Personal details

Professor Glen Tian
Science and Engineering Faculty,
Electrical Engineering, Computer Science,
Data Science
Discipline *
Distributed Computing, Computation Theory and Mathematics, Computer Software
+61 7 3138 2177
+61 7 3138 2703
View location details (QUT staff and student access only)
Identifiers and profiles

PhD (University of Sydney), PhD (Zhejiang University)

Professional memberships
and associations

Professor Yu-Chu Tian (Glen)

My personal homepage:

Please visit Google Scholar Citations of my publications.

Member, IEEE (Institute of Electrical and Electronic Engineers, USA)

Member, IChemE (Institution of Chemical Engineers, UK)

Member, IEAust (Institution of Engineers, Australia)


Big data, Distributed computing, Cloud computing, Computer networks, Wireless networks, Real-time computing, Process control, Networked control

* Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2008



Research theme: Computer Science

Research discipline: Networks and Communications 

Research areas: 

Big data processing and cloud computing with regard to

  • distributed computing
  • distributed data management and processing
  • scheduling and resource management of distributed systems
  • parallel computing
  • bioinformatics
  • Data centre management and optimisation
  • Cloud financial services

Real-time computing, embedded systems, distributed and cooperative real-time applications with the main topics of

  • real-time feedback scheduling
  • control-theoretic scheduling methods
  • task decomposition models
  • software/hardware co-design
  • scheduling of networked real-time tasks
  • adaptation of computing and network resources.

Networks and wireless communications with regard to

  • real-time network protocols
  • real-time networked control
  • integration of wireless networks with global positioning systems
  • adaptive allocation of computing and network resources
  • networked coordination and operation of intelligent agents
  • protocols and cross-layer design for wireless and sensor networks
  • Smart grid communications

Complex systems and systems engineering with regard to

  • complex dynamics
  • complex networks and interactions
  • coordination of large-groups of robots and intelligent agents
  • modelling and simulation, and integration of complex systems
  • modelling and control of large-scale and complex processes

