•     Funded Research Projects



•     Research Students



•     Research Projects



          ◦    Smart Roads for Future Autonomous Vehicles

          ◦    Sensing and Communication Infrastructure for Intelliegent Connected Vehicles
       

          ◦    Harnessing Mobile Crowd Sensing for Smart Cities



          ◦    Designing Vehicular Networks for Intelligent Transport Systems



          ◦    Spectrum and Energy Efficient Design and Management Technologies for Next Generation Mobile Networks



          ◦    Communication Network Design for Complex Intelligent Transport Systems



          ◦    Applied Graph Theory and Its Applications in Wireless Networks



          ◦    Large-Scale Highly Dynamic Wireless Networks: Architecture and Communication Strategies Design



          ◦    Wireless Localization Techniques



          ◦    Spatially Distributed Complex Multiagent Systems


-------Funded Research Projects------

Funded research projects for which I am a Chief Investigator:


1. "Multi-modal High-Accuracy Integrated Positioning for Intelligent and Connected Vehicles, Chinese National Science Foundation Key Project, ¥2,680,000, 2022-2024.

2. “Applications of Integrated Road-Vehicle Collaboration Systems”, Chinese Ministry of Science and Technology National Strategic Research and Development Project, ¥117,310,000, 2020-2022.

3. “SydTrains RPI (Responsive Passenger Information) system scoping study”, Sydney Trains, $492,180, 2017.

4. “Exploiting Networked Distributed Sensors”, Data61, CSIRO - Defence Science and Technology Group (DSTG) joint project award, $889,044, (2016-2019).


5. “A Novel Radio Resource Management Scheme for Ultra-Dense Networks”, Huawei HIRP Open Project, USD$62,000, (2016-2017).


6. “Monitoring from a distance – meeting the needs of the water industry using remote sensing and smart monitoring”, Water Research Australia, $60,000.00, (2015).


7. “Test-bed for Wide-Area Software Defined Networking Research and Experimentation”, ARC Linkage Infrastructure, Equipment and Facilities, $270,000, (2015).


8. “Distributed Control and Estimation in Networked Environments”, National ICT Australia (NICTA) - Defence Science and Technology Organization (DSTO) joint project award, $1,340,385, (2012-2015).

9. “Large-scale highly dynamic wireless networks: architecture and communication strategies design”, ARC Discovery Project, $300,000, (2012-2014)

10. “Spatially distributed complex multiagent systems”, ARC Discovery Project, $600,000, (2011-2015).

11. “Robust Multi-Agent Sensor Network Systems”, US Air Force, Asian Office of Research and Development, US$180,000, (2010-2011).


12. “Measuring and ensuring performance and information quality in multi-agent sensor network systems”, US Air Force, Asian Office of Research and Development, $43,000, (2009).

13. “Coordination, Control, Localization and Health Characterization of Autonomous Multi-Agent Swarms”, Joint National ICT Australia (NICTA) - Defence Science and Technology Office Research (DSTO) project, $1,239,000, (2008-2011).


14. "A Graph Theoretical Approach to Cooperative Radio Resource Management”, The University of Sydney Bridging Support Grant, $50,000, (2008).

15. “Large Scale Complex Multiagent Systems : Control Methodologies and Information Architectures”, ARC Discovery Project, $661,000, (2008-2010).

16. “A unified framework for analyzing the timescale of interest for traffic measurements, modelling and performance analysis”, ARC Discovery Project, $150,000, (2005-2007).


17. “A Quality of Service Monitoring System for Service Level Agreement Verification”, Optus contracted research project, $40,715, (2004-2005).

18. “Characterization, Diagnosis, and Assurance of Health and Quality of Sensor Formations”, NICTA-DSTO joint research project, $866,157, (2005-2008).


19. “Design of Video Transmission System Over IEEE Wireless Local Area Network", University of Sydney Research and Development Grant, $19,000, (2006).


20. “Distributed location estimation algorithms for wireless sensor networks”, University of Sydney Research and Development grant, $20,000, (2005).


21. “Location Estimation in Sensor Network”, National ICT Australia Research Project Award, $25,500, (2005-2007).


