TRack others if you can: localized proximity detection for mobile networks

For a set of mobile users with designated friendship relations, it is a recurring issue to keep track of whether some friends appear in the vicinity of a given user. While both distributed and centralized solutions for proximity detection have been proposed, the cost metrics for evaluating these pro...

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Vydáno v:	Wireless networks Ročník 20; číslo 6; s. 1477 - 1494
Hlavní autoři:	Zhang, Chi, Luo, Jun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Boston Springer US 01.08.2014 Springer Springer Nature B.V
Témata:	Algorithms Analysis Applied sciences Communication Communications Engineering Computer Communication Networks Computer science; control theory; systems Computer systems and distributed systems. User interface Electrical Engineering Energy consumption Energy efficiency Engineering Equipments and installations Exact sciences and technology Exchange Friendship Information retrieval. Graph IT in Business Mathematical analysis Message passing Messages Mobile radiocommunication systems Networks Proposals Proximity Radiocommunications Sensors Social networks Software Studies Telecommunications Telecommunications and information theory Tessellation Theoretical computing Wireless networks Proximity detection Mobile networks Energy efficiency Spatial tessellation Energy consumption Proximity Updating Distributed system Energy savings Friendship Message passing Graph connectivity Efficiency Wireless network Metric Tiling Graph method Counting Mobile computing
ISSN:	1022-0038, 1572-8196
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Abstract	For a set of mobile users with designated friendship relations, it is a recurring issue to keep track of whether some friends appear in the vicinity of a given user. While both distributed and centralized solutions for proximity detection have been proposed, the cost metrics for evaluating these proposals are always based on counting the number of message (e.g., query or update) exchanges. However, as mobile users often rely on wireless networks to maintain their connectivity, the cost incurred by any message passing is strongly affected by the distance between the sender and receiver. In this paper, we propose TRack Others if You can (TROY) as a novel distributed solution for proximity detection. Extending the principle of spatial tessellations, TROY incurs only localized message exchanges and is thus superior to existing proposals in terms of more realistic cost metrics that take into account the actual energy consumption of message passing. Moreover, our spatial tessellations inspired analytical framework allows for a meaningful comparison with an existing work. Finally, we use extensive experiments to validate the efficiency of TROY.
AbstractList	For a set of mobile users with designated friendship relations, it is a recurring issue to keep track of whether some friends appear in the vicinity of a given user. While both distributed and centralized solutions for proximity detection have been proposed, the cost metrics for evaluating these proposals are always based on counting the number of message (e.g., query or update) exchanges. However, as mobile users often rely on wireless networks to maintain their connectivity, the cost incurred by any message passing is strongly affected by the distance between the sender and receiver. In this paper, we propose TRack Others if You can (TROY) as a novel distributed solution for proximity detection. Extending the principle of spatial tessellations, TROY incurs only localized message exchanges and is thus superior to existing proposals in terms of more realistic cost metrics that take into account the actual energy consumption of message passing. Moreover, our spatial tessellations inspired analytical framework allows for a meaningful comparison with an existing work. Finally, we use extensive experiments to validate the efficiency of TROY.[PUBLICATION ABSTRACT] For a set of mobile users with designated friendship relations, it is a recurring issue to keep track of whether some friends appear in the vicinity of a given user. While both distributed and centralized solutions for proximity detection have been proposed, the cost metrics for evaluating these proposals are always based on counting the number of message (e.g., query or update) exchanges. However, as mobile users often rely on wireless networks to maintain their connectivity, the cost incurred by any message passing is strongly affected by the distance between the sender and receiver. In this paper, we propose TRack Others if You can (TROY) as a novel distributed solution for proximity detection. Extending the principle of spatial tessellations, TROY incurs only localized message exchanges and is thus superior to existing proposals in terms of more realistic cost metrics that take into account the actual energy consumption of message passing. Moreover, our spatial tessellations inspired analytical framework allows for a meaningful comparison with an existing work. Finally, we use extensive experiments to validate the efficiency of TROY. For a set of mobile users with designated friendship relations, it is a recurring issue to keep track of whether some friends appear in the vicinity of a given user. While both distributed and centralized solutions for proximity detection have been proposed, the cost metrics for evaluating these proposals are always based on counting the number of message (e.g., query or update) exchanges. However, as mobile users often rely on wireless networks to maintain their connectivity, the cost incurred by any message passing is strongly affected by the distance between the sender and receiver. In this paper, we propose TRack Others if You can (TROY) as a novel distributed solution for proximity detection. Extending the principle of spatial tessellations, TROY incurs only localized message exchanges and is thus superior to existing proposals in terms of more realistic cost metrics that take into account the actual energy consumption of message passing. Moreover, our spatial tessellations inspired analytical framework allows for a meaningful comparison with an existing work. Finally, we use extensive experiments to validate the efficiency of TROY.
