| CubeSat Cloud is a novel vision for a space based remote sensing network that includes a collection of small satellites (including CubeSats), ground stations, and a server, where a CubeSat is a miniaturized satellite with a volume of a 10x10x10 cm cube and has a weight of approximately 1 kg. The small form factor of CubeSats limits the processing and communication capabilities. Implemented and deployed CubeSats have demonstrated about 1 GHz processing speed and 9.6 kbps communication speed. A CubeSat in its current state can take hours to process a 100 MB image and more than a day to downlink the same, which prohibits remote sensing, considering the limitations in ground station access time for a CubeSat.;This dissertation designs an architecture and supporting networking protocols to create CubeSat Cloud, a distributed processing, storage and communication framework that will enable faster execution of remote sensing missions on CubeSat clusters. The core components of CubeSat Cloud are CubeSat Distributed File System, CubeSat MapMerge, and CubeSat Torrent. The CubeSat Distributed File System has been created for distributing of large amounts of data among the satellites in the cluster. Once the data is distributed, CubeSat MapReduce has been created to process the data in parallel, thereby reducing the processing load for each CubeSat. Finally, CubeSat Torrent has been created to downlink the data at each CubeSat to a distributed set of ground stations, enabling faster asynchronous downloads. Ground stations send the downlinked data to the server to reconstruct the original image and store it for later retrieval.;Analysis of the proposed CubeSat Cloud architecture was performed using a custom-designed simulator, called CubeNet and an emulation test bed using Raspberry Pi devices. Results show that for cluster sizes ranging from 5 to 25 small satellites, faster download speeds up to 4 to 22 times faster - can be achieved when using CubeSat Cloud, compared to a single CubeSat. These improvements are achieved at an almost negligible bandwidth and memory overhead (1%). 14. |