22. “Development of 4G wireless communication systems and wireless sensor networks”, ARC Linkage Infrastructure, Equipment and Facilities, $200,000, (2007).

-------My Research Students (as the Principal Supervisor)------

Ms. Yimeng Feng, PhD candidate (funded by CSIRO project, 2018-)

Mr. Peibo Duan, PhD candidate (funded by UTS scholarship, 2016-)


Mr. Junnan Yang, PhD candidate (funded by ARC scholarship, 2015-)


Mr. Shangbo Wang, PhD candidate (funded by ARC scholarship, 2015-)


Mr. Tian Ding, PhD candidate (funded by UTS scholarship, 2015-)


Ms. Jieqiong Chen, PhD candidate (funded by UTS scholarship, 2015-)


Mr. Peng Wang, PhD candidate (funded by ARC project and NIP top-up scholarship, completed in 2015, working as a Research Fellow at Nanyang University of Technology after graduation)


Ms. Ruixue Mao, Research Master (completed in 2013, funded by ARC project and NIP top-op scholarship, working at Huawei Australia after graduation)


Ms. Yang Tao, PhD candidate (completed in 2014, funded by ARC project and NIP top-up scholarship, working at Huawei Australia after gradation)


Mr. Seh Chun Ng, PhD candidate (completed in 2012, funded by EIPRS scholarship, NICTA top-up scholarship and NIP top-up scholarship, working as a Research Scientist in Maylaysia)


Mr. Zijie Zhang, PhD candidate (completed in 2012, funded by ARC scholarship and NIP top-up scholarship, working as a research fellow at NICTA after graduation)


Ms. Anushiya Kannan, PhD candidate (completed PhD in 2010, funded by UPA scholarship, NICTA top-up scholarship and NIP top-up scholarship, working as a Research Fellow at University of New South Wales after graduation)


Mr. Xiaoyuan Ta, Master and PhD candidate (completed Master in 2006 and PhD in 2009, funded by UPA scholarship, NICTA top-up scholarship, NIP top-up scholarship, My Optus project and my ARC project, now working in a financial company as a quantitative analyst in Hong Kong)


Mr. Lixiang Xiong, PhD candidate (completed PhD in 2008, now working at Australian Communications and Media Authority (ACMA), funded by APA scholarship, NICTA top-up scholarship, NIP top-up scholarship and my ARC project)


Mr. Yanqiang Luan, Research Master (completed in 2005, funded by my ARC project, now working in Motorola Mobility, China, as a software development team leader)

-------Visiting Students Under My Supervision------  

Mr. Kecheng Zhang, Beijing University of Post and Telecommunications, 2017

Mr. Wenwei Yue, Xidian University, 2017-2018

Mr. Bin Yang, Huazhong University of Science and Technology, 2015


Ms. Xiufang Shi, Zhejiang University, 2014-2015


Ms. Xuefei Zhang, Beijing University of Posts and Telecommunications, 2013-2014


Ms. Jindi Li, Wuhan University of Science and Technology, 2013-2014


Mr. Kun Wei, Shanghai Research Center for Wireless Communications, Chinese Academy of Science, 2013


Ms. Xue Han, Research Center for Wireless Communications Technology, Chinese Academy of Science, Beijing, 2012

-------Current Research Projects------

Sensing and Communication Infrastructure for Intelliegent Connected Vehicles
       

Intelligent connected vehicles (ICVs), including connected vehicles and autonomous vehicles, are set to completely disrupt countless aspects of daily personal life, business, and technology in the decades to come. The safe and efficient mass deployment of ICVs are simply not feasible without the support of revolutionary, next generation smart road infrastructure. Particularly, recent fatal accidents involving autonomous vehicles have highlighted the limitation of  relying on vehicle onboard sensing and localization equipment only. This project tackles the critical problem of smart infrastructure design by investigating wireless sensing and communication road infrastructure for accurate and reliable vehicle sensing and localisation in complex urban environments, such as urban canyons and tunnels. This is achieved by deploying a large number of low-cost wireless sensors on road surface directly to assist vehicle sensing and localization, and for pedestrian detection, road condition monitoring and road health monitoring. The developed technologies will transform our road infrastructure into smart ones and prepare our roads ready for the emerging ICVs.

