article

Energy-efficient opportunistic coverage for people-centric urban sensing

Authors:

Liang LiuAuthors Info & Claims

Wireless Networks, Volume 20, Issue 6

Pages 1461 - 1476

https://doi.org/10.1007/s11276-014-0687-0

Published: 01 August 2014 Publication History

Abstract

Human-carried or vehicle-mounted sensors can be exploited to collect data ubiquitously for urban sensing. In this work, we study a new coverage problem, opportunistic coverage, to characterize the sensing quality of such people-centric sensing systems. Compared with the traditional static coverage and dynamic coverage in sensor networks, opportunistic coverage has some unique characteristics caused by the requirements of urban sensing applications and human mobility features such as spatio-temporal correlation, hotspots effects and randomness. In order to achieve good trade-off between energy consumption and coverage quality, we propose an offline node selection mechanism and an online adaptive sampling mechanism. The former can select the minimum number of nodes to achieve coverage requirements, based on the history trajectories of the given set of nodes, and the latter can help each selected node to decide whether to perform the sampling task at some time adaptively. Based on a real human mobility dataset and a taxi mobility dataset, extensive simulation results evaluate that our proposed models and mechanisms are effective and efficient in terms of energy consumption and coverage quality.

References

[1]

Ahmed, A., Yasumoto, K., Yamauchi, Y., & Ito, M. (2011). Distance and time based node selection for probabilistic coverage in people-centric sensing. In Proceedings of IEEE SECON, pp. 134---142.

[2]

Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., & Srivastava, M. (2006). Participatory sensing. In Workshop on World-Sensor-Web, co-located with ACM SenSys.

[3]

Campbell, A., Eisenman, S., Lane, N., Miluzzo, E., & Peterson, R. (2006). People-centric urban sensing. In Second ACM/IEEE International Conference on Wireless Internet (WiCon), pp. 18---31.

Digital Library

[4]

Campbell, A., Eisenman, S., Lane, N., Miluzzo, E., Peterson, R., Lu, H., Zheng, X., Musolesi, M., Fodor, K., Ahn, G.et al. (2008). The rise of people-centric sensing. IEEE Internet Computing, 12(4), 12---21.

Digital Library

[5]

Cardei, M., & Wu, J. (2006). Energy-efficient coverage problems in wireless ad-hoc sensor networks. Computer communications, 29(4), 413---420.

Digital Library

[6]

CENS/UCLA: Participatory sensing/ urban sensing projects. http://research.cens.ucla.edu/.

[7]

Chvatal, V. (1979). A greedy heuristic for the set-covering problem. Mathematics of Operations Research, 4(3), 233---235.

Digital Library

[8]

Cuff, D., Hansen, M., & Kang, J. (2008). Urban sensing: Out of the woods. Communications of the ACM, 51(3), 24---33.

Digital Library

[9]

Dutta, P., Aoki, P., Kumar, N., Mainwaring, A., Myers, C., Willett, W., & Woodruff, A. (2009). Common sense: Participatory urban sensing using a network of handheld air quality monitors. In Proceedings of ACM SenSys, pp. 349---350.

Digital Library

[10]

Eisenman, S. (2008). People-centric mobile sensing networks. Ph.D. thesis, Columbia University.

Digital Library

[11]

Eisenman, S., Lane, N., & Campbell, A. (2008). Techniques for improving opportunistic sensor networking performance. In Proceedings of IEEE DCOSS, pp. 157---175.

Digital Library

[12]

Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., & Balakrishnan, H. (2008). The pothole patrol: using a mobile sensor network for road surface monitoring. In Proceedings of ACM MobiSys.

Digital Library

[13]

Ganti, R., Pham, N., Ahmadi, H., Nangia, S., & Abdelzaher, T. (2010). Greengps: A participatory sensing fuel-efficient maps application. In Proceedings of ACM MobiSys, pp. 151---164.

Digital Library

[14]

Ghosh, A., & Das, S. (2008). Coverage and connectivity issues in wireless sensor networks: A survey. Pervasive and Mobile Computing, 4(3), 303---334.

Digital Library

[15]

Hefeeda, M., & Bagheri, M. (2007). Randomized k-coverage algorithms for dense sensor networks. In Proceedings of IEEE INFOCOM, pp. 2376---2380.

Digital Library

[16]

Hsu, W., Dutta, D., & Helmy, A. (2012). Csi: A paradigm for behavior-oriented profile-cast services in mobile networks. Ad Hoc Networks, 10(8), 1586---1602.

Digital Library

[17]

Hu, S., Wang, Y., Huang, C., & Tseng, Y. (2009). A vehicular wireless sensor network for CO2 monitoring. In Proceedings of IEEE Sensors, pp. 1498---1501.

