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As the sixth generation (6G) network is under research, and one important issue is the aerial access network and terrestrial-space integration. The Internet of Remote Things (IoRT) sensors can access the unmanned aerial vehicles (UAVs) in the air, and low Earth orbit (LEO) satellite networks in the space help to provide lower transmission delay for delay-sensitive IoRT data. Therefore, in this article, we consider the LEO satellite-assisted UAV data collection for the IoRT sensors. Specifically, a UAV collects the data from the IoRT sensors, then two transmission modes for the collected data back to Earth: 1) the delay-tolerant data leveraging the carry-store mode of UAVs to Earth and 2) the delay-sensitive data utilizing the UAV-satellite network transmission to Earth. Considering the limited payloads of UAVs, we focus on minimizing the total energy cost (trajectory and transmission) of UAVs while satisfying the IoRT demands. Due to the intractability of direct solution, we deal with the problem using the Dantzig-Wolfe decomposition and design the column generation-based algorithms to efficiently solve the problem. Moreover, we present a heuristic algorithm for the subproblem to further reduce the complexity of large-scale networks. Finally, numerical results verify the efficiency of the proposed algorithms and the advantage of LEO satellite-assisted UAV trajectory design combined with the data transmission is also analyzed.
Jia et al. (Wed,) studied this question.