Optimization of the Internet of remote things data acquisition based on satellite UAV integrated network

In order to achieve dependable and efficient data acquisition and transmission in the Internet of Remote Things (IoRT), we investigate the optimization scheme of IoRT data acquisition under the unmanned aerial vehicle (UAV)-low earth orbit (LEO) satellite integrated space-air-ground network, in which the UAV acquires data from massive Internet of Things (IoT) devices in special scenarios. To combine with the actual scenario, we consider two different data types, that is, delay-sensitive data and delay-tolerant data, the transmission mode is accordingly divided into two types. For delay-sensitive data, the data will be transmitted via the LEO satellite relay to the data center (DC) in real-time. For delay-tolerant data, the UAV will store and carry the data until the acquisition is completed, and then return to DC. Due to non-convexity and complexity of the formulated problem, a multi-dimensional optimization Rate Demand based Joint Optimization (RDJO) algorithm is proposed. The algorithm first uses successive convex approximation (SCA) technology to solve the non-convexity, and then based on the block coordinate descent (BCD) method, the data acquisition efficiency is maximized by jointly optimizing UAV deployment, the bandwidth allocation of IoRT devices, and the transmission power of the UAV. Finally, the proposed RDJO algorithm is compared with the conventional algorithms. Simulation consequences demonstrate that the efficiency of IoRT data acquisition can be greatly improved by multi-parameter optimization of the bandwidth allocation, UAV deployment and the transmission power.