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A scalable energy-efficient continuous nearest neighbor search in wireless broadcast systems

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Abstract

When the mobile environment consists of light-weight devices, the energy consumption of location-based services (LBSs) and the limited bandwidth of the wireless network become important issues. Motivated by this, we propose new spatial query processing algorithms to support Mobile Continuous Nearest Neighbor Query (MCNNQ) in wireless broadcast environments. Our solution provides a general client–server architecture for answering MCNNQ on objects with unknown, and possibly variable, movement types. Our solution enables the application of spatio-temporal access methods specifically designed for a particular type, to arbitrary movements without any false misses. Our algorithm does not require any conventional spatial index for MCNNQ processing. It can be adapted to static or moving objects, and does not require additional knowledge (e.g., direction of moving objects) beyond the maximum speed and the location of each object. Extensive experiments demonstrate that our location-based data dissemination algorithm significantly outperforms index-based solutions.

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Notes

  1. Clients who have no prior knowledge of the contents of the broadcast data will access the directory from air [5].

  2. Nearest neighbor (NN) query is to find the spatial object with the smallest distance to a query position.

  3. Each object determines their maximum speed. For example, a moving car may not exceed a speed of 300 km/h.

  4. The R-tree is a classical spatial index structure. The basic idea is to approximate a spatial object with a minimal bounding rectangle (MBR) and to index the MBRs recursively [26].

  5. In BBS, indexes are broadcast m times during one broadcast cycle. The whole index is broadcast preceding every fraction \(({\frac{1}{m}})\) of the broadcast cycle [5]. By replicating the index for m times, the waiting time for reaching a forthcoming index segment can be reduced.

  6. The server disseminates data items via one-dimensional wireless broadcast channel and the client sequentially accesses them.

  7. If more than two points have the same x-axis value, upper point is selected first.

  8. In conventional moving query processing over moving objects, there is no accuracy guarantee, since even a high sampling rate may still miss some points of the query segment where there is a change of neighborhood [32].

  9. Split points represent the points of the query segment where there is a change of neighborhood.

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Acknowledgment

The authors would like to thank the editor Ivan Stojmenovic and anonymous reviewers for their valuable comments and suggestions that improved the quality of this paper. This paper was supported by Wonkwang university in 2008.

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Authors and Affiliations

  1. School of Electrical Electronics and Information Engineering, Wonkwang University, Iksan-Shi, Chunrabuk-do, 570-749, Republic of Korea

    Kwangjin Park

  2. School of Information and Communication Engineering, Sungkyunkwan University, Suwon-Shi, Gyeonggi-do, 440-746, Republic of Korea

    Hyunseung Choo

  3. INRIA and LINA, Université de Nantes, 2, rue de la Houssinière, BP 92208, 44322, Nantes Cedex 03, France

    Patrick Valduriez

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  1. Kwangjin Park
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Correspondence to Kwangjin Park.

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Park, K., Choo, H. & Valduriez, P. A scalable energy-efficient continuous nearest neighbor search in wireless broadcast systems. Wireless Netw 16, 1011–1031 (2010). https://doi.org/10.1007/s11276-009-0185-y

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