Abstract
This paper addresses the bandwidth slicing for different types of connected vehicle applications with diverse demands in heterogeneous networks. We develop a joint network and device levels slicing scheme based on bandwidth reservation and allocation optimization. The scheme enables vehicles to reserve bandwidth in advance for their critical applications. It categorizes and prioritises vehicular applications based on their degree of criticality. The reserved bandwidth is first allocated. Then, the sub-optimal allocation of the remaining bandwidth is determined and allocated according to degree of criticality of vehicular applications to achieve maximal resource utilization. Moreover, we employ machine learning to accurately predict the time for bandwidth re-slicing, whereas exceeding this time causes high cost of latency and consequently degrades the application performance. Our proposed scheme outperforms the conventional slicing allocation schemes in terms of efficiency and reliability of bandwidth utilization.
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Al-khatib, A.A., Khelil, A., Balfaqih, M. (2021). Bandwidth Slicing with Reservation Capability and Application Priority Awareness for Future Vehicular Networks. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-030-75100-5_58
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DOI: https://doi.org/10.1007/978-3-030-75100-5_58
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