Author
Listed:
- Sultan S. Alshamrani
- Nishant Jha
- Deepak Prashar
Abstract
Recently, 5G and beyond 5G (B5G) systems, Ultrareliable Low Latency Network (URLLC) represents the key enabler for a range of modern technologies to support Industry 4.0 applications, such as transportation and healthcare. Real-world implementation of URLLC can help in major transformations in industries like autonomous driving, road safety, and efficient traffic management. Furthermore, URLLC contributes to the objective of fully autonomous cars on the road that can respond to dynamic traffic patterns by collaborating with other vehicles and surrounding environments rather than relying solely on local data. For this, the main necessity is that how information is to be transferred among the vehicles in a very small time frame. This requires information to be transferred among the vehicles reliably in extremely short time duration. In this paper, we have implemented and analyzed the Multiaccess Edge Computing- (MEC-) based architecture for 5G autonomous vehicles based on baseband units (BBU). We have performed Monte Carlo simulations and plotted curves of propagation latency, handling latency, and total latency in terms of vehicle density. We have also plotted the reliability curve to double-check our findings. When the RSU density is constant, the propagation latency is directly proportional to the vehicle density, but when the vehicle density is fixed, the propagation latency is inversely proportional. When RSU density is constant, vehicle density and handling latency are strictly proportional, but when vehicle density is fixed, handling latency becomes inversely proportional. Total latency behaves similarly to propagation latency; that is, it is also directly proportional.
Suggested Citation
Sultan S. Alshamrani & Nishant Jha & Deepak Prashar, 2021.
"B5G Ultrareliable Low Latency Networks for Efficient Secure Autonomous and Smart Internet of Vehicles,"
Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, September.
Handle:
RePEc:hin:jnlmpe:3697733
DOI: 10.1155/2021/3697733
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