Artificial Intelligence Review

, Volume 27, Issue 2–3, pp 95–111 | Cite as

Assessment and improvement of quality of service in wireless networks using fuzzy and hybrid genetic-fuzzy approaches

  • Mohammad Saraireh
  • Reza Saatchi
  • Samir Al-khayatt
  • Rebecca Strachan


Fuzzy and hybrid genetic-fuzzy approaches were used to assess and improve quality of service (QoS) in simulated wireless networks. Three real-time audio and video applications were transmitted over the networks. The QoS provided by the networks for each application was quantitatively assessed using a fuzzy inference system (FIS). Two methods to improve the networks’ QoS were developed. One method was based on a FIS mechanism and the other used a hybrid genetic-fuzzy system. Both methods determined an optimised value for the minimum contention window (CWmin) in IEEE 802.11 medium access control (MAC) protocol. CWmin affects the time period a wireless station waits before it transmits a packet and thus its value influences QoS. The average QoS for the audio and video applications improved by 42.8% and 14.5% respectively by using the FIS method. The hybrid genetic-fuzzy system improved the average QoS for the audio and video applications by 35.7% and 16.5% respectively. The study indicated that the devised methods were effective in assessing and significantly improving QoS in wireless networks.


Fuzzy logic Hybrid genetic-fuzzy logic Quality of service Wireless networks Multimedia transmission 


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  1. Boyce J, Gaglianello R (1998) Packet loss effects on MPEG video sent over the public Internet. Proc ACM Multimedia 98: 181–190CrossRefGoogle Scholar
  2. Chen C, Hsiao P (2005) Supporting QoS in wireless MAC by fuzzy control. IEEE Wire Commun Netw Conf 2: 1242–1247CrossRefGoogle Scholar
  3. Chatzimisios P, Boucouvalas A, Vitsas V (2005) Performance analysis of the IEEE 802.11 MAC protocol for wireless LANs. Wiley Int J Commun Syst 18(6): 545–569CrossRefGoogle Scholar
  4. Dalgic I, Tobagi F (1996) Glitches as a measure of video quality degradation caused by packet loss. Technical Report No. CSL-TR-96–702, Computer Systems Laboratory, Department of Electrical Engineering and Computer Science, Stanford University, Stanford, USAGoogle Scholar
  5. Gannoune L, Robert S (2004) Dynamic tuning of the contention window minimum (CWmin) for enhanced service differentiation in IEEE 802.11 wireless adhoc networks. IEEE Int Sympos Personal Indoor Mobile Radio Commun (PIMRC’04) 1: 311–317Google Scholar
  6. Goldberg D (1989) Genetic algorithms in search, optimisation, and machine learning. Addison-WesleyGoogle Scholar
  7. IEEE (1999) IEEE standard for wireless LAN medium access control (MAC) and physical layer (PHY) Specifications, ISO/IEC 8802-11:1999EGoogle Scholar
  8. IEEE (2004) Wireless LAN medium access control (MAC) and physical layer (PHY) Specifications: Amendment 7: Medium Access Control (MAC) Quality of Service (QoS) Enhancement, IEEE Standard 802.11e/Draft 11.0Google Scholar
  9. ITU-T (2001) Recommendation G.1010, End-user multimedia QoS categoriesGoogle Scholar
  10. Liu Y, Hsu T (2005) MAC protocols for multi-channel WLANs. IEICE Trans Commun E88-B(1): 325–332CrossRefGoogle Scholar
  11. Mamdani E (1997) Application of fuzzy logic to approximate reasoning using linguistic systems. Fuzzy Sets Syst 26: 1182–1191Google Scholar
  12. NS-2, Network simulator 2. [Online]., last accessed 15 September 2008
  13. Peng Y, Wu H, Cheng S, Long K (2002) A new self-adapt DCF algorithm. IEEE Globecom 87–91Google Scholar
  14. Qixiang P, Soung L, Jack Y, Gary C (2004) A TCP- like adaptive contention window scheme for WLAN. IEEE Int Conf Commun 6: 3723–3727Google Scholar
  15. Ross T (2004) Fuzzy logic with engineering applications. WileyGoogle Scholar
  16. Sakawa M (2002) Genetic algorithms and fuzzy multiobjective optimisation. SpringerGoogle Scholar
  17. Saraireh M, Saatchi R, Shur U, Strachan R (2004) Fuzzy logic based evaluation of quality of service for multimedia transmission. Proc PREP 2004: 13–14Google Scholar
  18. Saraireh M, Saatchi R, Al-khayatt, S, Strachan, R (2006) Development and evaluation of a fuzzy inference engine to incorporate quality of service. In: Proceedings of IEEE international conference on wireless and mobile communications, Romania, pp 29–34Google Scholar
  19. Saraireh M, Saatchi R, Al-khayatt, S, Strachan, R, Abo-Hammour, Z (2006) Optimisation of IEEE 802.11 MAC protocol parameters using a hybrid genetic-fuzzy approach. In: Proceedings of the IEEE systems, man and Cybernetics Society United Kingdom and Republic of Ireland conference on Advances in Cybernetics Systems, United Kingdom, Chapter 5, pp 253–258Google Scholar
  20. Yener A, Rose C (1997) Genetic algorithms applied to cellular call admission: local policies. IEEE Trans Vehicular Technol 46(1): 72–79CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Mohammad Saraireh
    • 1
  • Reza Saatchi
    • 2
  • Samir Al-khayatt
    • 2
  • Rebecca Strachan
    • 2
  1. 1.Faculty of Engineering/Computer Engineering DepartmentMutah UniversityMutahJordan
  2. 2.Faculty of ACESSheffield Hallam UniversitySheffieldUK

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