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BRAIN-F: Beacon Rate Adaption Based on Fuzzy Logic in Vehicular Ad Hoc Network

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Abstract

Beacon rate adaption is a way to cope with congestion of the wireless link and it consequently decreases the beacon drop rate and the inaccuracy of information of each vehicle in the network. In a vehicular environment, the beacon rate adjustment is strongly dependent on the traffic condition. Due to this, we firstly propose a new model to detect traffic density based on the vehicle’s own status and the surrounding vehicle’s status. We also develop a model based on fuzzy logic namely the BRAIN-F, to adjust the frequency of beaconing. This model depends on three parameters including traffic density, vehicle status and location status. Channel congestion and information accuracy are considered the main criteria to evaluate the performance of BRAIN-F under both LOS and NLOS. Simulation results demonstrate that the BRAIN-F not only reduces the congestion of the wireless link but it also increases the information accuracy.

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Acknowledgments

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this research. The research is supported by Ministry of Education Malaysia (MOE) and conducted in collaboration with Research Management Center (RMC) at Universiti Teknologi Malaysia (UTM) under VOT NUMBER: R.J130000.7828.4F708. The authors also thank University of Malaya for the financial support (UMRG Grant RP036A-15AET, RP036B-15AET, RP036C-15AET, RG325-15AFR) and facilities to carry out the work.

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Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia

    Seyed Ahmad Soleymani, Abdul Hanan Abdullah & Satria Mandala

  2. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Mohammad Hossein Anisi, Zaidi Bin Razak & Noorzaily Mohamed Noor

  3. College of Applied Studies and Community Services, King Saud University (KSU), Riyadh, 12372, Saudi Arabia

    Ayman Altameem

  4. Communication System and Network (iKohza) Research Group, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100, Kuala Lumpur, Malaysia

    Wan Haslina Hasan & Shidrokh Goudarzi

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  1. Seyed Ahmad Soleymani
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  2. Abdul Hanan Abdullah
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  3. Mohammad Hossein Anisi
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  9. Noorzaily Mohamed Noor
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Correspondence to Seyed Ahmad Soleymani.

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Soleymani, S.A., Abdullah, A.H., Anisi, M.H. et al. BRAIN-F: Beacon Rate Adaption Based on Fuzzy Logic in Vehicular Ad Hoc Network. Int. J. Fuzzy Syst. 19, 301–315 (2017). https://doi.org/10.1007/s40815-016-0171-3

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  • DOI: https://doi.org/10.1007/s40815-016-0171-3

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