEE Transactions on Knowledge and Data Engineering, Vol.28, No.4, 2016.
  2. B. Niu, X. Zhu, W. Li, H. Li, Y. Wang, and Z. Lu, "A personalized two-tier cloaking scheme for privacy-aware location-based services," in Proc. IEEE International Conference on Computing, Networking and Communications, pp.94-98, 2015.
  3. D. Song, J. Sim, K. Park, and M. Song, "A privacy-preserving continuous location monitoring system for location-based services," International Journal of Distributed Sensor Networks, pp.1-10, 2015.
  4. X. He, R. Jin, and H. Dai, "Leveraging Spatial Diversity for Privacy-Aware Location-Based Services in Mobile Networks," IEEE Transactions on Information Forensics and Security, Vol.13, No.6, pp.1524-1534, 2018. https://doi.org/10.1109/TIFS.2018.2797023
  5. K. Mouratidis, S. Bakiras, and D. Papadias, "Continuous Monitoring of Spatial Queries in Wireless Broadcast Environments," IEEE Transactions on Mobile Computing, Vol.8, No.10, pp.1297-1311, 2009. https://doi.org/10.1109/TMC.2009.14
  6. T. Imielinski, S. Viswanathan, and B. R. Badrinath, "Data on Air: Organization and Access," IEEE Transactions on Knowledge and Data Engineering, Vol.9, No.3, pp.353-372, 1997. https://doi.org/10.1109/69.599926
  7. Z. Yu, Y. Liu, X. Yu, and K. Q. Pu, "Scalable Distributed Processing of K Nearest Neighbor Queries over Moving Objects," IEEE Transactions on Knowledge and Data Engineering, Vol.27, No.5, pp.1383-1396, 2015. https://doi.org/10.1109/TKDE.2014.2364046
  8. J. Zhou, H. Teng, Z. Yu, D. Wang, and J. Wang, "Distributed processing of continuous range queries over moving objects," in Proc. Information and Communications, pp.800-810, 2017.
  9. G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K. -L. Tan, "Private queries in location based services: Anonymizers are not necessary," in Proc. ACM SIGMOD International Conference on Management Data, pp.121-132, 2008.
  10. R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu, "Hippocratic databases," in Proc. International Conference on Very Large Data Bases, pp.143-154, 2002.
  11. C. -Y. Chow, M. F. Mokbel, and X. Liu, "A peer-to-peer spatial cloaking algorithm for anonymous location-based service," in Proc. International Conference on ACM Advanced Geographic Information Systems, pp.171-178, 2006.
  12. L. Sweeney, "K-anonymity: A model for protecting privacy," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol.10, No.5, pp.557-570, 2002. https://doi.org/10.1142/S0218488502001648
  13. T. Allard, G. Hebrail, F. Masseglia, and E. Pacitti, "A New Privacy-Preserving Solution for Clustering Massively Distributed Personal Times-Series," in Proc. International Conference on Data Engineering, pp.1370-1373, 2016.
  14. M. H. Afifi, K. Zhou, and J. Ren, "Privacy Characterization and Quantification in Data Publishing," IEEE Transactions on Knowledge and Data Engineering, Vol.30, No.9, pp. 1756-1769, 2018. https://doi.org/10.1109/TKDE.2018.2797092
  15. B. Niu, X. Zhu, X. Lei, and H. Li, "Privacy-area aware dummy generation algorithms for location-based services," in Proc. IEEE International Conference on Communications, pp. 957-962, 2014.
  16. H. Liu, X. Li, H. Li, J. Ma, and X. Ma, "Spatiotemporal Correlation-Aware Dummy-Based Privacy Protection Scheme for Location-Based Services," in Proc. IEEE Conference on Computer Communications, pp.1-9, 2017.
  17. D. Wu, Y. Zhang, and Y. Liu, "Dummy Location Selection Scheme for K-anonymity in Location based Services," in Proc. IEEE Trustcom/BigDataSE/ICESS, pp.441-448, 2017.