Harnessing Mobile Crowd Sensing for Smart Cities


Urban living poses ever-increasing challenges in our everyday lives. According to the Australian Bureau of Statistics, by 2061 Australia’s four largest cities Sydney, Melbourne, Brisbane and Perth are projected to have population increases of 70-90%, 80-130%, 70-150% and 130-250% respectively. Both Sydney and Melbourne will have a population of over 8 million by that time. These population increases create tremendous pressure on road infrastructure and traffic management, power supply, environmental management including water, noise and air quality, healthcare and safety. Cities need to be smarter in managing their resources, infrastructure and environment to promote economic, social and environmental wellbeing and urgent solutions are required for viable living conditions and sustainable city development.

Having correct and accurate information and knowledge about a city, its environment and residents plays a vital role in smart city management. Urban sensing plays a critical role in information gathering. Broadly speaking, urban sensing investigates the use of networked sensors in urban environments to collect information for smart city management. In addition to infrastructure sensing, proliferation of measurement capabilities in mobile devices is creating a deluge of data that can be harvested for smart city management. Specifically, mobile phones and other mobile terminals are currently capable of measuring large numbers of different parameters of the surrounding environment. Furthermore, the fact that mobile phones are moving and distributed all over is likely to make them an important source of sensing information either from sensors embedded into the phones or through the rich information contained in their mobility traces. 


This project proposes to develop the tools to harness the power of mobile crowd sensing, a new paradigm that takes advantage of pervasive sensing capabilities of mobile devices to efficiently collect data, and integrate with infrastructure sensing for smart city management. Three major challenges are expected to be conquered: a) measurements of a mobile phone user are temporally and spatially correlated and these measurements are further temporally and spatially correlated with measurements of other users. The intricate spatial and temporal correlations present great challenges for data modeling and analysis; b) movements of mobile phone users are uncontrollable, the availability of their measurements is unreliable and the accuracy and types of measurements are heterogeneous. These present great challenges for integrating the measurements into infrastructure sensing; c) existing mobile phone sensing system development is highly empirical. It is desirable to develop a common theoretical framework that formalizes the system design and facilitates knowledge accumulation.

Designing Vehicular Networks for Intelligent Transport Systems


Since the invention of cars, people have enjoyed much greater freedom in physical movement than ever before. Unfortunately such freedom comes at a heavy cost. Worldwide it was estimated that 1.2 million people were killed and 50 million more were injured in motor vehicle accidents every year. Other social, economical and environmental costs include road violence, traffic congestion, air pollution and noise. Vehicular networks are a development towards the dream of making our roads less congested and accident free. By enabling a vehicle to communicate with other vehicles and road-side infrastructure, vehicular networks pave the way for supporting a myriad of applications, e.g. collision detection, lane change warning, blind spot detection, and intelligent traffic scheduling and routing, that make our roads safer and more efficient.


After more than a decade of research and development, vehicular networks have reached a stage of maturity in which real-world deployment becomes both feasible and desirable. In the US, the IEEE 802.11p Dedicated Short Range Communications/Wireless Access in Vehicular Environments (DSRC/WAVE) family of standards were approved in 2010. In Europe, Release 1 of the Cooperative Intelligent Transport Systems standards have been completed in 2014, indicating deployment of a basic system in 2015. More than ever before, a large-scale deployment of vehicular networks is closer to reality. 


Compared with significant advances in vehicular communication technology, there is much less understanding on the optimum design and deployment of large vehicular networks. Particularly, the challenge of designing vehicular networks to seamlessly integrate with intelligent transport systems (ITS) and to cater for and exploit the intricate characteristics and dynamics of ITS remains an open problem. This project will develop the techniques for the optimum design and deployment of large vehicular networks to tackle the challenges.

