Efficient data dissemination using locale covers

Abstract

Location-dependent data are central to many emerging applications, ranging from traffic information services to sensor networks. The standard pull- and push-based data dissemination models become unworkable since the data volumes and number of clients are high. We address this problem using locale covers, a subset of the original set of locations of interest, chosen to include at least one location in a suitably defined neighborhood of any client. Since location-dependent values are highly correlated with location, a query can be answered using a location close to the query point. Typical closeness measures might be Euclidean distance, or a k -nearest neighbor criterion. We show that location-dependent queries may be answered satisfactorily using locale covers. Our approach is independent of locations and speeds of clients, and is applicable to mobile clients. We also introduce a nested locale cover scheme that ensures fair access latencies, and allows clients to refine the accuracy of their information over time. We also prove two important results: one regarding the greedy algorithm for sensor covers and the other pertaining to randomized locale covers for k -nearest neighbor queries.