The 6G networks envision, among other things, ubiquitous global network coverage i.e., coverage of even remote areas in the world. Unmanned Aerial Vehicles (UAVs) can be used to provide connectivity to the Internet of Remote Things (IoRT) devices which are deployed in remote areas for applications such as weather monitoring and fore-warning of calamities. The data collected from IoRT devices needs to be processed to extract useful information from it and relevant output data, apart from control information, may have to be sent back to the IoRT devices. To handle the processing of data collected from IoRT devices in remote areas, various architectures have been proposed in the literature using UAVs. UAVs have been used to provide Mobile Edge Computing (MEC) services to the IoRT devices either by placing an MEC device in the UAV or by placing an MEC device in the Low Earth Orbit (LEO) satellite and using UAVs as relays. Since the UAVs are energy-constrained devices, sending all the data to satellite is prohibitively costly. Similarly, carrying MEC device and performing computations at UAV consumes a large amount of energy, especially in applications involving image or video processing, which results in reduction in flight time and frequent trips to the charging station. Hence, we propose placing the MEC device on the ground, to which the UAVs relay all the data. The computation is performed at the MEC device and the results are sent back to the IoRT devices with the help of UAVs. As the UAVs send data only to the MEC device installed on the ground rather than to a satellite and the computation is done at MEC device rather than on the UAV, the energy consumption of UAV is reduced significantly. In our work, we provide the energy models for the three possible locations of MEC placement and compare them using simulations. We then optimize the connection scheduling, power control, bit transmission scheduling, bandwidth allocation, and trajectories of the UAV for MEC placement on the ground with the joint objective of minimizing the energy consumption and maximizing the system throughput. With detailed simulations, we demonstrate the effectiveness of our proposed solution.