Author	Luo, Jun Zhang, Chi
Author_xml	– sequence: 1 givenname: Chi surname: Zhang fullname: Zhang, Chi organization: School of Computer Engineering, Nanyang Technological University – sequence: 2 givenname: Jun surname: Luo fullname: Luo, Jun email: junluo@ntu.edu.sg organization: School of Computer Engineering, Nanyang Technological University
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Keywords	Proximity detection Mobile networks Energy efficiency Spatial tessellation Energy consumption Proximity Updating Distributed system Energy savings Friendship Message passing Graph connectivity Efficiency Wireless network Metric Tiling Graph method Counting Mobile computing
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References	HyytiaE.LassilaP.VirtamoJ.Spatial node distribution of the random waypoint mobility model with applicationsIEEE Transactions on Mobile Computing20065668069410.1109/TMC.2006.86 Mouratidis, K., Papadias, D., & Hadjieleftheriou, M. (2005). Conceptual partitioning: An efficient method for continuous nearest neighbor monitoring. In Proceedings of the ACM SIGMOD, pp. 634–645. Yu, X., Pu, K. Q., & Koudas, N. (2005). Monitoring k-nearest neighbor queries over moving objects. In Proceedings of the 21st ICDE, pp. 631–642. BrinkhoffT.StrO.A framework for generating network-based moving objectsGeoinformatica2002620210.1023/A:1015231126594 OkabeA.BootsB.SugiharaK.ChuiS.Spatial tessellations: concepts and applications of voronoi diagrams, 2 ed2000ChichesterWiley10.1002/9780470317013 IwerksG. S.SametH.SmithK. P.Maintenance of k-nn and spatial join queries on continuously moving pointsACM Transactions on Database System20063148553610.1145/1138394.1138396 Rahmati, A., & Zhong, L. (2007). Context-for-wireless: Context-sensitive energy-efficient wireless data transfer. In Proceedings of the 7th ACM/USENIX MobiSys. GedikB.LiuL.Mobieyes: A distributed location monitoring service using moving location queriesIEEE Transactions on Mobile Computing200651384140210.1109/TMC.2006.153 Amir, A., Efrat, A., Myllymaki, J., Palaniappan, L., & Wampler, K. (2004). Buddy tracking—efficient proximity detection among mobile users. In Proceedings of the 23rd IEEE INFOCOM. Xu, Z., & Jacobsen, A. (2007). Adaptive location constraint processing. In Proceedings of the ACM SIGMOD, pp. 581–592. Cai, Y., Hua, K. A., & Cao, G. (2004). Processing range-monitoring queries on heterogeneous mobile objects. In Mobile data management, MDM, pp. 27–38. Wang, H., Zimmermann, R., & shinn Ku, W. (2006). Distributed continuous range query processing on moving objects. In DEXA, pp. 655–665. Hu, H., Xu, J., & Lee, D. L. (2005). A generic framework for monitoring continuous spatial queries over moving objects. In Proceedings of the ACM SIGMOD, pp. 479–490. Zhang, J., Zhu, M., Papadias, D., Tao, Y., & Lee, D.-L. (2003). Location-based spatial queries. In Proceedings of the 30th ACM SIGMOD. StoyanD.KendallW.MeckeJ.Stochasitc geormetry and its applications, 2nd ed.1995ChichesterWiley IlarriS.MenaE.IllarramendiA.Location-dependent queries in mobile contexts: Distributed processing using mobile agentsIEEE Transactions on Mobile Computing2006581029104310.1109/TMC.2006.118 MouratidisK.PapadiasD.BakirasS.TaoY.A threshold-based algorithm for continuous monitoring of k nearest neighborsIEEE Transaction on Knowledge and Data Engineering2005171451146410.1109/TKDE.2005.172 NewmanM.Structure and function of complex networksSIAM Reviews200345216725610.1137/S0036144503424801029.68010 Zhang, R., Lin, D., Ramamohanarao, K., & Bertino, E. (2008). Continuous intersection joins over moving objects. In Proceedings of the 24th ICDE, pp. 863–872. StrogatzS.Exploring complex networksNature200142026827610.1038/35065725 RappaportT.Wireless communications: Principles and practice, 2 ed2002Upper Saddle RiverPrentice-Hall Inc Mokbel, M. F., Xiong, X., & Aref, W. G. (2004). Sina: Scalable incremental processing of continuous queries in spatio-temporal databases. In Proceedings of the ACM SIGMOD, pp. 623–634. Bash, B., & Desnoyers, P. (2007). Exact distributed Voronoi cell computation in sensor networks. In Proceedings of the 6th ACM/IEEE IPSN. Kolahdouzan, M., & Shahabi, C. (2004). Voronoi-based K nearest neighbor search for spatial network databases. In Proceedings of the 30th VLDB. Yiu, M.