Attributes | Description | Informational |
---|---|---|
Type | New | |
Industry sector | Area: SDN & NFV | ATTO RESEARCH focuses on the problems related to networking and software technology for a better connection. It has created a technology that allows to build IT infrastructure, and aims to grow into a ‘Software Defined Infrastructure’ company. |
Business driver | The stable vehicular network is essential to enable various applications such as autonomous driving through VANETs. Proposed MEC architecture tends to enable a promising infrastructure where a stable network topology can be predicted locally to improve the network performance by providing intensive calculation for vehicles in the adjacent roads. Thus, converging the two concepts of MEC and topology prediction can provide a strong use case for the vehicular networks such as proactive path stabilization. | The MEC-based Efficient Routing Algorithm can provide a stable path by using the predicted future position for the nearby vehicles. The information can also be made available to the adjacent road resulting from being useful to provide a stable topology on the road tracks. |
Business use case |
| |
Business cost - Initial build | Minimal configuration is three servers in total:
| Price factor depends on the cost of RSU quality, and should be only considered for physical deployment. i.e. wireless or wired. |
Business cost - Operational | Virtual environment does not require cost. | |
Operational need | Using the frontend GUI to:
| |
Additional details |
| PPT is attached as proposal statement. |
# | Relevance | Title |
---|---|---|
1 | IoV, Prediction | Abbas, M.T., Muhammad, A. and Song, W.C., 2019. Road-aware estimation model for path duration in Internet of vehicles (IoV). Wireless Personal Communications, 109(2), pp.715-738. |
2 | IoV, Prediction | Abbas, M.T., Jibran, M.A., Afaq, M. and Song, W.C., 2020. An adaptive approach to vehicle trajectory prediction using multimodel Kalman filter. Transactions on Emerging Telecommunications Technologies, 31(5), p.e3734. |
3 | IoV, Prediction | Abbas, M.T., Muhammad, A. and Song, W.C., 2020. SD-IoV: SDN enabled routing for internet of vehicles in road-aware approach. Journal of Ambient Intelligence and Humanized Computing, 11(3), pp.1265-1280. |
4 | RSU, MEC | Wang, X., Han, Y., Leung, V.C., Niyato, D., Yan, X. and Chen, X., 2020. Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(2), pp.869-904. |
5 | RSU, MEC | Ndikumana, A., Tran, N.H., Kim, K.T. and Hong, C.S., 2020. Deep Learning Based Caching for Self-Driving Cars in Multi-Access Edge Computing. IEEE Transactions on Intelligent Transportation Systems. |
Motivational aspects are:
To this end, we introduce:
The architectural view of our system is as follows:
Stable path connectivity scenarios are illustrated as shown below:
Company | Email (Contact) | Profiles | Picture |
---|---|---|---|
Asif Mehmood | Jeju National University | malikasifmahmoodawan@gmail.com | |
Afaq Muhammad | Jeju National University | afaq@jejunu.ac.kr | |
Saqib Muhammad | Jeju National University | saqib@jejunu.ac.kr | |
Muhammad Ali Jibran | --- | alijibran35@gmail.com | |
Wang-Cheol Song | Jeju National University | philo@jejunu.ac.kr | |
Taekyung Lee | ATTO Research | taekyung.lee@atto-research.com |
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