Spatial and continuous spatial queries on smart mobile clients

Abstract

With the number of global mobile subscribers now exceeding 1.5 billion and with various mobile gadgets built on cutting-edge technologies gaining worldwide popularity, location-based services (LBSs), the killer applications in mobile computing, show compelling promise in the telecommunication market. However, although these mobile devices are equipped with CPUs, considerable amount of memory, wireless connections, batteries, and even positioning apparatus, these resources are still limited compared to their desktop/laptop counterparts. As such, research is necessary regarding the efficient management of these resources, especially the data. In this thesis, we generally call these devices mobile and explore their data-management issues in the context of location-based services. More specifically, we investigate how spatial and continuous spatial queries can be efficiently processed on these smart mobile clients, by letting the clients contribute their resources to the processing of these queries. What distinguishes this thesis from existing spatio-temporal database literature is that it proposes both comprehensive frameworks and detailed techniques to exploit the three most distinct features of smart mobile clients, namely, the location sensing, and the inaccuracy of the location-sensing technology. The main body of the thesis addresses the three features and proposes our comprehensive solutions respectively. We first devise a new caching model called proactive caching, which serves as the caching framework to process all types of spatial queries on smart mobile clients. The new caching model reuses the cached data at the object level, and thus achieves outstanding performance compared with traditional page caching or semantic caching methods with respect to cache hit rate and bandwidth saving. We then study location sensing and propose a generic framework for monitoring continuous spatial queries, where the clients detect their own locations and decide if they need to perform location updates. Since clients are aware of the queries being monitored through the notion of safe region, the framework guarantees 100% monitoring accuracy and significant savings in monitoring cost, that is, the wireless bandwidth and the server CPU overhead. To address the inaccuracy issue of the location sensors, we introduce a new type of query that allows the users to specify their locations fuzzily by ranges rather than exact points. (Abstract shortened by UMI.)