skip to main content
10.1145/3009912.3009921acmconferencesArticle/Chapter ViewAbstractPublication PagesmiddlewareConference Proceedingsconference-collections
research-article

An Empirical Study on Coverage-Ensured Automotive Sensing using Door-to-door Garbage Collecting Trucks

Published:12 December 2016Publication History

ABSTRACT

Due to the advance in information and communication technologies, smart cities have become an increasingly appealing vision. One indispensable basis in the research, development and implementation of smart cities is urban sensing technologies, which collect various citywide data that will be processed to generate information for understanding the status of cities. Automotive sensing is a novel urban sensing technology that utilizes the mobility of automobiles to conduct sensing tasks. In a collaborative smart-city project with the city office of Fujisawa City, Japan, we designed and implemented an automotive sensing platform called Cruisers, and have deployed it into the garbage collecting trucks of the city to evaluate the applicability of automotive sensing in realistic urban settings. In this paper, we first detail the system design, implementation and deployment of Cruisers and then evaluate its performance in realistic setting.

References

  1. A. Bazzi and A. Zanella. Position based routing in crowd sensing vehicular networks. Ad Hoc Networks, 36:409--424, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. R. Bruno and M. Nurchis. Efficient data collection in multimedia vehicular sensing platforms. Pervasive and Mobile Computing, 16:78--95, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Q. Chen, X. Song, H. Yamada, and R. Shibasaki. Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference. In 30th AAAI Conference on Artificial Intelligence, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. X. Gao, B. Firner, S. Sugrim, V. Kaiser-Pendergrast, Y. Yang, and J. Lindqvist. Elastic pathing: Your speed is enough to track you. In Proc. ACM UbiComp, pages 975--986, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. B. Gedik and L. Liu. Mobieyes: A distributed location monitoring service using moving location queries. IEEE Transactions on Mobile Computing, 5(10):1384--1402, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. A. Hamid, H. Abouzeid, H. S. Hassanein, and G. Takahara. Optimal recruitment of smart vehicles for reputation-aware public sensing. In Proc. IEEE WCNC, pages 3160--3165, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  7. B. Hull, V. Bychkovsky, Y. Zhang, K. Chen, M. Goraczko, A. Miu, E. Shih, H. Balakrishnan, and S. Madden. Cartel: a distributed mobile sensor computing system. In Proc. ACM SenSys, pages 125--138, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. T. Kawasaki, T. Iwamoto, M. Matsumoto, T. Yonezawa, J. Nakazawa, K. Takashio, and H. Tokuda. A method for detecting damage of traffic marks by half celestial camera attached to cars. EAI Endorsed Transactions on Cognitive Communications, 15(5), 8 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Lanza, L. Sánchez, L. Muñoz, J. A. Galache, P. Sotres, J. R. Santana, and V. Gutiérrez. Large-scale mobile sensing enabled internet-of-things testbed for smart city services. International Journal of Distributed Sensor Networks, 2015:157, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. U. Lee, B. Zhou, M. Gerla, E. Magistretti, P. Bellavista, and A. Corradi. Mobeyes: smart mobs for urban monitoring with a vehicular sensor network. Wireless Communications, IEEE, 13(5):52--57, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. X. Li, W. Shu, M. Li, H.-Y. Huang, P.-E. Luo, and M.-Y. Wu. Performance evaluation of vehicle-based mobile sensor networks for traffic monitoring. IEEE Transactions on Vehicular Technology, 58(4):1647--1653, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  12. L. Liu, W. Wei, D. Zhao, and H. Ma. Urban resolution: New metric for measuring the quality of urban sensing. IEEE Transactions on Mobile Computing, 14(12):2560--2575, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. O. Masutani. A sensing coverage analysis of a route control method for vehicular crowd sensing. In Proc IEEE PerCom, pages 396--401, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  14. Y. Matsumoto and M. Satoh. Tablet-type gps tracking radiation detection system and viewer software. In Proc. IEEE SENSORS, pages 229--232, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  15. S. Morishita, S. Maenaka, D. Nagata, M. Tamai, K. Yasumoto, T. Fukukura, and K. Sato. Sakurasensor: Quasi-realtime cherry-lined roads detection through participatory video sensing by cars. In Proc. ACM UbiComp, pages 695--705, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. Nakazawa, Y. Takuro, T. Ito, M. Ogawa, and M. Sakamura. Keio Universal Sensor Network System Manual based on Sensor-Over-XMPP. http://sox.ht.sfc.keio.ac.jp.Google ScholarGoogle Scholar
  17. C. E. Palazzi, F. Pezzoni, and P. M. Ruiz. Delay-bounded data gathering in urban vehicular sensor networks. Pervasive and Mobile Computing, 8(2):180--193, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Sahoo, S. Cherkaoui, and A. Hafid. Hierarchical aggregation for delay-sensitive vehicular sensing. In Proc. IWCMC, pages 1365--1370, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  19. H. Wang, Y. Zhu, and Q. Zhang. Compressive sensing based monitoring with vehicular networks. In Proc. IEEE INFOCOM, pages 2823--2831, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  20. D. Zhao, H. Ma, L. Liu, and X.-Y. Li. Opportunistic coverage for urban vehicular sensing. Computer Communications, 60:71--85, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Y. Zhu, Z. Li, H. Zhu, M. Li, and Q. Zhang. A compressive sensing approach to urban traffic estimation with probe vehicles. IEEE Transactions on Mobile Computing, 12(11):2289--2302, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

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

Sign in
  • Published in

    cover image ACM Conferences
    SmartCities '16: Proceedings of the 2nd International Workshop on Smart
    December 2016
    55 pages
    ISBN:9781450346672
    DOI:10.1145/3009912

    Copyright © 2016 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 12 December 2016

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader