隨著無線網路的快速發展與行動裝置的普及，越來越多的使用者採用行動上網的方式來獲取所需的資料。在無線行動網路中，如何讓使用者快速且正確地取得其所需求的資料與資訊，即是一相當重要的關鍵，也是本論文主要的研究議題。在本論文中，我們針對兩類不同的網路環境與資料查詢方式來探討其機制。其一，在行動隨意網路(mobile ad hoc network) 中，資料以快取(cache) 的方式共享，使用者將查詢所得的資料放在自身的快取中，以利後續自身使用或是分享給鄰近節點使用。其二，在無線行動網路中，使用者以自身所在的位置為基礎，進行多種不同的空間查詢(spatial query)。
在快取共享部分，傳統上都採取兩種不同型態的快取分享技術：推播式(push-based) 與拉曳式(pull-based)，然而此兩種方法有各自的優缺點，前者會有主動推播的成本花費，而後者則可能造成資料重複傳送或是等待時間過長等問題。為解決快取共享資料的存取問題，我們提出了混合型的資料共享機制，稱為Pull-based with Piggybacked Push Protocol（簡稱PPP）。此機制可以有效地結合兩種機制的優點，若欲取得的資料儲存在鄰近節點的快取中，使用者能利用拉曳式技術獲取該資料；此外，採用挾帶(piggyback) 的推播方式，同時將資料儲存的消息傳送給鄰近節點周知，以利後續分享使用。此機制能有效地提昇資料查詢節點附近，快取共享的效能，且降低使用者的等待時間。
第二項研究議題，與近年來相當熱門的以位置為基礎的服務(Location-Based Service, LBS) 息息相關。在LBS 服務中，最大宗的莫過於以使用者位置為中心，所進行的多種空間查詢，如最近k 點查詢、範圍查詢等等。論文中主要探討較為複雜的，連續型全員最近k 點查詢問題(continuous all k-nearest neighbor query, CAkNN query)。所謂CAkNN 查詢，即是網路中所有的使用者都參與kNN 的查詢，每個點本身既是查詢者也是被查詢者，且持續地想知道自身最即時的kNN查詢結果。我們提出了以bucket PR quadtree 為基礎的結構，利用該結構的特點，可以快速地找出各節點的kNN 結果；此外，論文中提出兩種找尋反向最近k點(reverse k-nearest neighbor, RkNN) 的機制，能在節點移動時，有效率地找出受影響的其他節點，進而快速更新其kNN 查詢結果。模擬結果顯示我們的方法大幅地提昇了無線網路中資料共享與連續型全員最近k點查詢的整體效能。
最後，我們將CAkNN 查詢議題延伸至道路網路。道路網路由於路段的特性，其較上述一般平面網路擁有較多限制，計算方式也有所不同。除了運用前項研究的基礎，論文中亦提出多樣策略與理論，可有效幫助在道路網路中實現CAkNN查詢，且降低查詢所需的計算量。

Recent advance of wireless communication and easy availability of mobile devices had fueled the huge amount of data access demands among mobile users. Essentially, accessing the required data or information is the ultimate goal of a mobile users in a wireless environment. In this dissertation, we conduct research on the data access issues in two different domains and propose some related techniques to facilitate the process.
First, we study the cache sharing issue in a mobile ad hoc network (MANET). Cache sharing has been a popular technique used to support data access in mobile network environments. Existing methods generally resort to two kinds of techniques, push-based and pull-based. The push-based techniques suffer from the proactive advertising cost whereas the pull-based techniques may result in high response latency or redundant data copies. The key to cache sharing is to knowing the whereabouts of a nearby caching site and the
data it possessed.
In this dissertation, we propose a cache sharing protocol, called Pull with Piggybacked Push (PPP), in a MANET environment. PPP is unique in that it completely exploits data request broadcasts by performing data pull and index push operations together. In this manner, it retains the advantages of both techniques: performance and communication overhead. With data-pull packet, PPP fully utilizes the cache contents within its zone and delivers index-push information with virtually no overhead.
