A QoE Fuzzy Routing Protocol for Wireless Mesh Networks

  • Rafael Gomes
  • Waldir Junior
  • Eduardo Cerqueira
  • Antonio Abelem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6157)


Nowadays wireless systems changing our life experience, allowing ubiquitous communications, attracting new users, and supporting new applications, such as video streaming, VoIP, Mobile TV and other kind of multimedia applications. The combination of wireless networks and multimedia content distribution requires a new behavior of routing protocols for Wireless Mesh Networks (WMNs). Hence, new protocols must be developed to increase the user perception and optimize the usage of network resources. This paper presents a variation of the WMN routing protocol Optimized Link State Routing (OLSR), to achieve QoS and QoE requirements for multimedia applications. It is based on the dynamic choice of metrics and in a Fuzzy Link Cost (FLC) to determine the best routes for multimedia packets. Simulations were carried out to show the benefits of the proposed metric regarding user experience compared to existing versions of OLSR.


Wireless Mesh Networks QoS QoE Fuzzy Logic 


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  1. 1.
    Zhang, Y., Luo, J., Hu, H.: Wireless mesh networking: Architectures, protocols and standards. Auerbach Publications (2006)Google Scholar
  2. 2.
    Lekcharoen, S., Chaochanchaikul, C., Jittawiriyanukoon, C.: A design fuzzy control policing mechanisms on quality of service support in wireless networks. In: Proceedings of the 3rd international conference on Mobile technology, applications and systems (October 2006)Google Scholar
  3. 3.
    Takahashi, A., Hands, D., Barriac, V.: Standardization Activities in the IUT for a QoE Assessment of IPTV. IEEE Communication Magazine 46(2) (2008)Google Scholar
  4. 4.
    Clausen, T., Jacquet, P.: Optimized link state routing protocol (OLSR) RFC 3626 (2006), http://www.ietf.org/rfc/rfc3626.txt
  5. 5.
    De Couto, D., Aguayo, D., Bicket, J., Morris, R.: A high-throughput path metric for multi-hop wireless routing. In: 9th Annual International Conference on Mobile Computing and Networking, pp. 134–146 (2003)Google Scholar
  6. 6.
    Cordeiro, W., Aguiar, E., Moreira, W., Abelem, A., Stanton, M.: Providing quality of service for mesh networks using link delay measurements. In: 16th International Conference on Computer Communications and Networks, pp. 991–996 (2007)Google Scholar
  7. 7.
    Gomes, R., Moreira, W., Nascimento, V., Abelem, A.: Dynamic metric choice routing for mesh networks. In: 7th International Information and Telecommunication Technologies Symposium, I2TS (2008)Google Scholar
  8. 8.
    Moreira, W., Aguiar, E., Abelém, A., Stanton, M.: Using multiple metrics with the optimized link state routing protocol for wireless mesh networks. 26° Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Maio (2008)Google Scholar
  9. 9.
    Zadeh, L.A.: Fuzzy Sets. Information and Control 8 (1965)Google Scholar
  10. 10.
    Adeli, H., Sarma, K.C.: Cost Optimization of Structures: Fuzzy Logic, Genetic Algorithms, and Parallel Computing. Wiley, Chichester (2006)CrossRefGoogle Scholar
  11. 11.
    Zhang, R., Long, K.: A fuzzy routing mechanism in next-generation networks. In: Proc. IASTED International Conference on Intelligent Systems and Control (ISC) (October 2002)Google Scholar
  12. 12.
    Aboelela, E., Douligeris, C.: Routing in multimetric networks using a fuzzy link cost. In: Proceedings of the 2nd IEEE Symposium on Computers and Communications, ISCC ’97 (1997)Google Scholar
  13. 13.
    Anderson, D.H., Hall, L.O.: MR. FIS: Mamdani rule style fuzzy inference system. In: IEEE International Conference on Systems, Man, and Cybernetics (1999)Google Scholar
  14. 14.
    Institute of Electrical and Electronic Engineering. IEEE 802.11, 1999 Edition (ISO/IEC 8802-11: 1999): Information Technology - Telecom- munications and Information Exchange between Systems - Local and Metropolitan Area Network (1999)Google Scholar
  15. 15.
    Balam, J., Gibson, J.: Multiple descriptions and path diversity for voice communications over wireless mesh networks. IEEE Transactions on Multimedia, 1073–1088 (August 2007)Google Scholar
  16. 16.
  17. 17.
    Foreman. Paris and News, http://trace.eas.asu.edu/yuv/index.html
  18. 18.
    Lambrecht, C., Verscheure, O.: Perceptual quality measure using a spatio-temporal model of the human visual system. In: Digital Video Compression: Algorithms and Technologies, pp. 450–461 (1996)Google Scholar
  19. 19.
    Wang, Z., Lu, L., Bovick, A.: Video quality assessment based on structural distortion measurement. Signal Processing: Image Communication, special issue on Objective Video Quality Metrics (2004)Google Scholar
  20. 20.
    Wang, Z., Bovik, A.: Image quality assessment from error visibility to structural similarity. IEEE Trans. Image Processing (2004)Google Scholar
  21. 21.
    Pinson, M., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Transacions on Broadcasting (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rafael Gomes
    • 1
  • Waldir Junior
    • 2
  • Eduardo Cerqueira
    • 3
  • Antonio Abelem
    • 1
  1. 1.Federal University of Para 
  2. 2.Federal University of Para & INESC PortoPortugal
  3. 3.Federal University of Para & CISUC University of CoimbraPortugal

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