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CriticalRiver Helps Municipal Authority Revolutionize Street Lighting with IoT Solutions

  • The Customer

    The Customer

    A municipal authority aiming to optimize street lighting based on traffic and environmental conditions for cost and energy efficiency.

  • The Challenge

    The Challenge

    3D-Priniting-Challenges-Img.jpg

    To reduce the power consumption of streetlights by dynamically adjusting their intensity based on the actual traffic presence and conditions on the street.

  • The Solution

    The Solution

    • Developed an IoT-based solution that gathers real-time data from parking sensors, CCTV video feeds, and business application data.
    • Utilized data science to generate descriptive and prescriptive analytics, providing insights to adjust lighting intensity. This was based on real-time data from light sensors, motion detectors, and live weather conditions.
  • Solution Components

    Solution Components

    Parking sensors, CCTV systems, Business application interfaces, Light sensors, Motion detectors, Data analytics tools.

  • The Results

    The Results

    • Enabled the system to identify and predict traffic volumes on streets, allowing for appropriate lighting adjustments.
    • Automated dimming and brightening of streetlights based on pre-established triggers and conditions.
    • Achieved a significant reduction in power consumption, with savings exceeding 15%.

The Customer

A municipal authority aiming to optimize street lighting based on traffic and environmental conditions for cost and energy efficiency.

The Challenge

3D-Priniting-Challenges-Img.jpg

To reduce the power consumption of streetlights by dynamically adjusting their intensity based on the actual traffic presence and conditions on the street.

The Solution

  • Developed an IoT-based solution that gathers real-time data from parking sensors, CCTV video feeds, and business application data.
  • Utilized data science to generate descriptive and prescriptive analytics, providing insights to adjust lighting intensity. This was based on real-time data from light sensors, motion detectors, and live weather conditions.

Solution Components

Parking sensors, CCTV systems, Business application interfaces, Light sensors, Motion detectors, Data analytics tools.

The Results

  • Enabled the system to identify and predict traffic volumes on streets, allowing for appropriate lighting adjustments.
  • Automated dimming and brightening of streetlights based on pre-established triggers and conditions.
  • Achieved a significant reduction in power consumption, with savings exceeding 15%.

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