SBIR-STTR Award

One of the hardest realities of implementing IoT in the DoD is that so many defense assets already exist and are either dumb (no on-asset processing) or are unconnected (no wireless way to get data from the asset's processors to the outside world) or are a combination of dumb and unconnected. Our proposal is to deliver a "system architecture" capable of providing a secure, end-to-end, IoT System composed of an edge device module, network connection, device management, device authentication and device security, and the server-side tools to observe and manage that device and the assets to which it will be connected. The system architecture and proposed implementation methodology will be compatible with legacy equipment and on-going operational environments. While applicable to new acquisitions, our innovation is the ability to connect unconnected "things" in a legacy environment securely, safely, and cost effectively. Our architecture will deal with the design, development, manufacturing as well as the deployment, management and lifecycle (cradle to grave) management of the "system"Â

The System to be delivered will include the four basic components of an IoT System: (1) edge device module, (2) a network, (3) a data and device management core and a (4) set of tools for data analysis, visualization and a user interface. Contractor proposes to use COTS solutions for the majority of the System elements in order to focus resources on the technical innovations proposed within an operational context. As with all IoT platform solutions, the non-defense commercial solution being proposed by TIS3 is primarily used to connect “dumb”, “unconnected” assets and equipment to make said equipment/assets connected and smart. Connected, in that they become available -over a wired or wireless connection- to a cloud-based platform for data and device management. Smart, in that edge device module used to connect the asset or equipment has onboard processing capability which can be used for command and control, or simple machine learning tasks and in that the cloud platform has turnkey applications for command and control, deep learning and eventually AI-based use cases. The key System element for this proof of concept is the edge device module (EDM™), a small board that creates the interface and connectivity to the legacy equipment/assets and the backend, cloud-based