Second, this dissertation considers the location-based spatial query processing in a mobile environment. We investigate the continuous all k-nearest neighbor (CAkNN) query, which is a spatial query that continuously monitors kNN results for all mobile users in the network for a specified period of time. This problem is distinguished by the fact that a single movement of a node may result in that several nodes have to recompute their kNN results, which are the moving node itself and its Reverse k-Nearest Neighbor (RkNN) nodes at both the previous and the new locations.
In this dissertation, we adopt the bucket point-region quadtree as the indexing structure for mobile users. The maintenance of a quadtree is simple and can be localized to the vicinity of the moving node of concern. Besides, the structure helps a node to quickly determine an appropriate search radius, which is crucial for finding/verifying the kNN result. Moreover, we propose two novel RkNN techniques, coined MaxPd and QuadPd, that are tailored for the quadtree structure. These RkNN techniques enable us to integrate the kNN and RkNN searching operations together so as to facilitate the query process. Based on the notion of maximum prune distance, we present two algorithms in order to offer true continuity for a CAkNN query.
With the basis of above research effort, we further extend the CAkNN query problem into a road network. This dissertation proposes some strategies and theorems to facilitate the query processing among such network-constrained environment.

[1] Clausen, T and Jacquet, P, “Optimized Link State Routing Protocol (OLSR),” Internet RFC 3626,
2003.
[2] J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva, “A performance comparison of
multi-hop wireless ad hoc network routing protocols,” in Proceedings of the 4th annual ACM/IEEE
international conference on Mobile computing and networking, MobiCom ’98, pp. 85–97, 1998.
[3] C. Perkins and E. Royer, “Ad-hoc on-demand distance vector routing,” in Proceedings of Second IEEE
Workshop on Mobile Computing Systems and Applications, pp. 90–100, 1999.
[4] S. Das, C. Perkins, and E. Royer, “Performance comparison of two on-demand routing protocols for
ad hoc networks,” in Proceedings of IEEE INFOCOM 2000, vol. 1, pp. 3–12 vol.1, 2000.
[5] Y. Xu, J. Heidemann, and D. Estrin, “Geography-informed energy conservation for ad hoc routing,”
in Proceedings of the 7th annual international conference on Mobile computing and networking, MobiCom
’01, pp. 70–84, 2001.
[6] W.-H. Liao, J.-P. Sheu, and Y.-C. Tseng, “Grid: A fully location-aware routing protocol for mobile ad
hoc networks,” Telecommunication Systems, vol. 18, no. 1-3, pp. 37–37, 2001.
[7] G.-M. Chiu and C.-R. Young, “Exploiting in-zone broadcasts for cache sharing in mobile ad hoc networks,”
IEEE Transactions on Mobile Computing, vol. 8, no. 3, pp. 384–397, 2009.
[8] K. Virrantaus, J. Markkula, A. Garmash, V. Terziyan, J. Veijalainen, A. Katanosov, and H. Tirri,
“Developing gis-supported location-based services,” in Proc. Intl. Conf. on Web Information Systems
Engineering, vol. 2, pp. 66–75, Dec. 2001.
[9] C. Ververidis and G. Polyzos, “Mobile marketing using a location based service,” in Proceedings of
the First International Conference on Mobile Business, Athens, Greece, Citeseer, 2002.
[10] B. Rao and L. Minakakis, “Evolution of mobile location-based services,” Commun. ACM, vol. 46,
pp. 61–65, December 2003.
[11] J. Schiller and A. Voisard, Location-Based Services. Elsevier, 2004.
[12] S. Prabhakar, Y. Xia, D. Kalashnikov, W. Aref, and S. Hambrusch, “Query indexing and velocity constrained
indexing: scalable techniques for continuous queries on moving objects,” IEEE Transactions
on Computers, vol. 51, pp. 1124–1140, Oct. 2002.
[13] J. Zhang, M. Zhu, D. Papadias, Y. Tao, and D. L. Lee, “Location-based spatial queries,” in Proc. ACM
SIGMOD, (New York, NY, USA), pp. 443–454, ACM, 2003.
[14] M. F. Mokbel, X. Xiong, and W. G. Aref, “Sina: scalable incremental processing of continuous queries
in spatio-temporal databases,” in Proc. ACM SIGMOD, (New York, NY, USA), pp. 623–634, ACM,
2004.
[15] B. Gedik and L. Liu, “Mobieyes: A distributed location monitoring service using moving location
queries,” IEEE Transactions on Mobile Computing, vol. 5, no. 10, pp. 1384–1402, 2006.
[16] W.-S. Ku, R. Zimmermann, and H. Wang, “Location-based spatial query processing in wireless broadcast
environments,” IEEE Transactions on Mobile Computing, vol. 7, pp. 778–791, June 2008.
[17] T. Do, K. Hua, and C.-S. Lin, “Extrange: Continuous moving range queries in mobile peer-to-peer
networks,” in Proc. International Conference on Mobile Data Management: Systems, Services and
Middleware, pp. 317–322, May 2009.
[18] C.-Y. Chow, M. Mokbel, and H. V. Leong, “On efficient and scalable support of continuous queries in
mobile peer-to-peer environments,” IEEE Transactions on Mobile Computing, vol. 10, pp. 1473–1487,
oct. 2011.
[19] N. Roussopoulos, S. Kelley, and F. Vincent, “Nearest neighbor queries,” in Proc. ACM SIGMOD,
SIGMOD ’95, (New York, NY, USA), pp. 71–79, ACM, 1995.
[20] Z. Song and N. Roussopoulos, “K-nearest neighbor search for moving query point,” in Proc. International
Symposium on Advances in Spatial and Temporal Databases, (London, UK, UK), pp. 79–96,
Springer-Verlag, 2001.
[21] S.-H. Wu, K.-T. Chuang, C.-M. Chen, and M.-S. Chen, “Diknn: An itinerary-based knn query processing
algorithm for mobile sensor networks,” in Proc. International Conference on Data Engineering,
pp. 456–465, April 2007.
[22] W. Wu, W. Guo, and K.-L. Tan, “Distributed processing of moving k-nearest-neighbor query on moving
objects,” in Proc. International Conference on Data Engineering, (Los Alamitos, CA, USA),
pp. 1116–1125, IEEE Computer Society, 2007.
[23] K. Kim, Y. Cai, and W. Tavanapong, “Safe-time: Distributed real-time monitoring of cknn in mobile
peer-to-peer networks,” in Proc. International Conference on Mobile Data Management, (Washington,
DC, USA), pp. 124–131, IEEE Computer Society, 2008.
[24] X. Xiong, M. F. Mokbel, and W. G. Aref, “Sea-cnn: Scalable processing of continuous k-nearest
neighbor queries in spatio-temporal databases,” in Proc. International Conference on Data Engineering,
(Los Alamitos, CA, USA), pp. 643–654, IEEE Computer Society, 2005.
[25] X. Yu, K. Q. Pu, and N. Koudas, “Monitoring k-nearest neighbor queries over moving objects,” in
Proc. International Conference on Data Engineering, (Washington, DC, USA), pp. 631–642, IEEE
Computer Society, 2005.
[26] K. Mouratidis, D. Papadias, and M. Hadjieleftheriou, “Conceptual partitioning: an efficient method
for continuous nearest neighbor monitoring,” in Proc. SIGMOD, (New York, NY, USA), pp. 634–645,
ACM, 2005.
[27] Y. Chen and J. Patel, “Efficient evaluation of all-nearest-neighbor queries,” in Proc. ICDE, pp. 1056–
1065, April 2007.
