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Smart Cities

How Is External Data Used in Smart Cities?

Smart Cities rely on external data: proprietary, public, and often open-source. In addition to general data categories like the weather and the census, smart cities use data from a complex network of sensors and devices that provide highly localized information on movement, utilities, and more. CCTV and acoustic sensors, for example, identify criminal behavior. Meanwhile, underground sensors placed beneath parking lots provide information on parking availability. IoT device-equipped cars communicate with each other about their location, acceleration, and more to reduce the chance of accidents. The examples go on.

How are Machine Learning Models Used in Smart Cities?

Smart cities need machine learning programs to function: firstly, because no human can analyze the sheer amount of data (even de-centralized)—especially not in time to resolve emerging problems. Secondly, these programs identify patterns—of criminal activity, of electricity usage, etc.—which city planners can use to improve that aspect of life.

Which Companies Lead the Way in Building Advanced Analytics and Machine Learning Products in This Field?

Data Alliance provides crowd-sourced block-chain IoT data for governments, municipalities, and others. In less than a year since its inception, it became a Gold Member of the Open Connectivity Foundation.
Commscope offers a wide range of sensors (fiber, cellular, WiFi, converged network management, etc.) to run a smart city. They also offer the services of consultants, local distributors, and other professionals who can help set up and manage the smart city ecosystem.
Siemens Smart Infrastructure focuses on providing industrial services to smart cities. In particular, they provide electricity and major smart power grid system services.

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