Most current research on Location-Based Services （LBSs, for short） assumes point-to-point wireless commu- nication, where the server processes a query and returns the query result to the user via a point-to-point wi...Most current research on Location-Based Services （LBSs, for short） assumes point-to-point wireless commu- nication, where the server processes a query and returns the query result to the user via a point-to-point wireless channel. However, LBSs via point-to-point wireless channel suffer from a tremendous amount of tramc and service requests from the user and thereby result in poor performance. In this paper, we present broadcast-based spatial query processing algorithms designed to support k-NN （k-Nearest Neighbor） and range queries via a wireless network. The task of the query processor is to selectively monitor the wireless broadcast channel, when the data items are disseminated by the server, according to their locations. Experiments are conducted to evaluate the performance of the proposed algorithms. Comprehensive experiments illustrate that the presented algorithms are highly scalable and are more efficient than the previous techniques in terms of both access time and energy consumption.展开更多
文摘Most current research on Location-Based Services （LBSs, for short） assumes point-to-point wireless commu- nication, where the server processes a query and returns the query result to the user via a point-to-point wireless channel. However, LBSs via point-to-point wireless channel suffer from a tremendous amount of tramc and service requests from the user and thereby result in poor performance. In this paper, we present broadcast-based spatial query processing algorithms designed to support k-NN （k-Nearest Neighbor） and range queries via a wireless network. The task of the query processor is to selectively monitor the wireless broadcast channel, when the data items are disseminated by the server, according to their locations. Experiments are conducted to evaluate the performance of the proposed algorithms. Comprehensive experiments illustrate that the presented algorithms are highly scalable and are more efficient than the previous techniques in terms of both access time and energy consumption.