[18]

Hull, B., Bychkovsky, V., Zhang, Y., Chen, K., Goraczko, M., Miu, A., et al. (2006). Cartel: A distributed mobile sensor computing system. In Proceedings of ACM SenSys, pp. 125---138.

Digital Library

[19]

Kumar, S., Lai, T., & Balogh, J. (2004). On k-coverage in a mostly sleeping sensor network. In Proceedings of ACM MobiCom, pp. 144---158.

Digital Library

[20]

Lane, N., Eisenman, S., Musolesi, M., Miluzzo, E., & Campbell, A. (2008). Urban sensing systems: Opportunistic or participatory? In Proceedings of the 9th ACM workshop on mobile computing systems and applications, pp. 11---16.

Digital Library

[21]

Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140---150.

Digital Library

[22]

Lee, Y., Ju, Y., Min, C., Kang, S., Hwang, I., & Song, J. (2012). Comon: Cooperative ambience monitoring platform with continuity and benefit awareness. In Proceedings of ACM MobiSys, pp. 43---56.

Digital Library

[23]

Li, M., Cheng, W., Liu, K., Liu, Y., Li, X., & Liao, X. (2011). Sweep coverage with mobile sensors. IEEE Transactions on Mobile Computing, 10(11), 1534---1545.

Digital Library

[24]

Liu, B., Brass, P., Dousse, O., Nain, P., & Towsley, D. (2005). Mobility improves coverage of sensor networks. In Proceedings of IEEE ACM MobiHoc, pp. 300---308.

Digital Library

[25]

Meguerdichian, S., Koushanfar, F., Potkonjak, M., & Srivastava, M. (2001). Coverage problems in wireless ad hoc sensor networks. In Proceedings of IEEE INFOCOM, pp. 1380---1387.

[26]

Rana, R., Chou, C., Kanhere, S., Bulusu, N., & Hu, W. (2010). Ear-phone: An end-to-end participatory urban noise mapping system. In Proceedings of ACM/IEEE IPSN, pp. 105---116.

Digital Library

[27]

Rhee, I., Shin, M., Hong, S., Lee, K., & Chong, S. (2008). On the levy walk nature of human mobility. In Proceedings of IEEE INFOCOM, pp. 924---932.

[28]

Scellato, S., Musolesi, M., Mascolo, C., Latora, V., & Campbell, A. T. (2011). Nextplace: A spatio-temporal prediction framework for pervasive systems. In The 9th International Conference on Pervasive Computing, pp. 152---169.

Digital Library

[29]

Shin, M., Tsang, P., Kotz, D., & Cornelius, C. (2009) Deamon: Energy-efficient sensor monitoring. In Proceedings of IEEE SECON, pp. 1---9.

Digital Library

[30]

Srinivasan, A. (1995). Improved approximations of packing and covering problems. In Proceedings of the twenty-seventh annual ACM symposium on Theory of computing, pp. 268---276.

Digital Library

[31]

Srivastava, M., Abdelzaher, T., & Szymanski, B. (2012). Human-centric sensing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 370(1958), 176---197.

[32]

Wang, W., Srinivasan, V., & Motani, M. (2007). Adaptive contact probing mechanisms for delay tolerant applications. In Proceedings of ACM MobiCom, pp. 230---241.

Digital Library

[33]

Wang, X., Wang, X., & Zhao, J. (2011). Impact of mobility and heterogeneity on coverage and energy consumption in wireless sensor networks. In Proceedings of IEEE ICDCS.

Digital Library

[34]

Wang, Y., & Wu, H. (2007). Delay/fault-tolerant mobile sensor network (dft-msn): A new paradigm for pervasive information gathering. IEEE Transactions on Mobile Computing, 6(9), 1021---1034.

Digital Library

[35]

Wimalajeewa, T., & Jayaweera, S. (2010). Impact of mobile node density on detection performance measures in a hybrid sensor network. IEEE Transactions on Wireless Communications, 9(5), 1760---1769.

Digital Library

[36]

Yang, D., Xue, G., Fang, X., & Tang, J. (2012). Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing. In Proceedings of ACM MobiCom.

Digital Library

[37]

Zhao, D., Li, X. Y., & Ma, H. (2014). How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint. In Proceedings of IEEE INFOCOM.

[38]

Zhao, D., Ma, H., & Liu, L. (2012) Mobile sensor scheduling for timely sweep coverage. In Proceedings of IEEE WCNC, pp. 1771---1776.

[39]

Zheng, Y., Liu, Y., Yuan, J., & Xie, X. (2011). Urban computing with taxicabs. In Proceedings of ACM Ubicomp.