Potential PhD/Masters/Honours Projects

  1. Distributed and/or Parallel Processing of Big Data. Big data are data with large volume, fast and dynamic generation, and diversity of data formats. Their management, storage, retrival and processing are challenging due to these features. Hadoop and map/reduce are being widely used for big data management and processing, but they are not suitable for many real-world applications such as some bioinformatics problems we are investigating at the moment. Also, the efficient utilisation of the resources of each of the distributed machines is still a challenging task for big data processing. These call for emerging development of new front-end programming models and back-end technology support for big data processing. We have established a big data lab for experimental HPC and networks at QUT, and the lab provides state-of-the-art facilities to carry out the research in this area.  
  2. Communications and Big Data Processing in Smart Grids. Smart grid is a new concept to design and operate power generation, transmission, distribution and other related systems in an integrated environment with emerging services. There are a number of challenges in smart grid systems. One of the challeges is smart grid communications for real-time applications. The existing standards for smart grid communications are based on IP networks, which were not designed for real-time control systems and thus do not provide real-time performance guarantee. New network protocols need to be developed to complement existing smart grid network technologes to enable real-time communications for real-time QoS performance. Another challenge in smart grid systems is the management, storage, retrival, and processing of big data generated through a huge number of sensors in high frequencies for system monitoring, fault diagnosis, control and operation.This requires development of new technologies in this area. We have collaborations with QUT’s power engineering discipline, and have also established a big data lab for experimental HPC and networks at QUT, facillitating excellent research in this area.   
  3. Data Centre Management and Optimisation. Data centres have been increasingly built to provide a wide range of data, network, and other cloud services. The increasing demand in various services requires more and more data center resources and consequently more and more energy is consumed in data centres. It is estimated that about 30% of the running cost of a data centre is the energy consumption. Therefore, it is significant to manage and optimise data centre resources and energy consumption in data centres. New techniques are to be developed to tackle the problems of virtual machine management, task placement, task migration, and QoS maintainance for effiient management of data centre resources and energy comsumption. An understanding of operations research and data centre management is essential to carry out the research.
  4. Distributed/Cooperative Computing for Reducing Computational Complexity in Bioinformatics. Bioinformatics computing demands huge computing and memory resources, and becomes one of the main problems in computational bioinformatics. Investigating traditional sequential computing, parallel computing, cluster/distributed/cooperative computing and/or other computing approaches, the projects to be undertaken on this topic aim to develop architecture/frameworks, algorithms and scheduling strategies, dynamic memory management policies, implementation methods and/or web tools for complicated bioinformatics problems. They would make the best use of the computing resources including memory and CPUs for improved computation efficiency and/or more complicated bioinformatics computing. In particular, the projects will consider the scenarios of building Composition Vector (CV) Trees for phylogenetic analysis using complete genenomes without sequence alignment. Preliminary studies have been carried out in our group and working c++ code is already available for stand-alone sequential computation of CV tree building.
  5. Cooperative Computing of Ad-Hoc Wireless Networks and GPS for Seamless Vehicle Navigation Services. GPS (Global Position System) is the most widely used navigation technology for automobile systems. The GPS applications can be used in traffic navigation, emergency assistance, collision avoidance and vehicle tracking in automobiles. Seamless navigation means continuously navigating users over the applicable areas of those sensors and maps. Therefore, seamless navigation in GPS will provide reliable navigation, reduce the fuel consumption of the vehicles, minimize dangerous driving, and reduce the possibility of collision. However, current GPS devices can provide location information, route guidance and location-sensitive services only when there is a direct line of sight to four or more satellites. When a vehicle passes through a tunnel or urban areas, the tunnel or high skyscrapers block the signals from the satellites. As a result, the GPS device in the vehicle could not communicate with the satellites effectively, and continuous navigation of the vehicle becomes impractical. This motivates the research on seamless global navigation technologies. The projects to be carried out on this topic aim to develop leading edge technologies for seamless navigation services through corporative computing of ad-hoc wireless networks with GPS. They will focus on network architecture, routing protocols, cross-layer optimization, computing frameworks, and computing algorithms.
  6. Positioning Services and Clock Synchronization through Wireless Data Networks. Wireless is now a pervasive aspect of everyday life, and has become ubiquitous. This makes it possible to develop emerging and location-aware services that cannot be provided by existing network systems. Potential applications of location-aware services include emergency services, aged care and assisted living, to just mention a few. Although the Global Navigation Satellite Systems (GNSS) are an ideal positioning technology for open sky operation, they have limited availability in urban settings, especially in indoor applications, due to the blockage of signals. Preliminary studies have shown several possible solutions using wireless networks for positioning services. One is to use access points; however, noise characteristics will result in inaccuracy in location determination. An alternative and distributed solution is to establish relative clocks of the mobile nodes; however, accurate relative clocks and their synchronization in wireless networks are still challenging. Addressing both challenges, the projects to be carried out on this topic aim to develop effective technologies for accurate location determination in wireless networks. Specific objectives include: 1) Comprehensive requirements analysis for wireless location determination; 2) Development of fast and precise relative clock synchronization protocols, 3) Development of innovative methods, algorithms, and protocols for wireless location determination; and/or 4) Evaluation and demonstrations of the developed technologies.
  7. Disaster Management and Emergency Services over Wireless Sensor/Actuator Networks. For disaster management and emergency services such as fire protection, timely report and responses are exceptionally significant for reducing the number of casualties, injuries, and property damages from incidents. However, existing report and response systems are passive, implying that the incident is not known to the disaster management or emergency services centre until it is reported. In the worst case of such an incident, the communication infrastructure that we are using today may not function well. This makes it difficult to acquire, even rough, information about the incident, and then to respond to the incident quickly and appropriately. Sensor/actuator networks can provide a good solution to these problems through actively monitoring and timely reporting emergency incidents. While wireless devices have become an essential part of Australia’s infrastructure, disaster management and emergency services have not taken full advantage of wireless technologies. Our projects on this topic aim to resolve some key technical difficulties in this area, thus promoting the wireless technologies for significant improvement of disaster management and emergency services.
  8. Cross-Layer Design and Optimization for Wireless Network Applications. Wireless networked systems are being increasingly investigated for various applications due to their good scalability, fast deployment, and low implementation and maintenance costs. However, compared with wired systems, wireless ones introduce longer transmission delays, bigger jitter and higher rates of packet loss. These problems could result in unpredictable system behaviors which may not be acceptable in many services and applications. Our projects on this topic aim to improve overall performance of wireless networked applications through avoiding data packet dropouts, reducing network-introduced communication latency and jitter, and better scheduling wireless network resources. The methodology of the cross-layer design and optimization will be used in this project to develop new technologies and network protocols. Cross-layer design is an efficient and popular method for designing protocols especially for wireless networks; however, it will actively violate conventional layered architecture for better performance.
  9. Control-Theoretic Methods for Real-Time Scheduling and/or Network Access Control. The feedback control principle is being promoted for a number of applications such as real-time scheduling, performance improvement in network access control. However, only simple feedback control algorithms, e.g., PID, have been developed so far for this purpose. Applications of advanced feedback control technologies, such as predictive control, auto-tuning control, and adaptive control, in these applications are yet to be investigated comprehensively. Improved performance in real-time scheduling, network access control and other systems is expected from applications of these feedback control techniques. A fair understanding of feedback control principles as well as real-time task scheduling and/or network resource scheduling would be essential in order to undertake the research in this area.
  10. Integrated Modelling and Design for Wireless Networked Control Systems. There has been strong interest in wireless networked control, e.g., networked mobile vehicles, uninhabited aerial vehicles. Existing and emerging applications of wireless networked control are wide-spread including irrigation networks, water resource monitoring, disaster monitoring and emergency services, and aged care and assisted living. However, the full potential of wireless networked control is hindered by the lack of theory and technology support to address current challenges. The projects to be carried out on this topic will try to overcome these hurdles by creating new knowledge in the emerging area. They aim to develop innovative integrated design theory and methodologies for wireless networked control systems (WNCSs) for significant improvement in real-time performance. They are significant because if successful as we anticipate they will fundamentally change our philosophy to design networked control systems for emerging as well as existing applications, provide a total solution in a uniform framework to three significant challenges, and provide theoretical support to WNCS implementations. Expected outcomes include a uniform WNCS modelling framework under various scenarios, new methodologies for stability analysis and controller design, new network protocols, new techniques for feedback scheduling, and/or frameworks for integrated design.
  11. Theory and Applications of Delayed and/or Networked Control Systems. Large-scale real-time control systems over data networks are being increasingly implemented on a massive scale in various industries. Major challenges in such systems include multitasking- and network-induced delays, packet losses, and bandwidth constraints, which are all caused mainly by the sharing of system resources. Addressing those challenges, the projects to be carried out on this topic aim to develop an innovative scheduling framework for significant improvement of networked control performance through more efficient and flexible use of system resources. In order to do so, traditional fixed-period control is discarded to enable variable-period control. This leads to a number of difficulties which will be addressed in the research such as event-triggering control theory, system stability under period switching, controller synthesis in networked environments with period switching, etc. The outcomes from these projects will form the foundation of a new real-time control paradigm offering improved quality-of-control with reduced demand on system resources.
  12. Formal Methods for Design and Verification of Real-Time and Embedded Systems. While formal methods for design and verification of real-time and embedded systems have been well understood, there is still challenging to formally specify and verify real-time and embedded systems. The time sensitive nature of real-time systems and the hardware dependent characteristics of embedded systems bring difficulties into existing formal methods for system design and versification. Innovative technologies are to be developed to deal with these difficult issues for typical real-time and embedded system scenarios.
  13. Coordination and Networked Operation of Large Groups of Intelligent Agents. With the rapid development of the technologies in multi-robot and multi-agent systems, multi-robot and multi-agent systems have more and more applications. Many practical applications can benefit from the use of multi-agent systems, such as highway traffic control, battlefield environment, emergency services, and underwater and space exploration. In these challenging application domains, multi-agent systems can often deal with tasks which are difficult to accomplish by an individual robot/agent. While some topics in this area have been well addressed over the years, such as mission assignment, path planning, and formation generation and keeping, other significant issues are still challenging. Moreover, implementation of the theory in real world environment is also very limited. Our projects on this topic aim to optimize system structure, behaviour function and cooperation mechanism for large scale multi-robot/agent systems. Optimization strategies and algorithms will be established to enhance the overall performance of the large scale multi-robot/agent systems.

