QoS-Aware Machine Learning Task Offloading and Power Control in Internet of Drones

Internet of Drones (IoD), where drones act as the Internet of Things (IoT) devices, provides multiple services, such as object recognition, traffic monitoring, and disaster rescue. The service response time is limited by drones' onboard computing power, hence greatly affecting the Quality of Service (QoS). Machine learning [(ML), e.g., image processing] task offloading to the fog node attached to the ground base station can help reduce workload from drones and hence decrease service time. The communication latency between drones and the fog node during task offloading also affects the service time and hence the communication efficiency needs to be considered. Therefore, in this work, we consider the joint optimization of ML task offloading and power control in IoD to determine each drone's number of offloaded images and wireless transmission power. We analyze the energy model of the commonly used convolutional neural network (CNN) for object recognition, and formulate the joint optimization problem as a mixed-integer nonlinear programming (MINLP) problem to minimize drones' average service time constrained by drones' energy budgets. An approximation algorithm with low computational complexity is then designed to address the problem and its performances are compared with the lower bounds and demonstrated via extensive simulations.