Abstract
A content-matched (CM) range monitoring query over moving objects continually retrieves the moving objects (i) whose non-spatial attribute values are matched to given non-spatial query values; and (ii) that are currently located within a given spatial query range. In this paper, we propose a new query indexing structure, called the group-aware query region tree (GQR-tree) for efficient evaluation of CM range monitoring queries. The primary role of the GQR-tree is to help the server leverage the computational capabilities of moving objects in order to improve the system performance in terms of the wireless communication cost and server workload. Through a series of comprehensive simulations, we verify the superiority of the GQR-tree method over the existing methods.
Highlights
For evaluation of CM range monitoring queries, in the previous paper [1], we proposed an enhanced version of the QR-tree, called the bit-vector query region tree (BQR-tree), which stores the additional bit-vector information required to describe the non-spatial query values
In Monitoring query management (MQM), query region-tree method (QRT), BQR-tree method (BQRT), and GQR-tree method (GQRT), the CPU-time performance is mainly affected by the search process for assigning resident domains to moving objects, whereas, in safe region method (SR), the CPU-time performance is mainly affected by safe region computation
As shown in the figure, SR performs worst for Uniform because as the number of queries becomes larger, the size of a safe region assigned to each moving object o becomes smaller
Summary
With the technological advances in wireless networks and the wide deployment of mobile devices equipped with location sensing technology (e.g., smart phones and pads), location-based services (LBSs) have attracted much attention as one of the most promising applications in recent years [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]. Sensors 2015, 15 monitoring query, which is defined as (i) retrieving the moving objects located within a client-specified spatial query range and (ii) keeping the query result up to date during a certain time period, can be used in many LBSs such as mobile advertising and traffic condition monitoring. Let us consider the scenario of a mobile advertising service, where an advertiser (i.e., client) plans to send advertising messages to the nearby potential customers (i.e., moving objects) who have opted into the mobile advertising service. In many real-life LBSs, advertisers are moving away from bombarding customers with the same advertising messages regardless of whether the messages are relevant to the customers Instead, they are moving toward sending different advertising messages to different customers by specifying non-spatial target criteria. Let us suppose that a restaurant owner (i.e., client) wants to send advertising messages to only the nearby vegetarian customers whose ages are between
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