Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 9, Issue 4, Pages 2817-2828Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3093545
Keywords
6th-generation (6G) cellular networks; compatibility; machine learning (ML); routing; vehicular networks
Categories
Funding
- Brain Pool Program through the National Research Foundation of Korea (NRF) - Ministry of Science and ICT [2019H1D3A1A01102978]
- Grand Information Technology Research Center Program through the Institute of Information & Communications Technology and Planning & Evaluation (IITP) - Ministry of Science and ICT (MSIT), South Korea [IITP-2020-2020-0-01612]
- National Research Foundation of Korea [2019H1D3A1A01102978] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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The sixth-generation cellular networks incorporate artificial intelligence and machine learning to enable proactive communications and predictive decision making, promoting the Internet of Vehicles concept. The existing vehicular communications face challenges in unreliable communication links due to multihop ad hoc communications and high mobility environment. This study highlights the importance of communication route compatibility and proposes a machine learning and analytical compatibility-based ad hoc routing protocol to estimate and predict compatibility time for route selection.
The sixth-generation cellular networks bring about proactive communications with predictive decision making by incorporating artificial intelligence (AI) and machine learning (ML) in vehicular networks, toward envision of the Internet of Vehicles (IoV). Currently, vehicular communications suffer from unreliable communication links due to multihop ad hoc communications and the high-mobility environment. The available literature falls short in providing a reliable routing scheme that proactively and accurately estimates or predicts connectivity duration between two vehicles. In this study, we highlight the need for communication route compatibility (connectivity duration) as a route selection parameter along with trustworthiness. We propose an ML and analytical compatibility-based ad hoc routing protocol that allows a vehicle to estimate or predict the compatibility time of all candidate routes, to choose the best route. We evaluated one analytical and five ML classification techniques on our OpenStreemMap (OSM) and SUMO mobility trace generated data set (Seoul and Berlin). Our exhaustive simulation demonstrated that our proposed scheme (six variations) dismisses all short-lived routes and achieves 2-3 times higher packet delivery ratio in comparison to the existing hop count-based routing (AOMDV and trust cryptographic secure routing). The proposed scheme disregards paths having few intermediate nodes for long-lasting paths with the expenses of a few extra hops. We also present a comprehensive comparative study to evaluate ML techniques based on the well-known metrics, such as accuracy, time, misclassification, F1-score, etc.
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