A partial index for distributed broadcasting in wireless mobile networks

Song, Doohee; Park, Kwangjin

doi:10.1016/j.ins.2016.02.011

Cited by 11 publications

(10 citation statements)

References 21 publications

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“…LBS queries are generally classified as either snapshot queries or continuous queries [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. The query process using snapshot queries is as follows.…”

Section: Related Workmentioning

confidence: 99%

“…The query process using snapshot queries is as follows. Methods using k-anonymity to protect user privacy have been recently proposed [5][6][7][8].…”

Section: Related Workmentioning

confidence: 99%

“…If the server is hacked or otherwise abused, the location of the user can be revealed, potentially causing serious damage. Active research is therefore being conducted on the protection of user locations [6][7][8][9][10]. The D-Bcast model was proposed to enable clients that have not received data from the main server or clients that have moved from other main servers to effectively listen to data [6].…”

Section: Introductionmentioning

confidence: 99%

“…Active research is therefore being conducted on the protection of user locations [6][7][8][9][10]. The D-Bcast model was proposed to enable clients that have not received data from the main server or clients that have moved from other main servers to effectively listen to data [6]. The method proposed by [7] can store the received data in cache and reuse them in order to minimize the exposure of user query service data to the unreliable LBS server.…”

Section: Introductionmentioning

confidence: 99%

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A Privacy‐Preserving Spatial Index for Spatial Query Processing

Song

Park

2018

Wireless Communications and Mobile Computing

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An increasing amount of active research is being conducted to protect the locations of mobile device users. Users must tune to more data than they would like to in order to hide their location. In particular, if a user requests a query over kNN, the number of objects the user must receive may increase. Several studies have been proposed to solve these problems. However, problems have been identified during the course of query processing, such as errors and increased query processing times. When the tuning time is increased, the amount of data to download and the battery consumption of the client also increase. In this study, we propose the Privacy-preserving Spatial Index (PSI), an index that allows users to reduce their tuning time while being satisfied with the results of their queries. The querier (q) requests the object in the area protecting his/her location from the server. The server sends the requested data of points of interest (POIs) (DPOIs) in the Privacy-preserving Region (PR) to q. Finally, q reduces tuning time by selectively tuning to the desired data of POIs (Dw) through PSI. The superiority of PSI over previous techniques is experimentally proven.

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Section: Related Workmentioning

confidence: 99%

“…The query process using snapshot queries is as follows. Methods using k-anonymity to protect user privacy have been recently proposed [5][6][7][8].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Privacy‐Preserving Spatial Index for Spatial Query Processing

Song

Park

2018

Wireless Communications and Mobile Computing

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“…Interleaving spatial indexes with spatial objects could help mobile devices predict the arrival times of spatial objects and their related indexes. This would allow mobile devices to ignore unwanted data, thus reducing energy consumption [14][15][16]. Access and tuning times are the two main performance measures for spatial air index methods in wireless broadcast systems.…”

Section: Introductionmentioning

confidence: 99%

Spatial Air Index Based on Largest Empty Rectangles for Non-Flat Wireless Broadcast in Pervasive Computing

Shen

Chen

Tang

2016

IJGI

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Abstract:In pervasive computing, location-based services (LBSs) are valuable for mobile clients based on their current locations. LBSs use spatial window queries to enable useful applications for mobile clients. Based on skewed access patterns of mobile clients, non-flat wireless broadcast has been shown to efficiently disseminate spatial objects to mobile clients. In this paper, we consider a scenario in which spatial objects are broadcast to mobile clients over a wireless channel in a non-flat broadcast manner to process window queries. For such a scenario, we propose an efficient spatial air index method to handle window query access in non-flat wireless broadcast environments. The concept of largest empty rectangles is used to avoid unnecessary examination of the broadcast content, thus reducing the processing time for window queries. Simulation results show that the proposed spatial air index method outperforms the existing methods under various settings.

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Efficient Processing of Spatial Window Queries for Non-flat Wireless Broadcast

Shen

Mai

2017

Lecture Notes in Electrical Engineering

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A partial index for distributed broadcasting in wireless mobile networks

Cited by 11 publications

References 21 publications

A Privacy‐Preserving Spatial Index for Spatial Query Processing

A Privacy‐Preserving Spatial Index for Spatial Query Processing

Spatial Air Index Based on Largest Empty Rectangles for Non-Flat Wireless Broadcast in Pervasive Computing

Efficient Processing of Spatial Window Queries for Non-flat Wireless Broadcast

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