In this paper, we investigate how to analytically design an analytical offloading strategy for a multiuser mobile edge computing (MEC)-based smart internet of vehicle (IoV), where there are multiple computational access points (CAPs) which can help compute tasks from the vehicular users. As it is difficult to derive an analytical offloading ratio for a general MEC-based IoV network, we turn to provide an analytical offloading scheme for some special MEC networks including one-to-one, one-to-two and two-to-one cases. For each case, we study the system performance by using the linear combination of latency and energy consumption, and derive the analytical offloading ratio through minimizing the system cost. Simulation results are finally presented to verify the proposed studies. In particular, the proposed analytical offloading scheme can achieve the optimal performance of the brute force (BF) scheme. The analytical results in this paper can serve as an important reference for the analytical offloading design for a general MEC-based IoV.

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Review of Roaming Implementations 1. Status of this Memo This memo provides information for the Internet community. This memo does not specify an Internet standard of any kind. Distribution of this memo is unlimited.

In recent years, the utilization and application of unmanned aerial vehicle (UAV) have attracted much attention, both from academy and industry. UAVs have been widely used in many practical communication scenarios, due to its high flexibility, high mobility, and low cost. Therefore, this paper addresses the key technologies of UAV communication and reviews the current research status, from various aspects including UAV communication transmission, UAV formation control and networking, UAV resource allocation, and intelligent communication from artificial intelligence algorithms. Then, artificial intelligence is introduced into multiple aspects of UAV communication, including channel transmission, control and networking, and resource scheduling, to organically integrate artificial intelligence into UAV communication, which can help reduce the complexity of communication algorithms and improve system spectrum efficiency. This paper will help improve the efficiency of UAV communication and promote the development of UAV-related industries.