APIs Open Up Secure Shared Connections
APIs make it possible for these sensors to run together, seamlessly and more securely connecting formerly separate devices. For example, data from a smart traffic camera manufactured by one company can be shared with the information being collected by connected vehicles.
Connecting these seemingly disparate assets creates a mesh that can help cities improve everything from traffic flow to air quality and pedestrian safety.
Using enterprise open standards, modern APIs help municipalities to bring together existing investments in proprietary cameras and sensors while positioning themselves to accept new endpoints, regardless of the manufacturer.
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Rules Engines Lead to Smarter Decision-Making
Finally, rules engines deployed within containerized applications that live close to sensors determine which information to process locally and what should be sent to the centralized cloud or agency data center.
The more data that can be processed at or near the edge, the faster the decision-making can occur. Expenses are reduced too, since edge processing helps agencies to avoid excessive ingress and egress costs incurred by transmitting data to central processing centers.
Perhaps even more important, the less data needs to travel, the more resilient the overall ecosystem becomes. For instance, a self-driving car needs to locally process data using artificial intelligence algorithms to make a real-time decision to stop before it hits a jaywalker. It’s like building up human muscle memory through repeated practice and training to catch a Frisbee or hit a curveball.
Conversely, machine learning models may be used to determine smarter ways to lower overall vehicle maintenance costs, improve safety, and increase overall customer and employee satisfaction.
For example, telemetry information from city buses might be sent to the cloud to feed apps that inform customers about when the bus will arrive at their stops. Telemetry information collected over time paired with vehicle weight sensor data may be analyzed using centralized machine learning models to provide better insight into when a bus needs preventative maintenance.
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A Standard, Open Infrastructure and Future Possibilities
Ideally, each of these technologies should be supported by a common enterprise open-source infrastructure. This allows for better interoperability among different types of sensors. It will also provide IT professionals with a standard and consistent framework upon which to build and iterate over time.
Inherently modular components save agencies from having to make wholesale changes to their IT infrastructure every time they add a new tool to their toolboxes. They can add new features and applications incrementally and with less need for rework, expediting development while keeping costs low.
These baseline technologies set the stage for endless future possibilities, both large and small. Imagine having the ability to manage and monitor from a central location thousands of connected stoplights spread across a city. Imagine providing a state department of motor vehicles with the ability to modernize incrementally without the need to make substantial and costly changes to its IT environment.
State and local agencies must keep thinking about these future opportunities and move on from outdated technologies and thinking while continuing to take care of the present. Agencies should not throw away this shot at hitting all their infrastructure goals.
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