Detailed and accurate vehicle-oriented sensor data is considered fundamental for efficient vehicle-to-everything V2X communication applications, especially in the upcoming highly heterogeneous, brisk and agile 5G networking era. Information retrieval, transfer and manipulation in real-time offers a small margin for erratic behavior, regardless of its root cause.
This paper presents the vision of 5G-MiEdge, a research project leveraging the benefits of merging MEC and mmWave technologies. Based on that vision, the most relevant use cases and services for the forthcoming Tokyo 2020 Olympics are proposed. The focus is on showing how integrating MEC and mmWave into the 5G network architecture can offer a much more effective system.
The goal of this work is to propose an energy-efficient algorithm for dynamic computation offloading, in a multi-access edge computing scenario, where multiple mobile users compete for a common pool of radio and computational resources. We focus on delay-critical applications, incorporating an upper bound on the probability that the overall time required to send the data and process them exceeds a prescribed value. In a dynamic setting, the above constraint translates into preventing the sum of the communication and computation queues' lengths from exceeding a given value.
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