C2RO NEWS | MIT Senseable City Lab used C2RO Platform
MIT Senseable City Lab used C2RO Platform to Showcase the Efficiency of Cloud-Based Services for Processing Sensor Data Streams
MIT Senseable City Lab (SCL) implemented an experimental sensor data stream using C2RO’s cloud platform. This experiment has been done in context of one of the recent projects of SCL that is aiming to equip the single-purpose streetlight with various types of sensors in order to create a multifunctional digital platform and provide services like pedestrian detection in modern cities.
In this data streaming experience, thermal cameras were used, which have enormous potential for data insights because of their ability to detect pedestrians even in various light and weather conditions, without compromising individual privacy. However, in the past, turning thermal data into information on pedestrian traffic has been a challenge because of the processor-intensive task of sensory analysis. But now, C2RO’s platform eliminates the need for expensive on-camera processing power by moving the heavy lifting to the cloud, thereby significantly reducing implementation costs and expanding commercial uses of the technology.
“The Internet of Things (IoT) will soon change the way we live and work in the cities. As the number of connected things increases, we would need better ways to process and organize the huge amount of data generated by them,” said Dr. Amin Anjomshoaa, the Project Lead at MIT. “Cloud-based services such as C2RO’s platform are the perfect counterpart for IoT solutions which facilitate creating elaborated services for smart cities.”
C2RO’s cloud-based platform analyzes image feedback from the distributed thermal cameras to autonomously detect the presence of people. The C2RO dashboard displays image feedback in real-time, showing a constant count of humans in the frame and activity over time.
Collecting pedestrian traffic data at scale could have major implications for crowd control efforts, autonomous vehicles, and disaster relief, where the value of human detection would be measured in lives saved. The technology will be especially useful for urban planning professionals and emergency monitoring systems, which can more easily glean information about metro areas.
“This application showcased the performance and scalability benefits of the integration with our ready-to-use SaaS platform for any large-scale robotics or sensor-based projects,” said Dr. Soodeh Farokhi, Founder and CTO at C2RO. “We enable customers to focus on the unique part of their products while offloading the computational burden to our scalable and secure cloud solution and saving up to ten times the cost of necessary hardware”.
C2RO grants a scalable Software-as-a-Service solution with almost no computation required in the device (sensor) side. Backed by a proprietary technology and 6 years worth of research, the real-time data processing technology allows for sensors spread evenly across the country to connect together, enhancing the accumulated insight of data collected. These analytics are augmented by Machine Learning modules running on the Cloud, allowing for a wide range of smart cities and device/robot collaboration use cases. C2RO’s beta product is expected to be released to a wider audience of users in the upcoming weeks. Check more information here.
With increasing demand for a more data-driven understanding of our future world, such urban projects are a major breakthrough in learning more about our diverse and populous metropolitan areas.
C2RO Robotics offers a SaaS platform that uses real-time data processing technologies to enable artificial intelligence software solutions for any sensor/ device/ robot in a fast, secure, scalable and inexpensive manner. It augments the perception and collaboration capabilities of devices even those low-cost with limited sensing and computation resources. You can visit C2RO online at c2ro.com, and sign up for C2RO’s product beta release, or find their recently accepted paper, “Real-Time Cloud Robotics in Practical Smart City Applications” at IEEE PIMRC 2017.