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.























Similar content being viewed by others
Notes
Clients who have no prior knowledge of the contents of the broadcast data will access the directory from air [5].
Nearest neighbor (NN) query is to find the spatial object with the smallest distance to a query position.
Each object determines their maximum speed. For example, a moving car may not exceed a speed of 300 km/h.
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].
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.
The server disseminates data items via one-dimensional wireless broadcast channel and the client sequentially accesses them.
If more than two points have the same x-axis value, upper point is selected first.
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].
Split points represent the points of the query segment where there is a change of neighborhood.
References
Lee, D. L., Lee, W.-C., Xu, J., & Zheng, B. (2002). Data management in location-dependent information services: Challenges and issues. IEEE Pervasive Computing, 1(3), 65–72.
Zheng, B., Xu, J., Lee, W.-C., & Lee, D. L. (2006). Grid-partition index: A hybrid approach to nearest-neighbor queries in wireless location-based services. The International Conference on Very Large Data Bases (VLDB) Journal, 15(1), 21–39.
Zhang, J., & Gruenwald, L. (2002). Prioritized sequencing for efficient query on broadcast geographical information in mobile-computing. In Proceedings of SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS), pp. 88–93.
Zheng, B., Lee, W.-C., & Lee, D. L. (2004). Spatial queries in wireless broadcast systems. Wireless Network, 10(6), 723–736.
Imielinski, T., Viswanathan, S., & Badrinath, B. R. (1997). Data on air: Organization and access. IEEE Transactions on Knowledge and Data Engineering, 9(3), 353–372.
Zheng, B., Lee, W.-C., & Lee, D. L. (2003). Spatial index on air. In Proceedings of Pervasive Computing and Communication (PerCom), pp. 297–304.
Lee, W.-C., & Zheng, B. (2005). DSI: A fully distributed spatial index for location-based wireless broadcast services. In Proceedings of International Conference on Distributed Computing Systems (ICDCS), pp. 349–358.
Saltenis, S., Jensen, C. S., Leutenegger, S. T., & Lopez, M. A. (2000). Indexing the positions of continuously moving objects. In Proceedings of International Conference on Management of Data (SIGMOD), pp. 331–342.
Hu, H., Xu, J., & Lee, D. L. (2005). A generic framework for monitoring continuous spatial queries over moving objects. In Proceedings of International Conference on Management of Data (SIGMOD), pp. 479–490.
Tao, Y., & Papadias, D. (2003). Spatial queries in dynamic environments. ACM Transactions on Database Systems, 28(2), 101–139.
Mouratidis, K., Papadias, D., Bakiras, S., & Tao, Y. (2005). A threshold-based algorithm for continuous monitoring of k nearest neighbors. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1451–1464.
Mokbel, M. F. (2004). Continuous query processing in spatio-temporal databases. In Proceedings of International Conference on Extending Database Technology (EDBT), pp. 100–111.
Prasad Sistla, A., Wolfson, O., Chamberlain, S., & Dao, S. (1997). Modeling and querying moving objects. In Proceedings of International Conference on Data Engineering (ICDE), pp. 422–432.
Song, Z., & Roussopoulos, N. (2001). K-nearest neighbor search for moving query point. In Proceedings of Symposium on Spatial and Temporal Databases (SSTD), pp. 79–96.
Zhang, J., Zhu, M., Papadias, D., Tao, Y., & Lee, D. L. (2003). Location-based spatial queries. In Proceedings of International Conference on Management of Data (SIGMOD), pp. 443–454.
Zheng, B., & Lee, D. L. (2001). Semantic caching in location-dependent query processing. In Proceedings of Symposium on Spatial and Temporal Databases (SSTD), pp. 97–116.
Gedik, B., & Liu, L. (2004). MobiEyes: Distributed processing of continuously moving queries on moving objects in a mobile system. In Proceedings of International Conference on Extending Database Technology (EDBT), pp. 67–87.
Xiong, X., Mokbel, M. F., & Aref, W. G. (2005). SEA-CNN: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In Proceedings of International Conference on Data Engineering (ICDE), pp. 643–654.
Prabhakar, S., Xia, Y., Kalashnikov, D., Aref, W. G., & Hambrusch, S. E. (2002). Query indexing and velocity constrained indexing: Scalable techniques for continuous queries on moving objects. IEEE Transactions on Computers, 51(10), 1124–1140.
Zheng, B., Lee, W.-C., & Lee, D. L. (2004). Search continuous nearest neighbors on the air. In Proceedings of International Conference on Mobile and Ubiquitous Systems (MobiQuitous), pp. 236–245.
Hambrusch, S. E., Liu, E., Aref, W. G., & Prabhakar, S. (2001). Query processing in broadcasted spatial index trees. In Proceedings of Symposium on Spatial and Temporal Databases (SSTD), pp. 502–521.
Zheng, B., Lee, W.-C., & Lee, D. L. (2007). On searching continuous k nearest neighbors in wireless data broadcast systems. IEEE Transactions on Mobible Computing, 6(7), 748–761.
Park, K., Song, M., & Hwang, C.-S. (2006). Adaptive data dissemination schemes for location-aware mobile services. Journal of Systems and Software, 79(5), 674–688.
Park, K., & Choo, H. (2007). Energy-efficient data dissemination schemes for nearest neighbor query processing. IEEE Transactions on Computers, 56(6), 754–768.
Zheng, B., & Lee, D. L. (2005). Information dissemination via wireless broadcast. Communications of the ACM, 48(5), 105–110.
Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. In Proceedings of International Conference on Management of Data (SIGMOD), pp. 47–57.
Xu, J., Zheng, B., Lee, W.-C., & Lee, D. L. (2004). D-tree: An index structure for planar point queries in location-based wireless services. IEEE Transactions on Knowledge and Data Engineering, 16(12), 1526–1542.
Shih, E., Cho, S.-H., Ickes, N., Min, R., Sinha, A., Wang, A., & Chandrakasan, A. (2001). Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks. In Proceedings of International Conference on Mobile Computing and Networking (MOBICOM), pp. 272–287.
Lu, G., Krishnamachari, B., & Raghavendra, C. S. (2004). An adaptive energy-efficient and low-latency MAC for data gathering in sensor networks. In Proceedings of International Workshop on Algorithms for Wireless, Mobile, Ad Hoc and Sensor Networks (WMAN), pp. 1–12.
Ruzzelli, A. G., O’Hare, G. M. P., Tynan, R., Cotan, P., & Havinga, P. J. M. (2006). Protocol assessment issues in low duty cycle sensor networks: The switching energy. In Proceedings of International Conference on Sensor Networks, Ubiquitous, and. Trustworthy Computing (SUTC), pp. 136–143.
Kollios, G., Gunopulos, D., & Tsotras, V. J. (1999). On indexing mobile objects. In Proceedings of Symposium on Principles of Database Systems (PODS), pp. 261–272.
Tao, Y., Papadias, D., & Shen, Q. (2002). Continuous nearest neighbor search. In Proceedings of international Conference on Very Large Data Bases (VLDB), pp. 287–298.
Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communication and Mobile Computing, 2(5), 483–502.
Kasten, O. Energy consumption. ETH-Zurich, Swiss Federal Institute of Technology. Available at http://people.inf.ethz.ch/~kasten/research/bathtub/energy_consumption.htm
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.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-009-0185-y