Defense & Public Sector

When Your Satellite Window Is 4 Minutes

The Navy's unmanned vessel program had a problem: their ML models needed updates, but the vessels were underwater. Satellite links cost $14/minute and dropped constantly. Cloud-based ML wasn't going to work.

4 min Avg Satellite Window

97% Fleet Update Rate

72 hrs Full Propagation

Client

U.S. Navy

Industry

Defense & Public Sector

Use Case

Edge ML Analytics for Unmanned Maritime Vessels

Products Used

Expanso, Mycelial Kafka Connector

Timeline

Initial vessel in 11 weeks, fleet rollout ongoing

ROI

Eliminated dependency on real-time connectivity

The Challenge

The unmanned vessel program was stuck. ML models trained on shore worked great - until you tried to update them on vessels operating in the Western Pacific. The existing approach required vessels to surface, establish satellite link, and maintain connection for the full update. Success rate was around 23%.

Satellite bandwidth runs $14/minute and connections drop mid-transfer
Vessels surface for 4-6 minutes on average before diving again
Failed updates meant vessels ran stale models for weeks
DoD security review for any new software takes 9 months minimum
Existing ML platform required constant cloud connectivity
Program was 14 months behind schedule on autonomy milestones

The Solution

We built a system that assumes the connection will fail. Model updates break into small chunks. Vessels grab what they can in each window. The orchestrator tracks what each vessel has and what it still needs. A full model update completes across 3-4 surface windows instead of requiring one long session.

Chunked Model Delivery

Model updates split into 200KB segments. Each chunk verifies independently. Vessels resume from last successful chunk - no wasted bandwidth on retransmission.

Fleet-Wide State Tracking

Shore command sees exactly which models each vessel has, when they last connected, and what updates are queued. Priority vessels get updated first.

Opportunistic Data Return

Vessels collect sensor data continuously. When they surface, high-priority data uploads first. Raw feeds compress and transfer during longer windows. Nothing gets lost.

The Results

The program caught up on its autonomy milestones. Model update success rate went from 23% to 97%. The 9-month DoD security review came back clean - they appreciated that we assumed hostile networks from the start.

97% Update Success

72 hrs Fleet Propagation

11 weeks First Vessel

0 Failed Transfers

Model update success rate jumped from 23% to 97%
Full fleet receives updates within 72 hours of release
First operational vessel deployed in 11 weeks
DoD security review passed without findings
Satellite costs dropped 34% - less retransmission, smaller payloads
Vessels now run 3 concurrent ML models instead of 1
Program recovered 14-month schedule slip in 6 months

"Our update success rate was embarrassing. Vessels would surface, start an update, lose connection, and we'd have to start over next time. Now they grab what they can, dive, and pick up where they left off tomorrow. Sounds simple. Took us two years to find someone who could build it."

Program Manager, Unmanned Maritime Systems

Deploying to disconnected environments?

If your edge devices can't maintain constant connectivity, we should talk. We've deployed on vessels, aircraft, and remote sites where the network is hostile by design.

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