QLP-LBS: quantization and location prediction-based LBS for reduction of location update costs

  • Authors:

  • In Kee Kim;Sung Ho Jang;Jong Sik Lee

  • Affiliations:

  • School of Computer Science and Information Engineering, Inha University, Incheon, South Korea;School of Computer Science and Information Engineering, Inha University, Incheon, South Korea;School of Computer Science and Information Engineering, Inha University, Incheon, South Korea

  • Venue:
  • ISPA'07 Proceedings of the 2007 international conference on Frontiers of High Performance Computing and Networking
  • Year:
  • 2007

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Abstract

This paper proposes the QLP-LBS (Quantization and Location Prediction-based LBS). This QLP-LBS system is based on quantization theory and uses statistical location prediction mechanism. This LBS applies the quantum range of quantization theory to each mobile user and reduces location update costs by comparing results between moving distance of mobile user and quantum range. But, this LBS system generates location errors from the quantization. In order to solve this problem, we apply statistical location prediction mechanism to LBS system. This prediction mechanism predicts location of mobile user using its historical path and decrease location errors by quantization and makes more reliable LBS system. For performance evaluation, this paper measures location accuracy and reduction rate of location update costs with various quantum ranges. This experiments show that QLP-LBS effectively reduces location update costs of LBS system. Also, QLP-LBS solves problem of location errors using location prediction mechanism which is problem of general quantized system. Therefore, QLP-LBS is solution for reduction of location update costs and has reliable location accuracy.