Digital Library

Cited By

Wang LYu ZZhang DGuo BLiu C(2018)Heterogeneous Multi-Task Assignment in Mobile Crowdsensing Using Spatiotemporal CorrelationIEEE Transactions on Mobile Computing10.1109/TMC.2018.282737518:1(84-97)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.1109/TMC.2018.2827375
Zhao DMa HLi QTang S(2018)A unified delay analysis framework for opportunistic data collectionWireless Networks10.1007/s11276-016-1403-z24:4(1313-1325)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s11276-016-1403-z
Corrente G(2017)Performance issues in content dissemination to metropolitan mobile usersJournal of High Speed Networks10.3233/JHS-17056223:2(149-162)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/JHS-170562
Show More Cited By

Energy-efficient opportunistic coverage for people-centric urban sensing
1. Networks
  1. Network types

Recommendations

On Opportunistic Coverage for Urban Sensing
MASS '13: Proceedings of the 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems

Opportunistic sensing is a new paradigm which exploits human-carried or vehicle-mounted sensors to collect data ubiquitously for large-scale urban sensing. Existing work lacks an in-depth investigation on the sensing quality of such sensing systems, ...
People-centric urban sensing
WICON '06: Proceedings of the 2nd annual international workshop on Wireless internet

The vast majority of advances in sensor network research over the last five years have focused on the development of a series of small-scale (100s of nodes) testbeds and specialized applications (e.g., environmental monitoring, etc.) that are built on ...
Opportunistic coverage for urban vehicular sensing

Opportunistic vehicular sensing is a new paradigm which exploits variety of sensors embedded in vehicles or smartphones to collect data ubiquitously for large-scale urban sensing. Existing work lacks in-depth investigations on the coverage problem in ...

Comments

Information & Contributors

Information

Published In

cover image Wireless Networks

Wireless Networks Volume 20, Issue 6

August 2014

416 pages

ISSN:1022-0038

Issue’s Table of Contents

Copyright © Copyright © 2014 Springer Science+Business Media New York.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 August 2014

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang LYu ZZhang DGuo BLiu C(2018)Heterogeneous Multi-Task Assignment in Mobile Crowdsensing Using Spatiotemporal CorrelationIEEE Transactions on Mobile Computing10.1109/TMC.2018.282737518:1(84-97)Online publication date: 3-Dec-2018
https://dl.acm.org/doi/10.1109/TMC.2018.2827375
Zhao DMa HLi QTang S(2018)A unified delay analysis framework for opportunistic data collectionWireless Networks10.1007/s11276-016-1403-z24:4(1313-1325)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s11276-016-1403-z
Corrente G(2017)Performance issues in content dissemination to metropolitan mobile usersJournal of High Speed Networks10.3233/JHS-17056223:2(149-162)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/JHS-170562
Chen YLv PGuo DZhou TXu M(2017)Trajectory segment selection with limited budget in mobile crowd sensingPervasive and Mobile Computing10.1016/j.pmcj.2017.06.01040:C(123-138)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.pmcj.2017.06.010
Wu HMa HLiu LMa HYuan P(2017)A traffic-camera assisted cache-and-relay routing for live video stream delivery in vehicular ad hoc networksWireless Networks10.1007/s11276-016-1272-523:7(2051-2067)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s11276-016-1272-5
Ding SHe XWang JQiao BGai K(2017)Static Node Center Opportunistic Coverage and Hexagonal Deployment in Hybrid Crowd SensingJournal of Signal Processing Systems10.1007/s11265-016-1120-y86:2-3(251-267)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s11265-016-1120-y
Leppänen TÁlvarez Lacasia JTobe YSezaki KRiekki J(2017)Mobile crowdsensing with mobile agentsAutonomous Agents and Multi-Agent Systems10.1007/s10458-015-9311-731:1(1-35)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s10458-015-9311-7
Wang YLi HLi T(2017)Participant selection for data collection through device-to-device communications in mobile sensingPersonal and Ubiquitous Computing10.1007/s00779-016-0974-021:1(31-41)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s00779-016-0974-0
Bradai SKhemakhem SJmaiel M(2016)Web Services Description and Discovery for Mobile CrowdsensingInternational Journal of Information System Modeling and Design10.4018/IJISMD.20161001037:4(31-49)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.4018/IJISMD.2016100103
Wennerström HRohner CBoldrini Cde Amorim M(2016)Towards even coverage monitoring with opportunistic sensor networksProceedings of the Eleventh ACM Workshop on Challenged Networks10.1145/2979683.2979691(81-86)Online publication date: 3-Oct-2016
https://dl.acm.org/doi/10.1145/2979683.2979691
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents