Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Design of the Fuzzy-based Mobile Model for Energy Efficiency within a Wireless Sensor Network

  • Yun, Dai Yeol (Department of Plasma Bioscience and Display, Kwangwoon University) ;
  • Lee, Daesung (Department of Computer Engineering, Catholic University of Pusan)
  • Received : 2021.06.30
  • Accepted : 2021.08.04
  • Published : 2021.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Research on wireless sensor networks has focused on the monitoring and characterization of large-scale physical environments and the tracking of various environmental or physical conditions, such as temperature, pressure, and wind speed. We propose a stochastic mobility model that can be applied to a MANET (Mobile Ad-hoc NETwork). environment, and apply this mobility model to a newly proposed clustering-based routing protocol. To verify its stability and durability, we compared the proposed stochastic mobility model with a random model in terms of energy efficiency. The FND (First Node Dead) was measured and compared to verify the performance of the newly designed protocol. In this paper, we describe the proposed mobility model, quantify the changes to the mobile environment, and detail the selection of cluster heads and clusters formed using a fuzzy inference system. After the clusters are configured, the collected data are sent to a base station. Studies on clustering-based routing protocols and stochastic mobility models for MANET applications have shown that these strategies improve the energy efficiency of a network.

Keywords

References

  1. D. K. P. Asiedu, S. Shin, K. M. Koumadi, and K. J. Lee, "Review of simultaneous wireless information and power transfer in wireless sensor networks," Journal of Information and Communication Convergence Engineering, vol. 17, no. 2, pp. 105-116, 2019. DOI: 10.6109/jicce.2019.17.2.105.
  2. J. Won and H. K. Park, "An adaptive power-controlled routing protocol for energy-limited wireless sensor networks," Journal of Information and Communication Convergence Engineering, vol. 16, no. 3, pp. 135-141, 2018. DOI: 10.6109/jicce.2018.16.3.135.
  3. Amis, Alan D., Ravi Prakash, Thai HP Vuong, and Dung T. Huynh. "Max-min d-cluster formation in wireless ad hoc networks." In Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No. 00CH37064), vol. 1, pp. 32-41. IEEE, 2000. DOI: 10.1109/INFCOM.2000.832171.
  4. S. Basagni, "Distributed and mobility-adaptive clustering for multimedia support in multi-hop wireless networks," in Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference, vol. 2, pp. 889-893, 1999. DOI: 10.1109/VETECF.1999.798457.
  5. M. Alinci, E. Spaho, A. Lala, and V. Kolici, "Clustering algorithms in MANETs: A review," in 2015 Ninth International Conference on Complex, Intelligent, and Software Intensive Systems, pp. 330-335, 2015. DOI: 10.1109/CISIS.2015.47.
  6. ZigBee Specification, Document 053474r20, Zigbee Standards Organization: San Ramon, CA, USA, 2012.
  7. P. M. L. Chan, R. E. Sheriff, Y. F. Hu, P. Conforto, and C. Tocci, "Mobility management incorporating fuzzy logic for heterogeneous a IP environment," IEEE Communications Magazine, vol. 39, no. 12, pp. 42-51, 2001. DOI: 10.1109/35.968811.
  8. T. Inaba, D. Elmazi, Y. Liu, S. Sakamoto, L. Barolli, and K. Uchida, "Integrating wireless cellular and ad-hoc networks using fuzzy logic considering node mobility and security," in 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops, pp. 54-60, 2015. DOI: 10.1109/WAINA.2015.116.
  9. M. Khaledi, H. R. Rabiee, and M. Khaledi, "Fuzzy mobility analyzer: A framework for evaluating mobility models in mobile adhoc networks," in 2010 IEEE Wireless Communication and Networking Conference, pp. 1-6, 2010. DOI: 10.1109/WCNC.2010.5506382.
  10. C. Gomathy and S. Shanmugavel, "An efficient fuzzy based priority scheduler for mobile ad hoc networks and performance analysis for various mobility models," in 2004 IEEE Wireless Communications and Networking Conference, vol. 2, pp. 1087-1092, 2004. DOI: 10.1109/WCNC.2004.1311339.
  11. M. Saeed, M. El-Ghoneimy, and H. Kamal, "An enhanced fuzzy logic optimization technique based on user mobility for LTE handover," in 2017 34th National Radio Science Conference (NRSC), pp. 230-237, 2017. DOI: 10.1109/NRSC.2017.7893481.
  12. A. Klein, N. P. Kuruvatti, J. Schneider, and H. D. Schotten, "Fuzzy Q-learning for mobility robustness optimization in wireless networks," in 2013 IEEE Globecom Workshops (GC Wkshps), pp. 76-81, 2013. DOI: 10.1109/GLOCOMW.2013.6824965.
  13. Y. Ogawa, T. Inoue, T. Inada, Y. Tagawa, K. Yoshimitsu, and N. Shiba, "Locomotion assistance for the person with mobility impairment: Fuzzy control of cycling movement by means of surface electrical-stimulation," in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2420-2423, 2007. DOI: 10.1109/IEMBS.2007.4352816.
  14. J. Ye, X. Shen, and J. W. Mark, "Call admission control in wideband CDMA cellular networks by using fuzzy logic," IEEE Transactions on Mobile Computing, vol. 4, no. 2, pp. 129-141, 2005. DOI: 10.1109/TMC.2005.19.