[28] J. Sankaranarayanan, H. Samet, and A. Varshney, “A fast all nearest neighbor algorithm for applications
involving large point-clouds,” Computers & Graphics, vol. 31, no. 2, pp. 157–174, 2007.
[29] T. Emrich, F. Graf, H.-P. Kriegel, M. Schubert, and M. Thoma, “Optimizing all-nearest-neighbor
queries with trigonometric pruning,” in Scientific and Statistical Database Management (M. Gertz
and B. Ludäscher, eds.), vol. 6187, pp. 501–518, Springer Berlin Heidelberg, 2010.
[30] G. Chatzimilioudis, D. Zeinalipour-Yazti, W.-C. Lee, and M. Dikaiakos, “Continuous all k-nearestneighbor
querying in smartphone networks,” in Proc. MDM, pp. 79–88, July 2012.
[31] T. Hara, “Effective replica allocation in ad hoc networks for improving data accessibility,” in Proceedings
of IEEE INFOCOM 2001, vol. 3, pp. 1568–1576 vol.3, 2001.
[32] T. Hara, “Replica allocation in ad hoc networks with periodic data update,” in Proceedings of the Third
International Conference on Mobile Data Management, MDM ’02, pp. 79–86, 2002.
[33] T. Hara and S. Madria, “Data replication for improving data accessibility in ad hoc networks,” IEEE
Transactions on Mobile Computing, vol. 5, no. 11, pp. 1515–1532, 2006.
[34] L. Yin and G. Cao, “Supporting cooperative caching in ad hoc networks,” IEEE Transactions on Mobile
Computing, vol. 5, pp. 77–89, Jan. 2006.
[35] J.-L. Huang and M.-S. Chen, “On the effect of group mobility to data replication in ad hoc networks,”
IEEE Transactions on Mobile Computing, vol. 5, no. 5, pp. 492–507, 2006.
[36] L. Fan, P. Cao, J. Almeida, and A. Z. Broder, “Summary cache: a scalable wide-area web cache sharing
protocol,” IEEE/ACM Trans. Netw., vol. 8, pp. 281–293, June 2000.
[37] A. Rousskov and D. Wessels, “Cache digests,” Computer Networks and ISDN Systems, pp. 22–23,
1998.
[38] J. Shim, P. Scheuermann, and R. Vingralek, “Proxy cache algorithms: design, implementation, and
performance,” IEEE Transactions on Knowledge and Data Engineering, vol. 11, no. 4, pp. 549–562,
1999.
[39] D. Wessels and K. Claffy, “Icp and the squid web cache,” IEEE JOURNAL ON SELECTED AREAS
IN COMMUNICATION, vol. 16, pp. 345–357, 1998.
[40] C.-Y. Chow, H. Leong, and A. Chan, “Cache signatures for peer-to-peer cooperative caching in mobile
environments,” in Proc. 18th International Conference on Advanced Information Networking and
Applications, 2004. AINA 2004., vol. 1, pp. 96–101 Vol.1, 2004.
[41] C.-Y. Chow, H. Leong, and A. Chan, “Peer-to-peer cooperative caching in mobile environments,”
in Proc. 24th International Conference on Distributed Computing Systems Workshops, pp. 528–533,
2004.
[42] C.-Y. Chow, H. V. Leong, and A. Chan, “Group-based cooperative cache management for mobile
clients in a mobile environment,” in Proc. International Conference on Parallel Processing (ICPP),
pp. 83–90 vol.1, 2004.
[43] M. Papadopouli and H. Schulzrinne, “Effects of power conservation, wireless coverage and cooperation
on data dissemination among mobile devices,” in Proceedings of the 2nd ACM international
symposium on Mobile ad hoc networking & computing, MobiHoc ’01, pp. 117–127, 2001.
[44] W. H. O. Lau, M. Kumar, and S. Venkatesh, “A cooperative cache architecture in support of caching
multimedia objects in manets,” in Proceedings of the 5th ACM international workshop on Wireless
mobile multimedia, WOWMOM ’02, pp. 56–63, 2002.