Spectrum and Energy Efficient Design and Management Technologies for Next Generation Mobile Networks


The global development of mobile communication networks is one of the most celebrated achievements made by electrical engineers. These networks now reliably connect over half of the planet’s population, spanning from rural areas in India and China to central business districts in Australia, United States and Europe. Driven by both the increase in bandwidth-demanding applications and the shift from using desktop computers to using mobile devices, e.g. smart phones and tablets, to access the Internet, mobile data traffic has been increasing at an unprecedented rate in recent years. A number of studies have documented over 100 percent annual growth in mobile data traffic since 2008 and a 20-fold increase in mobile data traffic is expected over the next few years. Such a striking increase is not likely to slow down soon. Furthermore, according to former Google CEO Eric Schmidt, “Every two days now we create as much information as we did from the dawn of civilization up until 2003”. Much of the big data is generated by mobile devices and needs to be transported over the mobile networks. Therefore and almost inevitably, capacity of the current generation mobile networks, commonly known as 4G mobile networks, will become saturated sooner than originally expected. Unsurprisingly both industry and academia have realized the urgent need to invest in technologies for the next generation mobile networks, widely known as the fifth generation (5G) mobile networks. 


Among the several promising technologies for the next generation mobile networks, dense and small-cell networks technology is expected to contribute a capacity gain of 10-100 times; massive MIMO technology 5-20 times; advanced receiver design 2 times; device-to-device communication technology 3 times while adding more spectrum can bring in a capacity gain of 2-10 times.  Dense and small-cell networks technology will play a vital role in the development of the next generation mobile networks. 


Dense and small-cell networks feature very dense deployment of base stations (BSs). According to some predictions, there will be 11.5 million small-cell BSs by 2018, up from just 168,000 today. Some even predict that in 10-15 years time, the number of small-cell BSs may exceed the number of mobile users. The very large number of small-cell BSs poses significant challenges in the modelling, design and performance analysis of small-cell networks, in spectrum and energy efficient management and coordination of small-cell BSs and in determining the optimum BS that a mobile user should associate with, i.e. the optimum cell association. 


This project proposes to develop the spectrum and energy efficient network design and management technologies for the next generation mobile networks with a particular focus on dense and small-cell networks. The aim is to develop fundamental methodologies, built on a solid understanding of the characteristics of these networks, which provide design principles and management guidelines for these networks.

Communication Network Design for Complex Intelligent Transport Systems


The last two decades have witnessed unprecedented growth in telecommunications. This development in telecommunications opens doors to many sophisticated complex systems, e.g. intelligent transport systems (ITS), smart grids and social networks, that previously were not feasible. With the deeper penetration of telecommunications technology into these systems, people have come to realize that, instead of treating the communication network and the complex system served by the communication network as two separate entities with one demanding communication services from the other, a seamlessly integrated system can lead to an even greater benefit and in many cases is a perquisite for both systems to perform satisfactorily. With few exceptions, existing communication networks have not been designed to integrate with the complex systems and these complex systems are merely treated as consumers of communication networks posing some specific bandwidth and delay requirements. 


In this research, using intelligent transport systems as a representative example of a category of dynamic and evolving complex systems, we shall develop tools for communication network design to cater for the intricate demands of these complex systems and to integrate and exploit the characteristics and dynamics of complex systems.

Applied Graph Theory and Its Applications in Wireless Networks


Wireless multi-hop networks, in various forms, e.g. wireless sensor networks, underwater sensor networks, vehicular networks, mesh networks and UAV (Unmanned Aerial Vehicle) formations, and under various names, e.g. ad-hoc networks, hybrid networks, delay tolerant networks and intermittently connected networks, are being increasingly used in military and civilian applications. Graph theory, particularly a recently developed branch of graph theory, i.e. random geometric graphs, is well suited to studying these problems. These include but not limited to: cooperative communications; opportunistic routing; geographic routing; statistical characterization (e.g. connectivity, capacity and delay) of multi-hop wireless networks; geometric constraints among connected nodes and their use in autonomous parameter estimation without manual calibration. This research will investigate the use of graph theory to solve problems in the above broad areas. Research outcomes will benefit almost all areas in wireless multi-hop networks, including routing, scheduling, mobility management, dimensioning, interference control, energy management and localization.

