Abstract
Vehicle automation is driving the integration of advanced sensors and new applications that demand high-quality information, such as collaborative sensing for enhanced situational awareness. In this work, we considered a vehicular sensing scenario supported by 5G communications, in which vehicle sensor data need to be sent to edge computing resources with stringent latency constraints. To ensure low latency with the resources available, we propose an optimization framework that deploys User Plane Functions (UPFs) dynamically at the edge to minimize the number of network hops between the vehicles and them. The proposed framework relies on a practical Software-Defined-Networking (SDN)-based mechanism that allows seamless re-assignment of vehicles to UPFs while maintaining session and service continuity. We propose and evaluate different UPF allocation algorithms that reduce communications latency compared to static, random, and centralized deployment baselines. Our results demonstrated that the dynamic allocation of UPFs can support latency-critical applications that would be unfeasible otherwise.
Highlights
Vehicles have been equipped during recent years with a wide range of sensors such as odometers, Global Positioning System (GPS) receivers, distance sensors, cameras, etc
We can observe that, by increasing the percentage of Base Station (BS) that contain a User Plane Functions (UPFs), the latency decreased in general, as expected
The K-means greedy average algorithm was the best one followed by the Louvain modularity greedy average, which performed slightly worse for percentages of BSs with UPFs lower than 5%
Summary
Vehicles have been equipped during recent years with a wide range of sensors such as odometers, Global Positioning System (GPS) receivers, distance sensors, cameras, etc. Classic examples of sensing-oriented vehicles are space balloons, environmental satellites, and oceanographic vessels, to name just a few. Even though these platforms have been operated for decades, all of them are still useful for research [20,21,22]. By installing low-cost sensors in public transportation vehicles, for instance, the operation costs can be minimized. This idea is not new: the National Aeronautics and Space Administration (NASA) deployed sensors in 65 commercial aircraft in 2006 to create the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) moisture data repository for weather forecasting [26,27]. The research in [32] formalized this paradigm by defining and measuring the coverage depending on the traffic routes, by determining the relationship between the coverage quality and the number of vehicles and selecting the minimum number of vehicles to achieve a target coverage quality
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