[45] M. Takaaki and H. Aida, “Cache data access system in ad hoc networks,” in Proc. the 57th IEEE
Semiannual Vehicular Technology Conference, vol. 2, pp. 1228–1232 vol.2, 2003.
[46] Y.-C. Tseng, S.-Y. Ni, Y.-S. Chen, and J.-P. Sheu, “The broadcast storm problem in a mobile ad hoc
network,” Wireless Networks, vol. 8, no. 2-3, pp. 153–167, 2002.
[47] H. Artail, H. Safa, K. Mershad, Z. Abou-Atme, and N. Sulieman, “Coacs: A cooperative and adaptive
caching system for manets,” IEEE Transactions on Mobile Computing, vol. 7, no. 8, pp. 961–977,
2008.
[48] F. Sailhan and V. Issarny, “Cooperative caching in ad hoc networks,” in Proc. 4th International Conference
on Mobile Data Management, vol. 2574, pp. 13–28, Springer Berlin Heidelberg, 2003.
[49] N. Chand, R. C. Joshi, and M. Misra, “Cooperative caching in mobile ad hoc networks based on data
utility,” Mob. Inf. Syst., vol. 3, pp. 19–37, Jan. 2007.
[50] N. Chand, R. Joshi, and M. Misra, “Cooperative caching strategy in mobile ad hoc networks based on
clusters,” Wireless Personal Communications, vol. 43, no. 1, pp. 41–63, 2007.
[51] H. Shen, S. Das, M. Kumar, and Z. Wang, “Cooperative caching with optimal radius in hybrid wireless
networks,” NETWORKING 2004. Networking Technologies, Services, and Protocols; Performance of
Computer and Communication Networks; Mobile and Wireless Communications, vol. 3042, pp. 841–
853, 2004.
[52] B. Tang, H. Gupta, and S. R. Das, “Benefit-based data caching in ad hoc networks,” IEEE Transactions
on Mobile Computing, vol. 7, pp. 289–304, Mar. 2008.
[53] X. Fan, J. Cao, and W. Wu, “Contention-aware data caching in wireless multi-hop ad hoc networks,”
J. Parallel Distrib. Comput., vol. 71, pp. 603–614, Apr. 2011.
[54] M. Fiore, F. Mininni, C. Casetti, and C. Chiasserini, “To cache or not to cache?,” in Proc. INFOCOM
2009, pp. 235–243, 2009.
[55] A. Guttman, “R-trees: a dynamic index structure for spatial searching,” in Proc. ACM SIGMOD,
SIGMOD ’84, (New York, NY, USA), pp. 47–57, ACM, 1984.
[56] D. Greene, “An implementation and performance analysis of spatial data access methods,” in Proceedings
of Fifth International Conference on Data Engineering, pp. 606 –615, feb 1989.
[57] N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger, “The r*-tree: An efficient and robust access
method for points and rectangles,” in Proc. ACM SIGMOD, (New York, NY, USA), pp. 322–331,
ACM, 1990.
[58] J. Zhang, N. Mamoulis, D. Papadias, and Y. Tao, “All-nearest-neighbors queries in spatial databases,”
in Proceedings of International Conference on Scientific and Statistical Database Management,
pp. 297–306, June 2004.
[59] K. Clarkson, “Fast algorithms for the all nearest neighbors problem,” in Proceedings of 24th Annual
Symposium on Foundations of Computer Science, pp. 226–232, Nov 1983.
[60] Y. Wang, Y. Gao, L. Chen, G. Chen, and Q. Li, “All-visible-k-nearest-neighbor queries,” in Database
and Expert Systems Applications, vol. 7447, pp. 392–407, Springer Berlin Heidelberg, 2012.
[61] B. Zheng and D. L. Lee, “Semantic caching in location-dependent query processing,” in Proc. International
Symposium on Advances in Spatial and Temporal Databases, (London, UK), pp. 97–116,
Springer-Verlag, 2001.
