FogHorn Systems, a start-up based in Mountain View, California, announced a collaboration with Google Cloud IoT Core to simplify the deployment and maximize the business impact of Industrial IoT (IIoT) applications.
FogHorn has built a complex event processing (CEP) - driven edge analytics software for on-premises edge computing. The software has a very small footprint enabling it to deliver real-time analytics to resource-constrained edge devices such as PLCs, gateways and industrial PCs. FogHorn recently enhanced its CEP platform with a new "Lightning ML" edge machine learning solution that can be used to train and execute machine learning algorithms and other advanced data science models on streaming sensor data. FogHorn says this facilitates the creation and iterative enhancement of “digital twins” and other sophisticated machine learning and AI models without the need to send all the sensor data to a cloud or data center for processing.
Under the partnership with Google, FogHorn’s edge analytics and machine learning platform will be integrated with Google Cloud IoT Core, which is a fully managed service that for connecting, managing, and ingesting data from globally dispersed devices.
“Cloud IoT Core simply and securely brings the power of Google Cloud’s world-class data infrastructure capabilities to the IIoT market,” said Antony Passemard, Head of IoT Product Management at Google Cloud. “By combining industry-leading edge intelligence from FogHorn, we’ve created a fully-integrated edge and cloud solution that maximizes the insights gained from every IoT device. We think it’s a very powerful combination at exactly the right time.”
"Our integration with Google Cloud harmonizes the workload and creates new efficiencies from the edge to the cloud across a range of dimensions,” said David King, CEO at FogHorn. “This approach simplifies the rollout of innovative, outcome-based IIoT initiatives to improve organizations’ competitive edge globally, and we are thrilled to bring this collaboration to market with Google Cloud.”
FogHorn raises $30M for industrial IoT edge computing
FogHorn has built a complex event processing (CEP) - driven edge analytics software for on-premises edge computing. The software has a very small footprint enabling it to deliver real-time analytics to resource-constrained edge devices such as PLCs, gateways and industrial PCs. FogHorn recently enhanced its CEP platform with a new "Lightning ML" edge machine learning solution that can be used to train and execute machine learning algorithms and other advanced data science models on streaming sensor data. FogHorn says this facilitates the creation and iterative enhancement of “digital twins” and other sophisticated machine learning and AI models without the need to send all the sensor data to a cloud or data center for processing.
FogHorn's “edge intelligence” software targets industrial and commercial IoT application, such as complex machinery packed with sensors. For performance and cost reasons, FogHorn argues data from industrial equipment mostly should be processed locally and not sent to a distant cloud. On-premises computing provides better latency for near real-time feedback. It can also minimize the volume of data to be uploaded to the cloud. FogHorn's software is being used by OEMs and systems integrators. The company is also working directly with end customers in manufacturing, oil and gas, power and water, transportation, renewable energy, mining and agriculture, as well as Smart Building, Smart City and connected vehicle applications.
The new funding round was led by Intel Capital and Saudi Aramco Energy Ventures with new investor Honeywell Ventures and all previous investors participating, including Series A investors March Capital Partners, GE Ventures, Dell Technologies Capital, Robert Bosch Venture Capital, Yokogawa Electric Corporation, Darling Ventures and seed investor The Hive. The company has raised $47.5 million to date.
