With the consolidation of the Internet of Things (IoT), the unmanned aerial vehicle- (UAV-) based IoT has attracted much attention in recent years. In the IoT, cognitive UAV can not only overcome the problem of spectrum scarcity but also improve the communication quality of the edge nodes. However, due to the generation of massive and redundant IoT data, it is difficult to realize the mutual understanding between UAV and ground nodes. At the same time, the performance of the UAV is severely limited by its battery capacity. In order to form an autonomous and energy-efficient IoT system, we investigate semantically driven cognitive UAV networks to maximize the energy efficiency (EE). The semantic device model for cognitive UAV-assisted IoT communication is constructed. And the sensing time, the flight speed of UAV, and the coverage range of UAV communication are jointly optimized to maximize the EE. Then, an efficient alternative algorithm is proposed to solve the optimization problem. Finally, we provide computer simulations to validate the proposed algorithm. The performance of the joint optimization scheme based on the proposed algorithm is compared to some benchmark schemes. And the simulation results show that the proposed scheme can obtain the optimal system parameters and can significantly improve the EE.

中文翻译：

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认知物联网的认知无人机辅助边缘通信的能效优化

随着物联网（IoT）的整合，基于无人机的物联网（IoT）近年来引起了很多关注。在物联网中，认知无人机不仅可以解决频谱稀缺的问题，而且可以提高边缘节点的通信质量。但是，由于生成了大量的冗余物联网数据，因此很难实现无人机与地面节点之间的相互理解。同时，无人机的性能受到其电池容量的严重限制。为了形成自主且节能的物联网系统，我们研究了语义驱动的认知无人机网络，以最大程度地提高能效（EE）。构建了认知无人机辅助物联网通信的语义设备模型。感应时间，无人机的飞行速度 联合优化无人机通信的覆盖范围以最大化EE。然后，提出了一种有效的替代算法来解决优化问题。最后，我们提供计算机仿真以验证所提出的算法。将基于该算法的联合优化方案的性能与一些基准方案进行了比较。仿真结果表明，所提方案能够获得最优的系统参数，并能显着提高EE。