Dr. Wu Chou
As global urbanization trends continue, more people will move to the city. It is estimated that by the year 2050 more than 75% of the world population will live in cities. As a result, more streetlights will be needed to keep pace with expansion of a city and to provide ubiquitous lighting coverage for the population. The first challenge will be the huge amount of energy consumption. Street lighting consumes a good part of the 19% of power currently used for all lighting with a carbon footprint corresponding to burning billions of trees. Saving power for lighting has been an inspiration for technical advances and innovations and we have been advancing rapidly from incandescent, fluorescent, and high-pressure sodium lights to LED lighting.
There is also a strong demand for better and smarter cities with high quality and intelligent services. Similar to the advancement of lighting, technical advances and innovations have led to the era of Internet-of- Things (IoT) to enable smart cities. One fundamental feature of IoT is to connect the digital world, such as Internet, with the physical world, such as sensors, cars and lights. IoT offers tremendous opportunities for new services with improved efficiency and intelligence including street lighting.
In fact, the combination of IoT + LED lighting opens a new paradigm in street lighting. A fully connected lighting network offers more intelligent lighting control through which a reduction of energy consumption of up to 80% as well as cost reductions in operation can be achieved. The light can be dimmed and lit up instantly depending on the needs and the energy saving policy/algorithm being deployed. By integrating a GIS (geographic information module) component that provides precise geographic information, the status and well being of every single LED light can also be monitored. This information can be used to monitor the operation status, but also for (predictive) maintenance needs. Operation costs can be reduced by up to 90% while significantly lowering the down time of the light and improving the service quality and user experience.
It is a huge challenge to construct an IoT platform with good coverage of the city, but the emergence of IoT+LED based connected street lighting can work miraculously for smart cities. In addition to an IoT gateway connection, streetlights can be interconnected though an RF mesh network (e.g. 6LowPAN: IPv6 over low power wireless area network). This RF mesh network is extensible and ad hoc with self-healing capabilities. Today, more than 30 types of sensors can be deployed on this connected street lighting IoT platform for smart city services, including environmental services (air quality, traffic flow, etc.), city parking, garbage bin and manhole monitoring, security, etc.
Furthermore, a fully connected street lighting IoT platform makes the lighting poles increasingly valuable. They can be used for WiFi or small cell stations to provide wireless coverage of the city, for roadside charging of electrical cars, and even for advertisement applications with LED displays that can be mounted on the poles. As we enter the era of IoT, these applications are just the tip of the iceberg that the IoT+LED based connected street lighting IoT platform can provide. But most importantly, IoT+LED has become reality for lighting up smart cities.
Dr. Wu Chou
He received his Ph.D in Electrical Engineering from Stanford University. He worked for Bell Labs (AT&T/Lucent), Avaya, and joined Huawei in 2011 as Chief IT Scientist and head of Huawei Shannon (IT) Lab. He became the CTO of the Enterprise Network Product Line in 2015. He has published over 200 journal and conference papers and holds more than 40 US and international patents. Dr. Chou is an IEEE Fellow and worked as an editor and area expert for multiple standard bodies like ISO, W3C, ECMA and ETSI. He has also served as an editor for IEEE Transactions (C, AL) IEEE TSC Special Issue on Cloud computing, IEEE Transactions Special Issue on Human-machine Interaction. In addition to all that, Dr. Chou has served as a member of multiple advisory boards like that of MIT Computer Science and Artificial Intelligence Lab (CSAIL) Big Data Program and the New Jersey Institute of Technology (NJIT).
