FogLBS: Utilizing fog computing for providing mobile Location-Based Services to mobile customers

Abstract

The growth of Location-Based Services (LBSs) has been made possible by the widespread use of GPS-enabled devices. Some important LBSs require the ability to quickly process moving spatial-keyword queries over moving objects, such as when a moving customer is looking for a nearby mobile fuel delivery service. While there have been solutions proposed for scenarios where either the queries or the objects being queried are moving, there is still a need for solutions that can handle scenarios where both are in motion. This research focuses on the application of fog computing to provide real-time processing of moving spatial-keyword queries for LBSs. Specifically, the research proposes a new model, FogLBS, designed to efficiently process moving continuous top-k spatial-keyword queries over moving objects in a directed streets network, with a particular emphasis on the use case of a mobile service provider. FogLBS computes queries’ answer sets for time intervals and incrementally updates them using novel optimization techniques and indexing structures. By implementing FogLBS in a fog computing architecture, the model is able to meet the real-time requirements of the service provider application and other similar LBSs. The results of extensive experiments demonstrate the effectiveness of the proposed model in terms of efficiency, scalability, and accuracy, making it a valuable contribution to the field of fog computing in LBSs.