The Brace ([#1, #7, #9, #18, #20, #21, #22, #30, #26, #27, #25, #29, #24, #31, #10, #4, #33, #34, #35, #37]) is a research work aimed for dynamic security and reliability analysis for IoT. It is an easy-to-use runtime verification middleware for dynamic analysis that can identify violation of formally specified safety and liveness properties.  It integrates well with real-time simulation platforms (e.g., LabView and MatLab) and sensing middleware [#30, #26, #27, #4] to provide runtime verification not only in physical deployment environment but also in highly faithful simulation environment.  The work has attracted great interest from a few well established corporations in IoT and autonomous CPS.  There is ongoing collaboration discussion with a large public listed company in China  to provide safety guarantee for their fleet of autonomous vehicles. The department of Automotive engineering in Tsinghua , which is responsible for autonomous bus project in Beijing 2022 winter Olympics,  has invited the group leader Dr. James Xi Zheng to give a school level talk in Tsinghua 22nd Dec 2017 and the department has expressed strong interest to collaborate with Macquarie and Australian universities to collaborate on the security and reliability analysis of autonomous vehicles starting from collaborating to extend this work.







The SmartVM ([#2, #3, #8, #13, #14, #17, #19, #32, #38]) is an innovative development and deployment research work specifically designed for Microservice.  The work originates from business requirement from a public listed software company in China specialized in delivering restaurant software as a service to solve some challenging research questions in adopting Microservices architecture for Cloud and Fog computing paradigm.  Since 2016, we have collaborated with the corporation to solve a few challenges in Microservice deployment and we are bringing at the moment formal methods and some state of the art testings techniques to bridge more research challenges unveiled during the project.  The director of the corporation has expressed strong interest to send their architects and lead developers to Macquarie to conduct research with us side by side.  Similarly, the cloud and fog computing research teams in another big public listed company based in ShenZhen expressed strong interest to work with us to collaborate on the research work to address those research challenges in fog computing paradigm.  The solutions to these research challenges, in a nutshell, are to extend the work to provide optimized Microservices which are both efficient and scalable to cater for data processing algorithms and deep learning models.




The HealthWombat ([#15, #36]) is a research work specifically designed to improve the state of the art in providing health solutions for elder people and addicted people. At the moment, we have developed a prototype wristband with accelerometer, a few innovative feature selection algorithms, a few experimental mobile applications to detect drinking and smoking behavior.  I have been invited to give talks in Inner Mongolia, ShenZhen, Shanghai, Beijing, and USA for various corporations and Venture Capital investors by giving a demo of our current research prototype. There is strong interest from some industry jewelry designers to use our existing work to embed sensors onto rings and bracelets, and some real estate companies also expressed the interest to use our innovation to monitor elder person’s health situation. We are currently experiencing deep learning models (e.g., CNN and RNN) to improve the accuracy of subtle activity recognition at real time in real-world settings. If funded, the experience learned from the project can also benefit for the development and commercialization of this research work.