Mobility-Aware Prefetching and Replacement Scheme for Location-Based Services: MOPAR

Mobility-Aware Prefetching and Replacement Scheme for Location-Based Services: MOPAR

Ajay Kumar Gupta, Udai Shanker
Copyright: © 2021 |Pages: 26
DOI: 10.4018/978-1-7998-7756-1.ch002
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Abstract

Location-based services (LBS) are gaining prominence in today's environment. When a mobile user submits a location-based query in LBS, an adversary may infer the locations or other related sensitive information. Thus, an efficient location privacy preservation model (LPPM) with minimal overhead needs to be built by considering contextual understanding and analytical ability. With consideration of service efficiency and privacy, a location privacy preservation policy, namely mobility-aware prefetching and replacement (MOPAR) policy, has been proposed by the cloaking area formulation through user location, cache contribution rate, and data freshness in LBS. An incorporation of prefetching and replacement to anonymizer and consumer cache with formulation of cloak area is being deployed to protect customer sensitive information. The Markov mobility model-based next-position prediction procedure is used in this chapter for caching and formulation of cloaking area. The results of the simulation show significant enhancement in the efficiency of the location-privacy preservation model.
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Introduction

LBS (Gupta & Shanker, 2020e) is a ubiquitous computer-based services that essentially provides all consumers with enormous valued resources by directly incorporating the location of the devices through global positioning system (GPS) or indirectly using other relevant details in the query. Centred on the upgraded approach, queries can be divided into two forms in any context-aware method, i.e., traditional queries as well as continuous queries (Gupta & Shanker, 2020a). Conventional query data items do not update by themself over period if mobile clients change their locations. Thus, mobile clients only send requests once in continuous queries, and then if any location update happens as clients travel to various areas, the server would be automatically alert about the position or new mobile client query results.

Even though GPS allows efficient outdoor positioning, there are certain portable devices not having the built-in GPS receiver. Therefore, they may not be suitable for location detection of wireless devices. Sukaphat (Sukaphat, 2011) has developed a location based application with an android based identifier API to solve this problem. The location tracking process is conducted as a background procedure in this system and the transfer of this information regularly takes place within a cycle. Geographic Information System (GIS) services on mobile gadgets are beginning to expand. GeoFairy (Sun et al., 2017) is an application of LBS that offers geospatial information in real-time for the users (Gupta & Shanker, 2018a). OpenLayers is another open-source JavaScript library similar to the Google Maps API that provides APIs for viewing map data and creating web-based geospatial applications. Raster and vector data can also be made by OpenLayers from a range of file types including the web servers. The implementation of GeoPackage (C. Zhang et al., 2016) is a smartphone application that allows users to view, monitor, interpret and simulate details on both OpenLayers and Google Maps.

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