The German company said that the smart city hub will be powered by Siemens’ cloud-based IoT OS MindSphere
Hong Kong Science and Technology Parks Corporation (HKSTP) has signed an agreement with Siemens for the establishment of a smart city digital hub in Hong Kong Science Park.
The smart city hub will initially consist of a smart mobility testbed and a smart energy lab to accelerate the development of smart city related digital analytic applications.
The hub will be powered by MindSphere, an open cloud-based IoT operating system developed by Siemens with data analytics and connectivity capabilities.
“The smart city digital hub is expected to ramp up the R&D of smart city technologies developed by companies in Hong Kong Science Park. We welcome more technology corporations of all sizes to work with us in enhancing the ever-growing collaborative power of our ecosystem,” said Albert Wong, CEO of HKSTP.
“The agreement reconfirms Siemens’ intention to build up Hong Kong as a smart city and develop key partnerships among industry leaders. Certain challenges faced by Hong Kong today can be solved through digitalization,” said Eric Chong, president and CEO of Siemens Hong Kong and Macao.
Industrial Internet Consortium launches smart factory machine learning testbed
In related news, the Industrial Internet Consortium (IIC) has announced the smart factory machine learning for predictive maintenance testbed.
The IIC said that the testbed is led Plethora IIoT and Xilinx. This testbed explores machine-learning techniques and evaluates algorithmic approaches for time-critical predictive maintenance, the IIC explained.
“Testbeds are the major focus and activity of the IIC and its members. We provide the opportunity for both small and large companies to collaborate and help solve problems that will drive the adoption of IoT applications in many industries”, said IIC executive director Richard Mark Soley. “The smart factory of the future will require advanced analytics, like those this testbed aims to provide, to identify system degradation before system failure. This type of machine learning and predictive maintenance could extend beyond the manufacturing floor to have a broader impact to other industrial applications.”