[62] Y. Tao, D. Papadias, and Q. Shen, “Continuous nearest neighbor search,” in Proc. VLDB, pp. 287–298,
VLDB Endowment, 2002.
[63] S. Šaltenis, C. S. Jensen, S. T. Leutenegger, and M. A. Lopez, “Indexing the positions of continuously
moving objects,” in Proc. ACM SIGMOD, SIGMOD ’00, (New York, NY, USA), pp. 331–342, ACM,
2000.
[64] Y. Tao, D. Papadias, and J. Sun, “The tpr*-tree: an optimized spatio-temporal access method for
predictive queries,” in Proc. VLDB, VLDB ’2003, pp. 790–801, 2003.
[65] J. M. Patel, Y. Chen, and V. P. Chakka, “Stripes: an efficient index for predicted trajectories,” in Proc.
ACM SIGMOD, SIGMOD ’04, (New York, NY, USA), pp. 635–646, ACM, 2004.
[66] M. Cheema, L. Brankovic, X. Lin, W. Zhang, and W. Wang, “Multi-guarded safe zone: An effective
technique to monitor moving circular range queries,” in Proc. ICDE, pp. 189 –200, march 2010.
[67] P. Galdames, K. Kim, and Y. Cai, “A generic platform for efficient processing of spatial monitoring
queries in mobile peer-to-peer networks,” in Proc. MDM, pp. 1–10, May 2010.
[68] B. Gedik, K.-L. Wu, P. Yu, and L. Liu, “Motion adaptive indexing for moving continual queries over
moving objects,” in Pro. ACM international conference on Information and knowledge management,
(New York, NY, USA), pp. 427–436, ACM, 2004.
[69] H. Hu, J. Xu, and D. L. Lee, “A generic framework for monitoring continuous spatial queries over
moving objects,” in Proc. ACM SIGMOD, (New York, NY, USA), pp. 479–490, ACM, 2005.
[70] K. Mouratidis, D. Papadias, S. Bakiras, and Y. Tao, “A threshold-based algorithm for continuous
monitoring of k nearest neighbors,” IEEE Trans. on Knowl. and Data Eng., vol. 17, pp. 1451–1464,
November 2005.
[71] Y.-G. Zou and Q.-L. Fan, “Oq-quad: An efficient query processing for continuous k-nearest neighbor
based on quad tree,” in Proceedings of 4th International Conference on Computer Science Education
(ICCSE ’09), pp. 197–202, July 2009.
[72] K. Mouratidis, M. L. Yiu, D. Papadias, and N. Mamoulis, “Continuous nearest neighbor monitoring
in road networks,” in Proc. VLDB, pp. 43–54, VLDB Endowment, 2006.
[73] F. Liu, K. Hua, and T. Do, “A p2p technique for continuous k-nearest-neighbor query in road networks,”
in Database and Expert Systems Applications, vol. 4653 of Lecture Notes in Computer Science,
pp. 264–276, Springer Berlin Heidelberg, 2007.
[74] Y.-K. Huang, Z.-W. Chen, and C. Lee, “Continuous k-nearest neighbor query over moving objects in
road networks,” Advances in Data and Web Management, pp. 27–38, 2009.
[75] G. Li, P. Fan, Y. Li, and J. Du, “An efficient technique for continuous k-nearest neighbor query processing
on moving objects in a road network,” in Proceedings of 2010 IEEE 10th International Conference
on Computer and Information Technology (CIT), pp. 627–634, June 2010.
[76] G. Li, Y. Li, L. Shu, and P. Fan, “Cknn query processing over moving objects with uncertain speeds
in road networks,” in Proceedings of Asia-Pacific Web Conference on Web Technologies and Applications,
APWeb’11, (Berlin, Heidelberg), pp. 65–76, Springer-Verlag, 2011.
