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A Context Aware Framework for Mobile Crowd-Sensing

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Modeling and Using Context (CONTEXT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10257))

Abstract

Context awareness plays ever increasing role in Mobile Crowd-Sensing (MCS), which relies on sensing capabilities of mobile devices to collect real-time user data and related context. The paper proposes a MCS framework for valuable data collection in order to enable smart applications. The paper also addresses a key challenge in MCS on how to reduce energy consumption in order to encourage user participation. The paper argues that to optimize task allocation costs, it is important for a given query to select the most appropriate participants according to the context of the device, the participant, and the sensing task. Context awareness can significantly reduce the sensing and communication costs. Yet to incorporate context awareness into MCS, there is a need for a standard and overarching context model. This paper proposes a multi-dimensional context model to capture related contextual information in the MCS domain, and incorporate it into a context-aware MCS framework to improve energy efficiency and support task allocation. The paper concludes with discussing implementation and evaluation of the proposed approach.

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Notes

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  2. 2.

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References

  1. Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49, 32–39 (2011)

    Article  Google Scholar 

  2. Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Commun. Mag. 48(9), 140–150 (2010)

    Article  Google Scholar 

  3. Baier, P., Durr, F., Rothermel, K.: Efficient distribution of sensing queries in public sensing systems. In: 2013 IEEE 10th Internaional Conference on Mobile Ad-Hoc Sensor Systems, pp. 272–280, October 2013

    Google Scholar 

  4. Wang, Y., Lin, J., Annavaram, M., Jacobson, Q.A., Hong, J., Krishnamachari, B., Sadeh, N.: A framework of energy efficient mobile sensing for automatic user state recognition. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, MobiSys 2009, pp. 179–192 (2009)

    Google Scholar 

  5. Ra, M.-R., Liu, B., La Porta, T.F., Govindan, R.: Medusa: a programming framework for crowd-sensing applications. In: Proceedings of 10th international conference on Mobile Systems, Applications, and Services, MobiSys 2012, pp. 337–350 (2012)

    Google Scholar 

  6. Wang, L., Zhang, D., Xiong, H.: EffSense: energy-efficient and cost-effective data uploading in mobile crowdsensing. In: Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, UbiComp 2013, pp. 1075–1086 (2013)

    Google Scholar 

  7. Sherchan, W., Jayaraman, P.P., Krishnaswamy, S., Zaslavsky, A., Loke, S., Sinha, A.: Using on-the-move mining for mobile crowdsensing. In: 2012 IEEE 13th International Conference on Mobile Data Management, pp. 115–124, July 2012

    Google Scholar 

  8. Reddy, S., Estrin, D., Srivastava, M.: Recruitment Framework for Participatory Sensing Data Collections. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 138–155. Springer, Heidelberg (2010). doi:10.1007/978-3-642-12654-3_9

    Chapter  Google Scholar 

  9. Ruan, Z., Ngai, E.C.H., Liu, J.: Wireless sensor deployment for collaborative sensing with mobile phones. Comput. Netw. Int. J. Comput. Telecommun. Netw. 55(15), 3224–3245 (2011)

    Google Scholar 

  10. He, S., Shin, D., Zhang, J., Chen, J.: Toward optimal allocation of location dependent tasks in crowdsensing. In: 2014 Proceedings of IEEE, INFOCOM, pp. 745–753 (2014)

    Google Scholar 

  11. Hassani, A., Haghighi, P.D., Jayaraman, P.P.: Context-aware recruitment scheme for opportunistic mobile crowdsensing. In: Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS, pp. 266–273 (2016)

    Google Scholar 

  12. Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Melbourne, Australia

    Alireza Hassani & Pari Delir Haghighi

  2. Swinburne University of Technology, Melbourne, Australia

    Prem Prakash Jayaraman

  3. CSIRO, Research Way, Clayton, Melbourne, Australia

    Arkady Zaslavsky

Authors
  1. Alireza Hassani
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  2. Pari Delir Haghighi
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  3. Prem Prakash Jayaraman
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  4. Arkady Zaslavsky
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Corresponding author

Correspondence to Alireza Hassani .

Editor information

Editors and Affiliations

  1. Pierre and Marie Curie University , Paris, France

    Patrick Brézillon

  2. University of Maine , Orono, Maine, USA

    Roy Turner

  3. University of Genoa , Genoa, Italy

    Carlo Penco

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Hassani, A., Haghighi, P.D., Jayaraman, P.P., Zaslavsky, A. (2017). A Context Aware Framework for Mobile Crowd-Sensing. In: Brézillon, P., Turner, R., Penco, C. (eds) Modeling and Using Context. CONTEXT 2017. Lecture Notes in Computer Science(), vol 10257. Springer, Cham. https://doi.org/10.1007/978-3-319-57837-8_45

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  • DOI: https://doi.org/10.1007/978-3-319-57837-8_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57836-1

  • Online ISBN: 978-3-319-57837-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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