UAV-Aided Delay-Oriented IoT Data Collection Strategies Using a Pattern Search Framework

The role of unmanned aerial vehicle (UAV) technologies in the Internet of Things (IoT) data collection is attracting tremendous attention due to its operational flexibility. To guarantee reliability and network efficiency of the IoTecosystem, delay is a crucial performance metric in a IoTdata collection scenario. To reap the benefits of UAVs, this paper investigates delay-oriented IoT data collection scenarios utilizing UAVs. To achieve low-delay data transmission, non-orthogonal multiple access (NOMA), which has high spectral efficiency, is employed. Considering the queue system of IoT, we formulate a problem that jointly optimizes UAV deployment and IoT transmit power to minimize the average packet delay of IoT nodes. As a novel strategy to solve the non-convex problem, a pattern search framework that can obtain near-optimal UAV deployment and IoT transmit power is proposed. Numerical results show that the optimal deployment of UAV depends on the traffic volume of IoT nodes to minimize delay. Furthermore, our proposed pattern search framework achieves significant delay reduction up to 85% compared to other benchmark schemes, highlighting its potential in the UAV-aided IoT data collection.