Large-Scale Highly Dynamic Wireless Networks: Architecture and Communication Strategies Design


We are on the brink of a new era in wireless communications, brought on by the proliferation of wireless devices and the emergence of new types of communication systems such as IEEE 802.11p based Vehicular Networks, IEEE 802.15 based Wireless Personal Area Networks, IEEE 802.11s based Mesh Networks and IETF RFC4838 based Delay Tolerant Networks. Among the most exciting, challenging and important communications problems today are those involving large-scale highly dynamic wireless networks, particularly wireless vehicular networks for autonomous vehicles (also known as driverless or self-driving cars). The social and economical benefits of autonomous vehicles are numerous: reducing traffic congestion, saving fuels and reducing greenhouse gas emission, freeing drivers and improving productivity, helping reconnect the elderly and disabled to our society, and the most compelling benefit is of course improving road safety and saving lives – in Australia alone the number of serious injury accidents every year is over 25,000 and 1507 people lost their lives in Australian roads in 2009. 


This project proposes to develop scientific tools for the modelling, characterization, network architecture design and communication strategies design of highly dynamic networks, with a particular focus on highly dynamic vehicular networks for autonomous vehicles. The aim is to develop fundamental methodologies, built on a solid understanding of the characteristics of these networks, that provide design principles and management guidelines for these networks.


The following topic areas will be investigated (but not limited to):


* Characterization of dynamic networks. 


* Network architecture design. 


* Building local consensus. 


* Statistical radio resource management. 


* Cooperative communication strategy.  

Wireless Localization Techniques


Wireless sensor networks are a significant technology attracting considerable research attention in recent years. It is one of the most important technologies for the 21st century. Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power and multi-functional sensor nodes that are small in size and communicate in short distances. These tiny sensor nodes, which consist of sensing, data processing, and communicating components, bring the idea of wireless sensor networks into reality. Sensor networks represent a significant improvement over traditional sensors. Cheap, smart sensors, networked through wireless links and deployed in large numbers, provide unprecedented opportunities for monitoring and controlling homes, cities, and the environment. In addition, networked sensors have a broad spectrum of applications in the defense area, generating new capabilities for reconnaissance and surveillance as well as other tactical applications.


Emerging applications for wireless sensor networks will depend on automatic and accurate location of thousands of sensors. In environmental sensing applications such as bush fire surveillance, water quality monitoring, precision agriculture, and indoor air quality monitoring, “sensing data without knowing the sensor location is meaningless”. In addition, location estimation may enable applications such as inventory management, intrusion detection, traffic monitoring, and telecare, etc. In this research we shall investigate distributed location estimation algorithms in wireless sensor networks and its applications. Particularly we will both investigate theoretical problems in the area of large-scale sensor network localization and develop various localization techniques to solve practical problems encountered by our industrial partners in various application scenarios.

Spatially Distributed Complex Multiagent Systems


Among the most exciting, challenging and important control and communications problems today are those involving large-scale, multi-agent systems, often with spatially distributed physical agents. Examples include vehicular networks, formations of unmanned airborne vehicles, sensor networks for pollution, biological or border control, etc. The ultimate technical challenge flows from the architectures, often multiple architectures, for sensing, communications and control respectively; being decentralized and distributed, they were hardly ever studied in earlier decades. This research is aimed squarely at developing design methodologies for such systems, focusing on architectural fundamentals. For the sake of concreteness, the principal problem instantiations we will consider are formation control, and wireless networks, especially sensor networks and mobile multi-hop networks. The examples are linked operationally and theoretically, e.g., agents such as unmanned airborne vehicles (UAVs) in a moving formation are commonly part of a network of sensors, and tools of graph theory will underpin the solution of many problems in the two areas. 


Particularly, we shall investigate the following topic areas (but not limited to):


* Wireless Networks: three key problems


        Mobile multi-hop networks: fundamental trade-offs.


        Mobile network localization.


       ‘Local’ localization for mobile and static networks


* Formation Motion Control


        Effective shape control algorithms.


        Global behavior of control systems.