[77] ZJ Haas, MR Pearlman, and P. Samar, “The Zone Routing Protocol (ZRP) for Ad Hoc Networks,”
Internet draft, Mobile Ad hoc Networking (MANET) Working Group of the Internet Engineering Task
Force (IETF), 1997.
[78] “The network simulator - ns-2.” http://www.isi.edu/nsnam/ns/.
[79] George Zipf, “Human Behavior and the Principle of Least Effort,” Addison Wesley, 1949.
[80] L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker, “Web caching and zipf-like distributions:
evidence and implications,” in Proc. IEEE INFOCOM ’99., vol. 1, pp. 126–134 vol.1, 1999.
[81] “Digital’s web proxy traces: Currently available traces v1.2 format,” 2013. ftp://ftp.hpl.hp.
com/pub/dec/traces/proxy/tracelistv1.2.html.
[82] X. Hong, K. Xu, and M. Gerla, “Scalable routing protocols for mobile ad hoc networks,” IEEE Network,
vol. 16, no. 4, pp. 11–21, 2002.
[83] R. Finkel and J. Bentley, “Quad trees a data structure for retrieval on composite keys,” Acta Informatica,
vol. 4, no. 1, pp. 1–9, 1974.
[84] H. Samet, “The quadtree and related hierarchical data structures,” ACM Comput. Surv., vol. 16,
pp. 187–260, jun 1984.
[85] H. Samet, “Neighbor finding techniques for images represented by quadtrees,” Computer Graphics
and Image Processing, vol. 18, no. 1, pp. 37 – 57, 1982.
[86] I. Stanoi, D. Agrawal, and A. E. Abbadi, “Reverse nearest neighbor queries for dynamic databases,”
in Proceedings of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery,
pp. 44–53, 2000.
[87] Y. Tao, D. Papadias, and X. Lian, “Reverse knn search in arbitrary dimensionality,” in Proc. VLDB,
VLDB ’04, pp. 744–755, VLDB Endowment, 2004.
[88] W. Wu, F. Yang, C.-Y. Chan, and K.-L. Tan, “Continuous reverse k-nearest-neighbor monitoring,” in
Proc. MDM, pp. 132–139, April 2008.
[89] M. A. Cheema, W. Zhang, X. Lin, Y. Zhang, and X. Li, “Continuous reverse k nearest neighbors
queries in euclidean space and in spatial networks,” The VLDB Journal, vol. 21, no. 1, pp. 69–95,
2012.
[90] H. Samet, Foundations of Multidimensional and Metric Data Structures. San Francisco, CA, USA:
Morgan Kaufmann Publishers Inc., 2006.
[91] S. V. Pemmaraju and C. A. Shaffer, “Analysis of the worst case space complexity of a pr quadtree,”
tech. rep., Virginia Polytechnic Institute & State University, Blacksburg, VA, USA, 1992.
[92] T. Brinkhoff, “A framework for generating network-based moving objects,” GeoInformatica, vol. 6,
no. 2, pp. 153–180, 2002.
[93] J. Harri, M. Fiore, F. Filali, and C. Bonnet, “Vehicular mobility simulation with vanetmobisim,” SIMULATION,
vol. 87, no. 4, pp. 275–300, 2011.
[94] “U.s. census bureau, tiger system.” http://www.census.gov/geo/maps-data/data/tiger.
html. [Online; accessed 10-June-2014].
[95] E. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, vol. 1,
no. 1, pp. 269–271, 1959.
[96] R. B. Dial, “Algorithm 360: Shortest-path forest with topological ordering [h],” Commun. ACM,
vol. 12, pp. 632–633, Nov. 1969.
[97] D. Papadias, J. Zhang, N. Mamoulis, and Y. Tao, “Query processing in spatial network databases,” in
Proceedings of the 29th International Conference on Very Large Data Bases - Volume 29, VLDB ’03,
pp. 802–813, VLDB Endowment, 2003.