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Wireless Data Broadcast Schemes for Location-Dependent Information Services Under a Geometric Location Model

Wireless Data Broadcast Schemes for Location-Dependent Information Services Under a Geometric Location Model

KwangJin Park, MoonBae Song, Chong-Sun Hwang
Copyright: © 2005 |Volume: 1 |Issue: 4 |Pages: 14
ISSN: 1548-0631|EISSN: 1548-064X|ISSN: 1548-0631|EISBN13: 9781615201747|EISSN: 1548-064X|DOI: 10.4018/jbdcn.2005100101
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MLA

Park, KwangJin, et al. "Wireless Data Broadcast Schemes for Location-Dependent Information Services Under a Geometric Location Model." IJBDCN vol.1, no.4 2005: pp.1-14. http://doi.org/10.4018/jbdcn.2005100101

APA

Park, K., Song, M., & Hwang, C. (2005). Wireless Data Broadcast Schemes for Location-Dependent Information Services Under a Geometric Location Model. International Journal of Business Data Communications and Networking (IJBDCN), 1(4), 1-14. http://doi.org/10.4018/jbdcn.2005100101

Chicago

Park, KwangJin, MoonBae Song, and Chong-Sun Hwang. "Wireless Data Broadcast Schemes for Location-Dependent Information Services Under a Geometric Location Model," International Journal of Business Data Communications and Networking (IJBDCN) 1, no.4: 1-14. http://doi.org/10.4018/jbdcn.2005100101

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Abstract

Indexing techniques are used to implement selective tuning in wireless environments. Indices are broadcast together with data to help mobile clients locate the required information. As a result, clients stay in doze mode most of the time. The drawback of this solution is that broadcast cycles are lengthened due to additional index information. In this paper, we introduce the broadcast-based LDIS scheme (BBS) for the mobile computing environment. In the BBS, broadcasted data objects are sorted sequentially, based on their location, and the server broadcasts the location-dependent data (LDD) without additional indices. Then, we present a data prefetching scheme and OBC (Object Boundary Circle) in order to reduce the client’s tuning time. The performance of the proposed scheme is investigated in relation to various environmental variables, such as the distributions of the data objects, the average speed of the clients, and the size of the service area.

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