AI at the Edge: Kinnami & University of Nebraska Showcase Real-Time Infrastructure Monitoring Supporting Responders in Austere Environment

On July 28–29, 2025, Kinnami participated in a successful two-day field demonstration in Sarpy County, Nebraska, alongside academic and research partners from the University of Nebraska–Lincoln, the University of Nebraska at Omaha, and the University of New Hampshire. The demonstration was part of the SMART-RDF (Smart Analytics for Critical Infrastructure inside a Resilient Data Fabric) project, sponsored by the U.S. Army Corps of Engineers Engineering Research and Development Center (USACE ERDC).

At the center of the demonstration was AmiShare—our resilient data fabric—Kinnami’s trusted data platform that enables secure, resilient, and real-time collaboration between AI-enabled edge devices—especially in mission-critical environments where infrastructure is at risk and bandwidth is limited or unreliable. The event served as a real-world validation of how AmiShare and its integrated architecture can help modernize critical infrastructure assessment through autonomous sensing, data fusion, and secured communication across distributed teams.

Why Edge Data Matters More Than Ever

Infrastructure monitoring is no longer limited to scheduled inspections or static sensors. In disaster-impacted zones—such as flood-prone areas or post-earthquake regions—first responders and engineers must evaluate the condition of critical assets in real time, with limited access to connectivity and under time-sensitive conditions. Edge AI devices, such as drones and contact sensors, offer a powerful means to collect and analyze this data at the source.

However, the challenge isn’t just collecting data—it’s securing and managing it under extreme conditions. Corrupted or lost data can result in duplicated efforts, flawed assessments, or delayed emergency response. Repeating expensive R&D experiments, particularly in complex environments like bridge inspections in disaster zones, can waste critical time (and budgets) and potentially put lives at risk.

This is where Kinnami’s AmiShare platform provides unique capabilities. Designed to operate in resource-constrained environments at the computing edge, AmiShare ensures that every byte of collected data is securely stored, reliably transmitted to authorized devices, and immediately usable—without relying on stable networks or a cloud connection. AmiShare brings resilience, agility, and trust to the edge.

A Real-World Use Case: Post-Flood Route Planning

One of the most compelling scenarios demonstrated during the Nebraska event was centered on emergency route planning in a post-flood environment.

In this scenario, “Alice,” a U.S. Army Corps of Engineers (USACE) route planner, is evaluating transportation options across a flood-damaged region in Nebraska. One critical link in her potential route is an old steel truss bridge, whose structural integrity is unknown due to recent flooding. Alice doesn’t have the ability to physically climb onto the bridge for inspection. Instead, she relies on a drone equipped with a high-resolution camera and an on-board Jetson Nano running light-weight AI models and enhanced with AmiShare.

The drone captures high-resolution imagery and performs AI-powered defect detection on corroded bridge joints and other structural elements. Lightweight AI models optimized for edge deployment run in real-time using a Jetson Nano module, flagging potential failures in the steel structure. Additionally, contact sensors installed on key structural components monitor dynamic strain under simulated loading.

All the data—imagery, sensor readings, AI inferences—is secured, fragmented, and encrypted by AmiShare, then transmitted to a ground station in real time. Should the primary network fail, AmiShare’s network-agnostic architecture seamlessly switches to an alternate network. Data is stored locally on the drone as well, ensuring redundancy (a secure backup) even if transmission is disrupted.

What is significant is that this data is not only collected—it is instantly actionable. Within seconds, Alice can visualize the drone’s findings, identify compromised joints, and make informed decisions on whether to approve the bridge for supply transport. She can then share her decision-making rationale, supported by encrypted data, with FEMA, NDOT, and other mission stakeholders.

Technology in Action

The live field testing conducted on an operational steel truss bridge brought together a suite of edge-AI and secure data technologies, including:

• Drone-mounted non-contact imaging systems for corrosion assessment via AI-enabled vision models.

• Jetson Nano modules running lightweight, real-time AI models tailored for SWaP (Size, Weight, and Power) constraints.

• Strain sensors deployed to collect contact data during dynamic load testing, enabling deeper analysis for structural modeling and health monitoring.

• AmiShare’s encrypted data fabric, which fragments, replicates, and encrypts each file for maximum security and redundancy—ensuring data survives the edge.

• Field routers to create low-latency, resilient mesh networks between devices, the ground station, and cloud endpoints (when available).

Together, these technologies created a fully autonomous inspection workflow that required no permanent infrastructure, making it ideal for use in emergency scenarios, military operations, or remote industrial monitoring.

Why Resilience Must Be Designed in, Not Added Later as an Afterthought.

Too often, resilience is treated as an afterthought. Teams conduct critical field tests or emergency operations and only afterward recognize that a corrupted hard drive, dropped connection, or faulty sensor compromised their data. At that point, the cost of redoing the operation is often formidable and time is a critical factor—the window for informed decisions may have closed.

With AmiShare, resilience is designed into the architecture. From redundant storage on the drone (and any other device in the distributed system), to intelligent failover networking, to encryption at the source and authentication of every node, the system is designed to ensure that data is never lost, never exposed, and always accessible to users on authorized devices—even if something goes sideways.

This built-in resilience saves time, money, and lives. It eliminates the need for costly test re-runs and empowers frontline personnel to act with confidence—because the data they rely on is trustworthy, complete, and securely delivered. AmiShare ensures that original data reaches its destination on time, every time, and only to authorized devices, even in degraded or disconnected environments.

A Blueprint for Infrastructure of the Future

This demonstration in Nebraska was more than a proof of concept—it’s a signal for what’s next in intelligent infrastructure monitoring. The collaborative work between Kinnami, academic research institutions, and federal agencies is setting the stage for a new generation of edge-based, AI-enhanced, resilient data platforms.

Whether it’s bridge inspections, disaster response, or mission-critical military logistics, the SMART-RDF framework powered by AmiShare shows how we can securely collect, analyze, and share data anywhere it’s needed, without sacrificing speed or security.

For teams operating in remote, degraded, or hostile environments, redoing a test is not an option. You need technology that performs the first time—intelligently, securely, and autonomously at the edge. This is what Kinnami AmiShare uniquely delivers.

A special thanks to our partners the University of Nebraska–Lincoln, the University of Nebraska at Omaha, the University of New Hampshire and USACE ERDC including, Dr. Robin Gandhi, Dr. Daniel Linzell, Dr. Ji Young Lee, Dr. Kwangsung Oh, Dr. Mubarak Abu Zouriq, Dr. Rola El-Nimri, John Helzer and Dr. Mihan McKenna-Taylor, and Kinnami team members Dr. Teresa Davies and Jim Burke!