Proposing a Method for Controlling Congestion in Wireless Sensor Networks Using Comparative Fuzzy Logic



Recent developments and advances on electronics and wireless telecommunications have enabled researchers to design and produce low-power and small sensors with reasonable prices which can be used for various applications. Wireless multimedia sensor networks are a new subset of WSN family which is capable of doing operations such as receiving multimedia information, i.e. video, sound, photo and numerical data from the surrounding environment, processing them and transmitting them. Due to high transmission rate and the explosive feature, the transmission of video flows in WSNs concerned with several challenges. Congestion also leads to the loss of packets and costly retransmission of packets. Consequently, the limited energy of the sensor nodes is wasted. Accordingly, in this paper, using fuzzy logic, a new congestion control method was proposed for these networks. In the proposed method, congestion announcement and control are carried out by using three main parameters, i.e. the remaining energy level of the node, load density and accessible detection bandwidth. The results of evaluations, done via OPNET 11.5, indicated that using the proposed method led to an average delay reduction in packet arrival. Also, less energy of the nodes is consumed and network lifetime is enhanced. Multimedia is used for novel approaches such as the followings: communications, commerce, education, entertainment, personal locator services, advanced health care, control systems, traffic avoidance and execution and in Information Technology.


Wireless sensor networks (WSNs) Congestion control Fuzzy logic Energy Packet delivery delay 


  1. 1.
    Aghdam, S. M., Khansari, M., Rabiee, H. R., & Salehi, M. (2014). WCCP: A congestion control protocol for wireless multimedia communication in sensor networks. Ad Hoc Networks, 13, 516–534.CrossRefGoogle Scholar
  2. 2.
    Wang, C., Sohraby, K., Lawrence, V., Li, B. & Hu, Y. (2006). Priority-based congestion control in wireless sensor networks. In IEEE International Conference on Sensor Networks, Uubiquitous, and Trustworthy Computing (SUTC 2006) (pp. 22–31). Taichung, Taiwan, IEEE Computer Society.Google Scholar
  3. 3.
    Yaghmaee, M. H., & Adjeroh, D. A. (2009). Priority-based rate control for service differentiation and congestion control in wireless multimedia sensor networks. Computer Netsworks, 53(11), 1798–1811.CrossRefMATHGoogle Scholar
  4. 4.
    Chen, S., & Yang, N. (2006). Congestion avoidance based on light weight buffer management in sensor networks. IEEE Transactions on Parallel and Distributed Systems, Special Issue on Localized Communication and Topology Protocols for Ad Hoc Networks, 17, 934–946.CrossRefGoogle Scholar
  5. 5.
    Wan C. Y., Eisenman S. B. & Campbell A. T. (2003) CODA: Congestion detection and avoidance in sensor networks. In The Proceeding of ACM Sensys’03, Los Angeles, California, USA.Google Scholar
  6. 6.
    Hull, B., Jamieson, K. & Balackrishnan, H. (2004). Mitigatting congestion in wireless sensor networks. In Proceedings of Sensor Systems 04, 2004 (pp. 134–147).Google Scholar
  7. 7.
    Ee, C. & Bajsys, R. (2004). Congestion control and fairness for many-to-one routing in sensor networks. In Proceedings of ACM Sensor Systems.Google Scholar
  8. 8.
    Wang, C., Li, B., Sohrabi, K., Daneshmand, M., & Hu, Y. (2007). Upstream congestion control in wireless sensor networks through cross-optimaization. IEEE Journal on Ed Area in Communication, 25(4), 786–795.CrossRefGoogle Scholar
  9. 9.
    Vuran, M. C., & Akyildiz, I. F. (2010). XLP: A cross-layer protocol for efficient communication in wireless sensor networks. IEEE Transactions on Mobile Computing, 9(11), 1578–1591.CrossRefGoogle Scholar
  10. 10.
    Zawodniok, M., & Jagannathan, S. (2007). Predictive congestion control protocol for wireless sensor networks. IEEE Transactions on Wireless Communication, 6(11), 3955–3963.CrossRefGoogle Scholar
  11. 11.
    Adjeroh, D. & Yaghmaee, H. (2008). A new priority based congestion control protocol for wireless multimedia sensor networks. In: International Symposium on A world of Wireless, Mobile and Multimedia Networks, Newport beach, CA, USA (pp. 1–8) June 23–26, 2008.Google Scholar
  12. 12.
    Huang, R., Fang, Y., Li, S., Yin, X., & Zhou, X. (2009). A fairness-aware congestion control scheme in wireless sensor networks. IEEE Transactions on Vehicular Technology, 58(9), 5225–5234.CrossRefGoogle Scholar
  13. 13.
    Tao, L. Q. & Yu, F. Q. (2010). ECODA: Enhanced congestion detection and avoidance for multiple class of traffic in sensor networks. Transactions on Consumer Electronics, 56(3).Google Scholar
  14. 14.
    Basaran, C., Kang, K. D., & Mehmet, H. S. (2010). Hop-by-hop congestion control and load balancing in wireless sensor networks. In 2010 IEEE 35th conference on local computer networks (LCN) (pp. 448–455). IEEE.Google Scholar
  15. 15.
    Akyildiz, I. F., Akan, O. B. & Sankarasubramaniam, Y. (2003). ESRT: Event-to-sink reliable transport in wireless sensor networks. In Proceedings of ACMMobihoc’03, June 1–3, 2003, Annapolis, Maryland.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer, Malekan BranchIslamic Azad UniversityMalekanIran
  2. 2.Tehran University of Medical SciencesTehran UniversityTehranIran

Personalised recommendations