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Performance evaluation of the ADSA in a vehicular network: MAC approach in IEEE 802.11p

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Abstract

The added benefits brought by the advent of the Vehicular network (VN) technology have stimulated a lot of hope in the area emergent transportation industries. Two most important factors that have motivated and contributed to the development, design and implementation of the VN standards include the need to ensure safety and the need to consider road accident avoidance strategies. However, the innate dynamic and the high topological mobility of the nodes in Vehicular Ad Hoc Networks (VANETs) raise complex and challenging issues with the standard. One of the complexities is the problem posed by Doppler effect (DE) resulting from the high mobility of the VANET nodes. In an attempt to compensate the induced Doppler shift (DS), the Automatic Doppler shift adaptation (ADSA) method was recently introduced to combat DE in a VANET. ADSA proved to be more resilient and effective in term of Bit error rate (BER). Moreover, for realistic applications, BER tests alone are insufficient. Therefore, in this work, a thorough analysis of the method is explored and the strength of the refined ADSA method is evaluated in terms of throughput, elapsed time, packet loss, model efficiency and data transfer rate. These metrics are used to perform a comparative analysis of ADSA versus adaptive modulation code (AMC) and auto-rate fallback (ARF). Results from the analysis shows that the ADSA approach is very effective and has a strong robustness compared to ARF and AMC with up to 300–700 % improvement in throughput and a 60–75 % reduction in consumed time.

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Acknowledgments

This work was supported by Tshwane University of Technology/French South African Institute of Technology and National Research Foundation (NRF) via the scholarship program of Research and Innovation Support and Advancement (RISA).

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Feukeu, E.A., Djouani, K. & Kurien, A. Performance evaluation of the ADSA in a vehicular network: MAC approach in IEEE 802.11p. J Ambient Intell Human Comput 6, 351–360 (2015). https://doi.org/10.1007/s12652-015-0268-9

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