-L., Hou, H., Šaltenis, S., & Tzoumas, K. (2010). Efficient proximity detection among mobile users via self-tuning policies. In Proceedings of the 36th VLDB. Treu, G., Wilder, T., & Küpper, A. (2006). Efficient proximity detection among mobile targets with dead reckoning. In Proceedings of the 4th ACM MobiWAC. 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 the 21st international conference on data engineering, ICDE ’05, pp. 643–654. G. S. Iwerks (690_CR9) 2006; 31 690_CR16 690_CR11 690_CR10 690_CR13 T. Rappaport (690_CR17) 2002 690_CR6 690_CR2 690_CR1 690_CR4 690_CR20 A. Okabe (690_CR15) 2000 690_CR26 690_CR25 E. Hyytia (690_CR7) 2006; 5 S. Strogatz (690_CR19) 2001; 420 690_CR27 690_CR22 690_CR21 690_CR24 T. Brinkhoff (690_CR3) 2002; 6 690_CR23 S. Ilarri (690_CR8) 2006; 5 K. Mouratidis (690_CR12) 2005; 17 M. Newman (690_CR14) 2003; 45 B. Gedik (690_CR5) 2006; 5 D. Stoyan (690_CR18) 1995
References_xml	– reference: Bash, B., & Desnoyers, P. (2007). Exact distributed Voronoi cell computation in sensor networks. In Proceedings of the 6th ACM/IEEE IPSN. – reference: RappaportT.Wireless communications: Principles and practice, 2 ed2002Upper Saddle RiverPrentice-Hall Inc – reference: Hu, H., Xu, J., & Lee, D. L. (2005). A generic framework for monitoring continuous spatial queries over moving objects. In Proceedings of the ACM SIGMOD, pp. 479–490. – reference: Cai, Y., Hua, K. A., & Cao, G. (2004). Processing range-monitoring queries on heterogeneous mobile objects. In Mobile data management, MDM, pp. 27–38. – reference: Mokbel, M. F., Xiong, X., & Aref, W. G. (2004). Sina: Scalable incremental processing of continuous queries in spatio-temporal databases. In Proceedings of the ACM SIGMOD, pp. 623–634. – reference: Yiu, M.-L., Hou, H., Šaltenis, S., & Tzoumas, K. (2010). Efficient proximity detection among mobile users via self-tuning policies. In Proceedings of the 36th VLDB. – reference: Mouratidis, K., Papadias, D., & Hadjieleftheriou, M. (2005). Conceptual partitioning: An efficient method for continuous nearest neighbor monitoring. In Proceedings of the ACM SIGMOD, pp. 634–645. – reference: Yu, X., Pu, K. Q., & Koudas, N. (2005). Monitoring k-nearest neighbor queries over moving objects. In Proceedings of the 21st ICDE, pp. 631–642. – reference: BrinkhoffT.StrO.A framework for generating network-based moving objectsGeoinformatica2002620210.1023/A:1015231126594 – reference: IwerksG. S.SametH.SmithK. P.Maintenance of k-nn and spatial join queries on continuously moving pointsACM Transactions on Database System20063148553610.1145/1138394.1138396 – reference: Amir, A., Efrat, A., Myllymaki, J., Palaniappan, L., & Wampler, K. (2004). Buddy tracking—efficient proximity detection among mobile users. In Proceedings of the 23rd IEEE INFOCOM. – reference: IlarriS.MenaE.IllarramendiA.Location-dependent queries in mobile contexts: Distributed processing using mobile agentsIEEE Transactions on Mobile Computing2006581029104310.1109/TMC.2006.118 – reference: Zhang, J., Zhu, M., Papadias, D., Tao, Y., & Lee, D.-L. (2003). Location-based spatial queries. In Proceedings of the 30th ACM SIGMOD. – reference: NewmanM.Structure and function of complex networksSIAM Reviews200345216725610.1137/S0036144503424801029.68010 – reference: Kolahdouzan, M., & Shahabi, C. (2004). Voronoi-based K nearest neighbor search for spatial network databases. In Proceedings of the 30th VLDB. – reference: OkabeA.BootsB.SugiharaK.ChuiS.Spatial tessellations: concepts and applications of voronoi diagrams, 2 ed2000ChichesterWiley10.1002/9780470317013 – reference: StrogatzS.Exploring complex networksNature200142026827610.1038/35065725 – reference: Xu, Z., & Jacobsen, A. (2007). Adaptive location constraint processing. 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Title	TRack others if you can: localized proximity detection for mobile networks
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