Abstract
Caching has been widely used in mobile networks to improve system performance. However, conventional caching methodologies have two major drawbacks in dealing with spatial queries in a dynamic mobile network: (i) the description of cached data is defined based on the query context instead of data content ignoring the spatial or semantic locality of the data. (ii) the description of cached data does not reflect the popularity of the data, making it inefficient in providing QoS-related services. To address these issues, we propose a location-aware caching (LAC) model which reflects the distribution of images based on the analysis of earlier queries. The novelty of our method stems from several factors including: 1) describing the image data distribution based on a Hilbert space-filling curve, 2) optimizing spatial query resolution through efficient exploitation of locally cached data, and 3) reducing the cost of query resolution with restricted search scope. Through extensive simulations, we show that our model can perform spatial search with less cost. In addition, it is scalable to large environments and voluminous data.
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