skip to main content
10.1145/2996890.3007853acmotherconferencesArticle/Chapter ViewAbstractPublication PagesuccConference Proceedingsconference-collections
short-paper

Efficient estimation and control of WSANs for the greenhouse environment

Authors Info & Claims
Published:06 December 2016Publication History

ABSTRACT

This paper investigates the collaborative estimation and control problem of wireless sensor and actuator networks (WSANs) in the greenhouse environments. In order to reduce the energy consumption of the sensor nodes, each node is designated to transmit data to the actuator nodes under the non-uniform transmission rate mode. Considering the mutual effect of related clusters, a collaborative control scheme of the actuator nodes is proposed to enhance the estimation accuracy and convergence speed. Combining the fuzzy neural network with the PID control algorithm, the actuators conduct reliable control over the greenhouse environmental parameters. Performance evaluation analysis on the greenhouse temperature behaviors are presented to demonstrate the effectiveness of our proposed scheme in controlling the greenhouse environmental changes.

References

  1. Gelder,D. A., Dieleman, J. A., Bot, G. P. A., Marcelis, L. F. M. An overview of climate and crop yield in closed greenhouses. Journal of Horticultural Science and Biotechnology, 87(3): 193--202, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  2. Ramírez-Arias, A., Rodríguez,F., Guzmán, J. L., Berenguel,M. Multiobjective hierarchical control architecture for greenhouse crop growth. Automatica, 48(3):490--498, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Mirabella, O. and Brischetto, M. A hybrid wired/wireless networking infrastructure for greenhouse management. IEEE Transactions on Instrumentation and Measurement, 60(2):398--407, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  4. Yick, J., Mukherjee, B., Ghosal, D.Wireless sensor network survey. Computer Networks, 52(12): 2292--2230, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Jiang, H., Hallstrom, J. O. Fast, Accurate Event Classification on Resource-Lean Embedded Sensors. ACM Transactions on Autonomous and Adaptive Systems, 9(5) :39:1--23, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Gomes, T., Brito, J., Abreu, H., Gomes, H. and Cabral, J., "GreenMon: An Efficient Wireless Sensor Network Monitoring Solution for Greenhouses", in Proceedings of the 2015 IEEE International Conference on Industrial Technology, pp. 2192--2197, Sevilla, Spain, Mar. 2015.Google ScholarGoogle ScholarCross RefCross Ref
  7. Mo, L., Xu, B., "Adaptive filtering based collaborative actuation for wireless sensor and actuator networks", International Journal of Ad Hoc & Ubiquitous Computing, 20(4):223--236, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Akyildiz, I. F. and Kasimoglu, I. H., "Wireless sensor and actor networks: research challenges", Ad Hoc Networks, 2(4):351--367, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  9. Ghalib, A. S., Muslim, B. and Faisal, B. H., "Cluster-based coordination and routing framework for wireless sensor and actor networks", Wireless Communications and Mobile Computing, 11(8): 1140--1154, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Fan, D., Li, X., "Application of normalized weighted average algorithm in temperature acquisition system", Mechanical engineering and automation, 3:115--116, 2012.Google ScholarGoogle Scholar
  11. Li, H., Lin, K., Li, K., "Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks", Computer Communications, 34(4):591--597, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Wang, J., Xue, H., Jiang, X., "Application of Kalman filtering algorithm in greenhouse environment monitoring", in Proceedings of the 2013 International Symposium on Instrumentation and Measurement, Sensor Network and Automation, pp. 539--554, Ontario, Canada, Dec. 2013.Google ScholarGoogle Scholar
  13. Liao, Y., Chou, J., "Weighted data fusion use for ruthenium dioxide thin film PH array electrodes", IEEE Sensors Journal, 842--848, 2009Google ScholarGoogle ScholarCross RefCross Ref
  14. Zhang, W., Liu, S., and Yu, L., "Fusion Estimation for Sensor Networks with Nonuniform Estimation Rates", IEEE Transactions on Circuits and Systems I: Regular Papers, 61(5):1485--1498, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  15. Azaza, M., Tanougast, C, Fabrizio, E., Mami, A., "Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring", ISA Transactions, 1--11, 2015.Google ScholarGoogle Scholar
  16. Cheng, S. and Chang, T., "A Cyber Physical System Model Using Genetic Algorithm for Actuators Control", in Proceedings of the 2nd International Conference on Consumer Electronics, Communications and Networks, pp.2269--2272, Hubei, China. 2012.Google ScholarGoogle ScholarCross RefCross Ref

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 Other conferences
    UCC '16: Proceedings of the 9th International Conference on Utility and Cloud Computing
    December 2016
    549 pages
    ISBN:9781450346160
    DOI:10.1145/2996890

    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: 6 December 2016

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • short-paper

    Acceptance Rates

    Overall Acceptance Rate38of125submissions,30%

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader