Internet and mobile computing, location-based services (LBS)-the capability to deliver location-aware content to subscribers on the basis of the positioning capability of the wireless infrastructure-are emerging as key value-added services that telecom operators can offer. To support efficient and effective development and deployment of innovative location-aware applications, a flexible and resilient middleware should be built as the enabling infrastructure. This paper presents the research and efforts made in the IBM China Research Laboratory toward developing an infrastructure that supports location-aware services. This infrastructure is based on a proposed location operating reference model (LORE), which addresses many major aspects of building location-aware services, including positioning, location modeling, location-dependent query processing, tracking, and intelligent location-aware message notification. Three key components of the infrastructure-the location server, a moving object database, and a spatial publish/subscribe engine-are introduced in detail. The location server has a common location adapter framework that supports heterogeneous positioning techniques and industrystandard location application program interfaces (APIs). The moving object database manages the location stream and processes the location-based queries. The spatial publish/subscribe engine enables intelligent location-aware message notification. We also present some location-aware application demonstrations that leverage the LORE infrastructure. Part of our work has been tested in pilot projects with leading carriers in China and has been integrated into the IBM WebSphere Everyplace Suite.

One of the challenges to build a high performance Moving Object Database (MOD) is how to efficiently process the location stream which are inserted at a high rate beyond the tolerance of database, while location accuracy needs to be preserved. Based on CAMEL, an MOD prototype implemented in IBM China Research Lab, we present the location filter module and integrate it into the CAMEL to address the issue. The proposed location filter includes two components: the on-line location filter to filter the location stream online by filtering algorithm plug-ins, and the temporal location manager to insert location data into database at an acceptable insertion rate. An enhanced adaptive location stream filtering algorithm (ALSF) based on the mobility pattern of moving objects, as well as several other filtering algorithms, are also introduced. Experiments with the simulated data show the performance advantage of the enhanced ALSF algorithm.