Seamless Transitions between Filter Schemes for Location-Based Mobile Applications

With a plethora of sensors and ubiquitous access to the Internet, modern smartphones have enabled a broad range of context-based applications. Most applications make use of the user's physical location to filter relevant content. However, filtering based on dynamic contextual information results in high complexity of the filtering process. This limits the applicability of existing publish/subscribe systems, as they rely on aggregation of filters and fast decentralized matching and forwarding. In this work, we propose a mechanism for transitions between different filter schemes for location-based services. Our mechanism adapts the filtering process to the dynamics in user behavior and resulting load by trading computational complexity at the broker against communication overhead and computational complexity at the mobile client. We integrate our mechanism into an existing publish/subscribe system and evaluate transitions between a context-based filter scheme and two channel-based filter schemes, showing the applicability of our approach.