Abstract: A data processing solution for data generated by autonomous vehicles, unmanned aerial vehicles (UAVs), and other Internet of Things (IoT) devices may include a network of edge devices and one or more cloud servers to gather and process data. Initially, all data generated by the edge devices may be gathered, transported, and processed by the cloud server(s) to train machine learning models. The cloud server(s) may push machine learning (ML) models to the network of edge devices. Using the ML models running on the edge devices, the data gathered by the individual edge devices may be filtered by its relevance score, which correlates to the impact the data would have as training data. The relevance score is used by the edge device to identify the data to upload to the cloud.

Abstract: A location identifying solution may use a pre-existing infrastructure of smart hubs and smart devices on user's properties to provide location beacons. An autonomous delivery vehicle may request a location signal from the server when nearing a destination for an order. The server may identify a smart hub with connected devices (e.g., light bulbs, speakers, etc.) associated with the user originating the order. The server may generate and transmit beaconing information to the hub. The hub may cause the devices to output a beacon (e.g., to flicker light at a frequency that is undetectable to human eye). Using a computer vision algorithm, the vehicles could detect the beacon and determine the correct property.