Professor Celestine Iwendi

Unmanned aerial vehicles (UAVs) with AI-enabled logistics are gradually demonstrating their special benefits for upcoming smart cities. However, current research on logistics UAV path planning fails to take into account the limits on UAV energy consumption, customer time windows, and the effects of wind direction and speed at the same time. As a result, we study how wind direction and speed affect UAV flight states, determine relevant parameters and how wind conditions affect them, and explore the logistics problem of UAV path planning that simultaneously takes into account the constraints on UAV energy consumption, customer time windows, and wind conditions [1]. The ubiquitous monitoring and intelligent control capabilities of the Internet of Things (IoT) are mainly dependent on inexpensive wireless sensors with low energy consumption. Nevertheless, remote terminals that are not covered by wireless can be connected to IoT networks using unmanned aerial vehicles (UAVs). With the help of this solution, IoT networks may reach a wider audience and have more options for control and monitoring. Notwithstanding this advantage, the UAV’s onboard battery has a modest capacity [2].