Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Wireless Networks
Published in: Wireless Networks 5/2017

09-03-2016

Incentivize maximum continuous time interval coverage under budget constraint in mobile crowd sensing

Authors: Jia Xu, Jinxin Xiang, Yanxu Li

Published in: Wireless Networks | Issue 5/2017

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Mobile crowd sensing has become an effective approach to meet the demand in large scale sensing applications. In mobile crowd sensing applications, incentive mechanisms are necessary to compensate the resource consumptions and manual efforts of smartphone users. In this paper, we focus on exploring budget feasible frameworks for a novel and practical mobile crowd sensing scenario, where the platform expects to maximize the continuous time interval coverage under budget constraint. We present the system model and formulate the budget feasible maximum continuous time duration problem for this scenario. We design two budget feasible frameworks: BFF-STI and BFF-BTI, and integrate MST as the truthful mechanism to maximize the social efficiency. Then we extend the budget feasible frameworks to the general case, in which each user can bid multiple time intervals simultaneously. We show the proposed budget feasible frameworks are computationally efficient, individually rational, truthful and budget feasible. Through extensive simulations, we demonstrate that our budget feasible frameworks are efficient with different parameter settings. The simulation results also show that BFF-STI has superiority in large scale mobile crowd sensing applications, while BFF-STI is more suitable for long-term sensing applications.
previous article TCP-Gvegas with prediction and adaptation in multi-hop ad hoc networks
next article Stochastic approximation based on-line algorithm for fairness in multi-rate wireless LANs