For further information, please refer to my homepage:

This information has been contributed by Professor Glen Tian.


Teaching history

  • 2019 Semester 2: IFN507 Network Systems
  • 2019 Semester 1: ENN523 Advanced Network Engineering
  • 2018 Semester 2: IFN507 Network Systems
  • 2018 Semester 1: ENN523 Advanced Network Engineering
  • 2017 Semester 2: IFN505 Analysis of Algorithms
  • 2017 Semester 1: ENN523 Advanced Network Engineering
  • 2016 Semester 2: IFN505 Analysis of Algorithms
  • 2016 Semester 1: ENN523 Advanced Network Engineering
  • 2015 Semester 2: IFN507 Network Systems
  •  2015 Semester 2: CAB303 Networks
  • 2015 Semester 1: ENN523 Advanced Network Engineering
  • 2014 Semester 2: IFN507 Network Systems
  • 2014 Semester 2: CAB303 Networks
  • 2014 Semester 1: ENN523 Advanced Network Engineering
  • 2013 Semester 1: ENN523 Advanced Network Engineering
  • 2013 Semester 1: INB353/INN353 Wireless and Mobile Networks
  • 2012 Semester 1: INB353/INN353 Wireless and Mobile Networks
  • 2012 Semester 1: INB201 Scalable Systems Development
  • 2011 Semester 2: INB352/INN352 Network Planning
  • 2011 Semester 2: INB353/INN353 Wireless and Mobile Networks
  • 2010 Semester 2: INB352/INN352 Network Planning
  • 2009 Semester 2: INB352/INN352 Network Planning
  • 2009 Semester 1: INB860 Computational Intelligence for Control and Embedded Systems
  • 2008 Semester 2: ITB722/ITN722 Network Planning and Deployment
  • 2008 Semester 1: ITB749/ITN749 Scientific Programming (in C/C++).
This information has been contributed by Professor Glen Tian.


For more publications by this staff member, visit QUT ePrints, the University's research repository.

Research projects

Grants and projects (Category 1: Australian Competitive Grants only)

Cloud scheduling and management of energy systems with real-time support
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
Start year
Control and Communications for High Value Distributed Electrical Storage
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
Start year
An integrated mathematical approach to synchronise and optimise hospital operations
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
Start year
Scheduling; Operations Research; Operations Management
Wavelet-based Modelling and Model Predictive Control of Complex Multidimensional Crystallisation Processes
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
Start year


Current supervisions

  • Low-latency Communications for Wide Area Control of Energy Systems
    PhD, Principal Supervisor
    Other supervisors: Professor Gerard Ledwich, Dr Yateendra Mishra
  • Machine learning and statistical modeling with applications.
    PhD, Associate Supervisor
    Other supervisors: Professor You-Gan Wang, Professor Kevin Burrage
  • Memory Sharing in Cloud Servers
    PhD, Principal Supervisor
    Other supervisors: Dr Maolin Tang
  • A Dynamic and Predictive Virtual Machine Placement for Energy Optimisation in Cloud Data Centres
    PhD, Principal Supervisor
    Other supervisors: Professor You-Gan Wang
  • Profile-based Virtual Resource Management for Energy Efficiency of Data Centres
    PhD, Principal Supervisor
    Other supervisors: Professor You-Gan Wang, Dr Maolin Tang
  • An Adaptive Slice Independent Handover Framework for Inter-Slice Mobility Management in 3GPP Service-based 5G Networks
    PhD, Associate Supervisor
    Other supervisors: Dr Dhammika Jayalath
  • Cloud-based Cooperative Multi-Vehicle Dynamic Routing Using Co-evolutionary Computation
    PhD, Associate Supervisor
    Other supervisors: Dr Maolin Tang, Dr Marc Miska
  • Minimising the Energy Consumption of Data Centres by Genetic Algorithms
    PhD, Associate Supervisor
    Other supervisors: Dr Maolin Tang
  • Tensor based Multiple-Context Aware Recommendation Systems
    PhD, Associate Supervisor
    Other supervisors: Associate Professor Richi Nayak

Completed supervisions (Doctorate)