| Year | 2019/20 |
| School / Department / Unit | Supply Chain and Information Management |
| Funding Scheme/Source | PCCW Global Limited and Research Matching Grant Scheme (RMGS) |
| Project Title | Smart Space - Intelligent Smart Cities Management and IoT Analytics Platform |
| Project Team (HSUHK Staff) | Dr George HO (PI) Dr Collin WONG(Co-PI) Dr Jack WU (Co-PI) |
| Project Period | 2019-08-01 to 2023-09-30 (On-going) |
| Funding Amount | $4,455,278 and $3,350,728.10 (RMGS) = $7,806,006.10 |
| Other Collaborating Parties | PCCW Global Limited |
| Abstract | This research initiative aims to design IoT and big data analytics-based system architecture for facilitating the network in the smart city. With the global aging population projected to swell by nearly 2.5 billion between now and 2050, it is necessity to create the cities that are aware of the special needs of all citizens including the needs of aging population. In smart cities, the citizens activities are not limited to their home and they live their lives in an entangled community (Cocchia, 2014; Skouby et al., 2014; Suzuki 2017). Therefore, smart cities need to address the needs of citizens across various areas such as housing, social participations health care, transportation and community support services in order to improve the quality of life of citizens. In the last decade, artificial intelligence (AI) and the Internet of Things (IoT) are classified as two independent operational technologies that cities are using to prepare for the influx of new citizens. While IoT is treated as the digital nervous system, AI becomes the brain that facilitates decisions for controlling the components involved in smart cities. These technologies have brought improvements in the lives of the citizens. In the healthcare aspect, Suryadevara and Mukhopadhyay (2012) developed a home monitoring system using the wireless sensor devices for determine the status of the elderly on performing essential daily activities. Pramanik et al. (2017) proposed the use of the AI for improving the understanding of diseases and facilitating the decision making in diseases diagnosis. In the transportation area, Sherly and Somasundareswari (2015) proposed a smart transportation system with the use of IoT to monitor and control the traffic problem. Chatzigiannakis et al. (2016) adopted IoT to design a privacy-preserving smart parking system so as to provide a solution that protect the privacy of the users. However, due to the limitations of AI, such systems can only function within the framework specified by the humans which do not self-correcting and self-healing abilities. In addition, the sensor devices equipped in the IoT environment only use for collecting and exchange the data without the ability for generating real-time and rapid response. This may bring the negative impact on smart devices that are getting smarter, downplaying their advancements. Therefore, an emerging technique of Artificial Intelligence of Things (AIoT) is introduced to overcome the above limitations. AIoT is transformational and mutually beneficial for both types of technology as AI adds value to IoT through machine leaming capabilities and IoT adds value to AI through connectivity, signalling data exchange. With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data for providing rapid decisions using machine leaming algorithm. The applications of AIoT opens the door to a wealth of use cases like the regulation of utility grids, more insightful transportation systems, and buildings that can predict and deliver on the needs of residents and workforces in the smart city. While the concept of AIoT is still relatively new, research in this area is still rare. Therefore, in this research, there are three objectives: (i) to design and develop urban infrastructure with the use of artificial intelligence of things (AIoT) in smart city to improve the IoT operations and human machine interactions, (ii) to build an open ecosystem for public to access and enhance data management for analytics, and, (iii) to apply the latest AIoT technology in the smart city applications. In summary, establishing the state-of-the-art urban infrastructure using AIoT for supporting and powering the networks in smart city is a significant step toward success, strengthening the integration of every component in smart city seamlessly. With the support by the PCCW Global Limited, important elements for constructing the smart cities infrastructure can be defined and identified through conducting the site survey and discussion with professionals and operation staff. In addition, the designed infrastructure can be implemented to validate its feasibility. The principal investigator of this research, Dr. George T.S. Ho, conducted related studies on the various components of smart city including smart transport (Ho et al., 2019), smart health (Tang et al., 2019), smart logistics (Tsang et al., 2018; Wu et al. 2018; Choy et al., 2017). In this research, he aimed at exploring the application of AIoT in smart city for providing proactively responds to the needs of citizens. |