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Data stashing: energy-efficient information delivery to mobile sinks through trajectory prediction

Published: 12 April 2010 Publication History

Abstract

In this paper, we present a routing scheme that exploits knowledge about the behavior of mobile sinks within a network of data sources to minimize energy consumption and network congestion. For delay-tolerant network applications, we propose to route data not to the sink directly, but to send it instead to a relay node along an announced or predicted path of the mobile node that is close to the data source. The relay node will stash the information until the mobile node passes by and picks up the data. We use linear programming to find optimal relay nodes that minimize the number of necessary transmissions while guaranteeing robustness against link and node failures, as well as trajectory uncertainty.
We show that this technique can drastically reduce the number of transmissions necessary to deliver data to mobile sinks. We derive mobility and association models from real-world data traces and evaluate our data stashing technique in simulations. We examine the influence of uncertainty in the trajectory prediction on the performance and robustness of the routing scheme.

References

[1]
TinyOS 2.1.0. http://www.tinyos.net/tinyos-2.1.0/.
[2]
A. Agrawal and S. K. Khaitan. A new heuristic for multiple sequence alignment. In Proceedings of the IEEE International Conference Electro/Information Technology, 2008.
[3]
D. Ashbrook and T. Starner. Using gps to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, Jan 2003.
[4]
N. Banerjee, M. D. Corner, D. Towsley, and B. N. Levine. Relays, base stations, and meshes: enhancing mobile networks with infrastructure. In MobiCom '08: Proceedings of the 14th ACM international conference on Mobile computing and networking, pages 81--91, New York, NY, USA, 2008. ACM.
[5]
M. Bayir, M. Demirbas, and N. Eagle. Mobility profiler: A framework for discovering mobile user profiles (technical report version). cse.buffalo.edu, 2008.
[6]
A. Chakrabarti, A. Sabharwal, and B. Aazhang. Using predictable observer mobility for power efficient design of sensor networks. In IPSN '03: Proceedings of the 2nd International Workshop on Information Processing in Sensor Networks, Palo Alto, CA, USA, 2003.
[7]
J.-H. Chang and L. Tassiulas. Maximum lifetime routing in wireless sensor networks. IEEE/ACM Trans. Netw., 12(4):609--619, 2004.
[8]
D. S. J. De Couto, D. Aguayo, J. Bicket, and R. Morris. A high-throughput path metric for multi-hop wireless routing. In MobiCom '03: Proceedings of the 9th annual international conference on Mobile computing and networking, pages 134--146, New York, NY, USA, 2003. ACM.
[9]
J. Froehlich and J. Krumm. Route prediction from trip observations. SAE SP, Jan 2008.
[10]
S. Gandham, M. Dawande, R. Prakash, and Subbarayan. Energy efficient schemes for wireless sensor networks with multiple mobile base stations. In GlobeCom '03: Proceedings of the Global Communications Conference, San Francisco, CA, USA, 2003.
[11]
J. Ghosh, M. Beal, H. Ngo, and C. Qiao. On profiling mobility and predicting locations of campus-wide wireless network users. Technical Report: State University of New York at Buffalo, Jan 2005.
[12]
O. Gnawali, R. Fonseca, K. Jamieson, D. Moss, and P. Levis. Collection Tree Protocol. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (SenSys'09), November 2009.
[13]
A. Goldsmith. Wireless Communications. Cambridge University Press, New York, NY, USA, 2005.
[14]
D. Johnson, D. Maltz, and J. Broch. DSR: The dynamic source routing protocol for multihop wireless ad hoc networks. In Ad Hoc Networking, 2001.
[15]
S. Johnson. Hierarchical clustering schemes. Psychometrika, 32(3):241--254, September 1967.
[16]
H. S. Kim, T. F. Abdelzaher, and W. H. Kwon. Minimum-energy asynchronous dissemination to mobile sinks in wireless sensor networks. In SenSys '03: Proceedings of the 1st international conference on Embedded networked sensor systems, pages 193--204, New York, NY, USA, 2003. ACM.
[17]
D. Kotz, T. Henderson, and I. Abyzov. CRAWDAD data set dartmouth/campus (v. 2004-12-18). Downloaded from http://www.crawdad.org/dartmouth/campus, Dec. 2004.
[18]
J. Krumm. Real time destination prediction based on efficient routes. Society of Automotive Engineers (SAE) 2006 World Congress, Jan 2006.
[19]
B. Kusy, H. Lee, M. Wicke, N. Milosavljevic, and L. Guibas. Predictive qos routing to mobile sinks in wireless sensor networks. In IPSN '09: Proceedings of the 2009 International Conference on Information Processing in Sensor Networks, pages 109--120, Washington, DC, USA, 2009. IEEE Computer Society.
[20]
K. Laasonen. Clustering and prediction of mobile user routes from cellular data. LECTURE NOTES IN COMPUTER SCIENCE, Jan 2005.
[21]
K. Laasonen, M. Raento, and H. Toivonen. Adaptive on-device location recognition. LECTURE NOTES IN COMPUTER SCIENCE, Jan 2004.
[22]
H. Lee, A. Cerpa, and P. Levis. Improving wireless simulation through noise modeling. In IPSN '07: Proceedings of the 6th international conference on Information processing in sensor networks, pages 21--30, New York, NY, USA, 2007. ACM Press.
[23]
P. Levis, N. Lee, M. Welsh, and D. Culler. TOSSIM: Simulating large wireless sensor networks of tinyos motes. In Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003.
[24]
Y. Li, J. Harms, and R. Holte. Optimal traffic-oblivious energy-aware routing for multihop wireless networks. In INFOCOM '06: Proceedings of the 26th Conference on Computer Communications, Barcelona, Spain, 2006.
[25]
L. Liao, D. Fox, and H. Kautz. Extracting places and activities from gps traces using hierarchical conditional random fields. The International Journal of Robotics Research, Jan 2007.
[26]
L. Liao, D. Patterson, D. Fox, and H. Kautz. Learning and inferring transportation routines. Artificial Intelligence, Jan 2007.
[27]
L. Lin, N. B. Shroff, and R. Srikant. Asymptotically optimal energy-aware routing for multihop wireless networks with renewable energy sources. IEEE/ACM Trans. Netw., 15(5):1021--1034, 2007.
[28]
J. Luo and J.-P. Hubaux. Joint mobility and routing for lifetime elongation in wireless sensor networks. In INFOCOM '05: Proceedings of the 25th Conference on Computer Communications, Miami, FL, USA, 2005.
[29]
J. Luo, J. Panchard, M. Piorkowski, M. Grossglauser, and J.-P. Hubaux. Mobiroute: Routing towards a mobile sink for improving lifetime in sensor networks. In DCOSS '06: Proceedings of the International Conference on Distributed Computing in Sensor Systems, San Francisco, CA, USA, 2006.
[30]
Y. Mao, F. Wang, L. Qiu, S. S. Lam, and J. M. Smith. S4: Small state and small stretch routing protocol for large wireless sensor networks. In 4th Symposium on Networked Systems Design and Implementation (NSDI 2007), 2007.
[31]
C. Notredame. Recent progress in multiple sequence alignment: a survey. Pharmacogenomics, 3(1):131--144, January 2002.
[32]
P. Nurmi and J. Koolwaaij. Identifying meaningful locations. Mobile and Ubiquitous Systems: Networking & Services, 2006 Third Annual International Conference on, pages 1--8, Jul 2006.
[33]
C. E. Perkins, E. M. Belding-Royer, and S. Das. Ad hoc on demand distance vector (AODV) routing. IETF Internet draft, draft-ietf-manet-aodv-09.txt, November 2001 (Work in Progress).
[34]
R. C. Shah, S. Roy, S. Jain, and W. Brunette. Data mules: Modeling a three-tier architecture for sparse sensor networks. In IEEE SNPA Workshop, pages 30--41, 2003.
[35]
T. F. Smith and M. S. Waterman. Identification of common molecular subsequences. Journal of Molecular Biology, 147(1):195--197, March 1981.
[36]
L. Song, U. Deshpande, U. Kozat, D. Kotz, and R. Jain. Predictability of wlan mobility and its effects on bandwidth provisioning. INFOCOM 2006. 25th IEEE International Conference on Computer Communications. Proceedings, pages 1--13, Apr 2006.
[37]
L. Song, D. Kotz, R. Jain, and X. He. Evaluating location predictors with extensive wi-fi mobility data. INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, 2:1414--1424 vol.2, Feb 2004.
[38]
J. D. Thompson, D. G. Higgins, and T. J. Gibson. Clustal w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res, 22(22):4673--4680, November 1994.
[39]
L. Wang and T. Jiang. On the complexity of multiple sequence alignment. Journal of Computational Biology, 1(4):337--348, 1994.
[40]
R. Wohlers, N. Trigoni, R. Zhang, and S. Ellwood. Twinroute: Energy-efficient data collection in fixed sensor networks with mobile sinks. In MDM '09: Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pages 192--201, Washington, DC, USA, 2009. IEEE Computer Society.
[41]
F. Ye, H. Luo, J. Cheng, S. Lu, and L. Zhang. A two-tier data dissemination model for large-scale wireless sensor networks. In MobiCom '02: Proceedings of the 8th annual international conference on Mobile computing and networking, pages 148--159, New York, NY, USA, 2002. ACM.
[42]
J. Yin, Q. Yang, D. Shen, and Z.-N. Li. Activity recognition via user-trace segmentation. Transactions on Sensor Networks (TOSN), 4(4), Aug 2008.

