Remote Sensing: Leveraging Cloud IoT and AI/ML Services

摘要

Artificial Intelligence/Machine Learning (AI/ML) services implemented at the tactical edge on multiple, distributed low power sensors can take advantage of Cloud IoT services and processes to learn in complex data environments supporting evolving mission tasks and continuous improvement of algorithms through Cloud automation and management. Remote sensors and their associated functionality must exhibit resilience against adversaries and deceptive techniques, and operate securely in all domains. Amazon Web Services (AWS) IoT Greengrass uses machine learning models that are built in the cloud and deployed locally on remote sensors and IoT devices. Limited datasets can be used to train models and be refined as more data is available. AWS SageMaker can be used for scene detection using Images and other signals of interest resulting in alerts and notifications from deployed sensors. The quality of machine learning models can be improved through captured data from IoT Greengrass being returned to the Cloud and processed by AWS SageMaker. Labeling of data can be streamlined through Cloud services such as Amazon SageMaker Ground Truth using auto-segment, automatic 3D cuboid snapping, and other automated labeling features. Using AWS Cloud security services in addition to AI/ML and IoT services provides additional advantages supporting identity and certificate management, sensor and data analytics, and intrusion detection and prevention. The number of low-cost sensors supporting Cloud-based IoT remote sensing using AI/ML algorithms at the edge continues to grow. Raspberry PI, NVIDIA Jetson Nano and others, equipped with appropriate sensor devices, are readily available for building a distributed remote sensing network and communicating with the Cloud. This paper provides a detailed description of components and the initial results of building a small distributed remote sensing network using NVIDIA Jetson Nano edge devices equipped with inexpensive acoustic and image sensors using IoT Greengrass and AWS SageMaker for detecting and identifying several target types. Additional Cloud services were used supporting monitoring and auditing, ensuring a secure and resilient operating environment.