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Ganti, R. K., Ye, F., & Lei, H. (2011). Mobile crowdsensing: Current state and future challenges. IEEE Communications Magazine, 49(11), 32–39.CrossRef
2.
Consolvo, S., et al. (2008). Activity sensing in the wild: A field trial of Ubifit Garden. In Proceedings of the 26th annual ACM SIGCHI conference on human factors in computer systems (pp. 1797–1806).
3.
Miluzzo, E., et al. (2008). Sensing meets mobile social networks: The design, implementation, and evaluation of the CenceMe Application. In Proceedings of the 6th ACM SenSys (pp. 337–350).
4.
Mun, M., et al. (2009). Peir, the personal environmental impact report, as a platform for participatory sensing systems research. In Proceedings of 7th ACM MobiSys (pp. 55–68).
5.
Thiagarajan, A., et al. (2009). VTrack: Accurate, energy-aware traffic delay estimation using mobile phones. In Proceedings of the 7th ACM SenSys (pp. 85–98).
6.
7.
Dong, W., Lepri, B., & Pentland, S. (2012). Tracking co-evolution of behavior and relationships with mobile phones. Tsinghua Science and Technology, 17(2), 136–151.CrossRef
8.
Koukoumidis, E., Peh, L., & Martonosi, M. (2011). SignalGuru: Leveraging mobile phones for collaborative traffic signal schedule advisory. In Proceedings of the ACM ninth international conference on mobile systems, applications, and services (MobiSys) (pp. 127–140).
9.
Costa, C., Laoudias, C., Zeinalipour-Yazti, D., & Gunopulos, D. (2011). Smarttrace: Finding similar trajectories in smartphone networks without disclosing the traces. In Proceedings of the IEEE 27th international conference on data engineering (ICDE) (pp. 1288–1291).
10.
Liu, Y., Zhao, Y., Chen, L., Pei, J., & Han, J. (2012). Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. IEEE Transactions on Parallel and Distributed Systems, 23(11), 2138–2149.CrossRef
11.
Yang, Z., Wu, C., & Liu, Y. (2012). Locating in fingerprint space: Wireless indoor localization with little human intervention. In Proceedings of the ACM MobiCom (pp. 269–280).
12.
Yang, D., Xue, G., Fang, X., & Tang, J. (2012). Crowdsourcing to Smartphones: Incentive mechanism design for mobile phone sensing. In Proceedings of the ACM MobiCom (pp. 173–184).
13.
Zhao, D., Li, X., & Ma, H. (2014). How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint. In Proceedings of the IEEE INFOCOM (pp. 1213–1221).
14.
Singer, Y., & Mittal, M. (2013). Pricing mechanisms for crowdsourcing markets. In Proceedings of the 22nd international conference on World Wide Web (pp. 1157–1166).
15.
Feng, Z., Zhu, Y., Zhang, Q., Ni, L. M., & Vasilakos, A.V. (2014). TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing. In Proceedings of the IEEE INFOCOM (pp. 1231–1239).
16.
Subramanian, A., Kanth, G. S., Moharir, S., & Vaze, R. (2015). Online incentive mechanism design for smartphone crowd-sourcing. In Proceedings of the WiOpt (pp. 403–410).
17.
Zhang, X., Yang, Z., Zhou, Z., Cai, H., Chen, L., & Li, X. (2014). Free market of crowdsourcing: incentive mechanism design for mobile sensing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3190–3200.CrossRef
18.
Singer, Y. (2010). Budget feasible mechanisms. In Proceedings of the IEEE FOCS (pp. 765–774).
19.
Koutsopoulos, I. (2013). Optimal incentive-driven design of participatory sensing systems. In Proceedings of the IEEE INFOCOM (pp. 1402–1410).
20.
Zhang, X., Xue, G., Yu, R., Yang, D., & Tang, J. (2015). Truthful incentive mechanisms for crowdsourcing. In Proceedings of the IEEE INFOCOM (pp. 2830–2838).
21.
Lee, J., & Hoh, B. (2010). Sell your experiences: a market mechanism based incentive for participatory sensing. In Proceedings of the IEEE PerCom (pp. 60–68).
22.
Zhou, P., Zheng Y., & Li, M. (2012). How long to wait?: Predicting bus arrival time with mobile phone based participatory sensing. In Proceedings of the ACM MobiSys (pp. 1228–1240).
23.
Rana, R., Chou, C., Kanhere, S., Bulusu, N., & Hu, W. (2010). Earphone: “An end-to-end participatory urban noise mapping”. In Proceedings of the ACM/IEEE IPSN (pp. 105–116).
24.
25.
Xu, J., Xiang, J., & Yang, D. (2015). Incentive mechanisms for time window dependent tasks in mobile crowdsensing. IEEE Transactions on Wireless Communications, 14(11), 6353–6364.CrossRef
26.
27.
Blumrosen, L., & Nisan, N. (2007). Combinatorial auctions (a survey). In N. Nisan, T. Roughgarden, E. Tardos, & V. Vazirani (Eds.), Algorithmic game theory (pp. 267–298). Cambridge: Cambridge University Press.CrossRef
28.
Cormen, T. (2009). Introduction to algorithms. Cambridge: MIT Press.MATH
29.
Amicia, R., Bonolaa, M., Bracciale, L., Rabuffi, A., Loretia, P., & Bianchi, G. (2014). Performance assessment of an epidemic protocol in VANET using real traces. Procedia Computer Science, 40, 92–99.CrossRef
30.
Peng, D., Wu, F., & Chen, G. (2015). Pay as how well you do: A quality based incentive mechanism for crowdsensing. In Proceedings of the ACM MobiHoc (pp. 177–186).
31.
Han, K., Zhang, C., Luo, J., Hu, M., & Veeravalli, B. (2016). Truthful scheduling mechanisms for powering mobile crowdsensing. IEEE Transactions on Computers, 65(1), 294–307.MathSciNetCrossRefMATH
32.
Zhang, Q., Wen, Y., & Tian, X., et al. (2015). Incentivize crowd labeling under budget constraint. In Proceedings of the IEEE INFOCOM (pp. 2812–2820).
Metadata
Title
Incentivize maximum continuous time interval coverage under budget constraint in mobile crowd sensing
Authors
Jia Xu
Jinxin Xiang
Yanxu Li
Publication date
09-03-2016
Publisher
Springer US
Published in
Wireless Networks / Issue 5/2017
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-016-1244-9

Other articles of this Issue 5/2017

Wireless Networks 5/2017 Go to the issue

OriginalPaper

Energy minimization resource allocation schemes for relay-enhanced OFDMA networks

OriginalPaper

Cooperation-enabled energy efficient base station management for dense small cell networks

OriginalPaper

Enhancing EAP-TLS authentication protocol for IEEE 802.11i

OriginalPaper

Algebraic connectivity aided energy-efficient topology control in selfish ad hoc networks

OriginalPaper

Stochastic approximation based on-line algorithm for fairness in multi-rate wireless LANs

OriginalPaper

TCP-Gvegas with prediction and adaptation in multi-hop ad hoc networks