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cover image ACM Conferences
IPSN '10: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
April 2010
460 pages
ISBN:9781605589886
DOI:10.1145/1791212
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Publication History

Published: 12 April 2010

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Author Tags

  1. mobile data delivery
  2. mobility pattern
  3. network optimization
  4. sensor networks
  5. trajectory prediction

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Overall Acceptance Rate 143 of 593 submissions, 24%

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  • (2020)Performance Assessment of the Fixed Node Assisted Collection Tree Protocol (FNA-CTP) in a Mobile EnvironmentHandbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's10.1007/978-3-030-40305-8_8(134-154)Online publication date: 9-Feb-2020
  • (2019)An Analysis of the Directional Preference ETX Measure for the Collection Tree Protocol in Mobile Sensor NetworksProcedia Computer Science10.1016/j.procs.2019.08.050155(351-359)Online publication date: 2019
  • (2018)Delay-Aware Energy-Efficient Routing towards a Path-Fixed Mobile Sink in Industrial Wireless Sensor NetworksSensors10.3390/s1803089918:3(899)Online publication date: 18-Mar-2018
  • (2018)Ubiquitous Transmission of Multimedia Sensor Data in Internet of ThingsIEEE Internet of Things Journal10.1109/JIOT.2017.27627315:1(403-414)Online publication date: Feb-2018
  • (2017)A New Energy Efficient Clustering Algorithm Based on Routing Spanning Tree for Wireless Sensor NetworkIEICE Transactions on Communications10.1587/transcom.2016EBP3487E100.B:12(2110-2120)Online publication date: 2017
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  • (2017)HHSRP: a cluster based hybrid hierarchical secure routing protocol for wireless sensor networksCluster Computing10.1007/s10586-017-1065-3Online publication date: 29-Jul-2017
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