Visibility + Predictability = Smarter Cities
Integrating data from a diverse collection of municipal systems (e.g. street lighting, traffic information, parking, public safety, etc.) for interactive management and community access is a common vision for smart city initiatives. However, the sheer amount of data generated requires too much bandwidth and processing for cloud-based systems. FogHorn’s Edge AI platform provides a more effective solution that distributes data processing and analytics to the edges where sensors and data sources are located.
Minimize operating costs, improve energy efficiency, reduce environmental impact.
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.
Improving public safety, traffic conditions and energy savings through connected edge analytics
When smart cities convert their streetlights to use low energy LEDs they are not simply replacing existing lights with more efficient light bulbs. They are also investing in new IoT technologies that can add new embedded sensors for capturing a wide range of data such as video, audio, motion, ambient light, vibration and more. FogHorn’s edge intelligence solution can also provide precise geographical information that is important to a smart city solution that needs to know the locations of people, vehicles, wastewater, and other factors. Armed with IoT data, cities can improve public safety and security, energy efficiency, traffic management, and respond to ever changing environmental and weather conditions.
Since most use cases require real-time “here and now” actions, sending sensor data from street fixtures to a remote cloud server for analysis and processing would take too long and result in failures to respond due to end-to-end latency. FogHorn’s edge intelligence solution can provide a fast, real-time, location aware solution for many smart cities IoT use cases. In addition to being able to quickly ingest and analyze a wide range of video, sound, motion and other data, a proper edge analytics solution will also be able to meet small footprint hardware and software requirements due to the limited computing power and memory of most IoT systems.
And considering that large numbers of sensors will generate massive amounts of data in different data formats, the cost of sending all of the raw data to the cloud is huge and unnecessary. FogHorn’s edge solution can analyze the streaming data and send only relevant data to the cloud for further processing or integration with other backend systems.