What You Need to Know, on the Go

As part of the rise in the Industrial Internet, trains and tracks, buses, aircraft, and ships are being equipped with a new generation of instruments and sensors generating petabytes of data that require additional intelligence for analysis and real-time response. FogHorn’s Edge AI platform processes this data locally to enable real-time asset monitoring and management to minimize operational risk and downtime. It can also be used to monitor and control engine idle times to reduce emissions, conserve fuel and maximize profits.

Use Cases


Optimize systems for maximum KPI performance, quickly adjust to changing conditions.


Lower maintenance costs, improve asset performance and longevity, and maximize yield.


Reduce repair costs, minimize downtime, maximize output, and improve remediation efficiency.

Asset Performance

Maximize high-value asset output, balance reliability, performance, and cost.

Energy Usage Optimization

Maximize high-value asset output, balance reliability, performance, and cost.


IoT is rapidly transforming commercial and public transportation—perhaps even more than most other major industries. While the most prominent applications are GPS solutions that drivers use for navigation and fleet managers use for vehicle tracking and scheduling, there are many other applications that will define the future of connected transportation systems.

IoT-based solutions provide many opportunities for optimizing the efficiency of fleet operations. These include collecting and analyzing large amounts of information that can provide actionable data related to fuel consumption, repair histories, predictive maintenance, asset utilization and other operational considerations. While the most advanced functions such as fully autonomous vehicle control are years away, there are many existing IoT applications that are available now to enhance the efficiency, reliability and safety of commercial and public transportation. These include vehicle control and safety systems such as cameras, driver assistance and collision avoidance functions that are being added to new vehicles every year.

FogHorn’s edge intelligence platform, brings high performance processing directly inside fleet vehicles for critical command and control decisions and to minimize the cost and latency associated with uploading huge amounts of data to remote data centers. Its extremely small footprint makes it possible to provide a full range of real-time, industrial-strength analytics and machine learning capabilities using a vehicle’s existing onboard processors.

The FogHorn platform is built to operate in heterogeneous environments enabling simplified integration of a wide range of disparate systems from multiple vendors. In addition to supporting dozens of on-board edge systems, the platform can also upload selected data points to remote, cloud-based data centers for additional analysis and overall system optimization.

Reduce operation costs and improve safety by deploying real-time monitoring and diagnostics for trains and rail

Perhaps the most essential responsibility for train operators is to monitor train tracks, freight cars, and the wheels of the train to detect any problems that may lead to a derailment. As part of the rise in the Industrial Internet, trains and tracks are now being equipped with a new generation of instruments and sensors. Locomotives also include sophisticated hardware and software making them the central hub for all the data gathered from these sensors.

Rail companies can improve their operations and cut costs by analyzing all the data gathered from the trains and tracks to gain insight into their operation. Since trains are massive, fast-moving objects, the real-time nature of analysis and response is very critical but also challenging because reliable network connections are usually unavailable for continuous remote monitoring. As a result, railroads cannot rely on sending a large amount of data to the cloud for analysis and a timely response. FogHorn’s edge-based analytics and machine learning solution deployed into the locomotive’s gateway hardware can provide real-time analytics and predictive insights on rail operations. This enables faster responses to situations that can improve the safety and reliability of the trains and tracks as well as provide cost-saving measures that conserve fuel and increase safe operating speeds.