BRAIN-F: Beacon Rate Adaption Based on Fuzzy Logic in Vehicular Ad Hoc Network
| Autor: | Mohammad Hossein Anisi, Satria Mandala, Seyed Ahmad Soleymani, Zaidi Razak, Wan Haslina Hasan, Ayman Altameem, Noorzaily Mohamed Noor, Shidrokh Goudarzi, Abdul Hanan Abdullah |
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| Rok vydání: | 2016 |
| Předmět: |
Vehicular ad hoc network
Computer science business.industry ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS 020206 networking & telecommunications Computational intelligence Location status 02 engineering and technology Fuzzy logic Theoretical Computer Science Density based Non-line-of-sight propagation Computational Theory and Mathematics Artificial Intelligence Traffic conditions 0202 electrical engineering electronic engineering information engineering Wireless 020201 artificial intelligence & image processing business Software Computer network |
| Zdroj: | International Journal of Fuzzy Systems. 19:301-315 |
| ISSN: | 2199-3211 1562-2479 |
| DOI: | 10.1007/s40815-016-0171-3 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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