This theme merges sensor technology, connectivity and data analytics to create smart, interconnected systems. We design networks of sensors and Internet of Things (IoT) devices that monitor infrastructure and the environment in real time, converting raw data into actionable intelligence. Our work spans next-generation wireless communications, distributed edge computing and digital modelling to enable responsive, efficient systems – from smart cities and energy grids to automated construction and environmental monitoring.
Our research spans three subthemes across multidisciplinary research
Our research in smart sensor networks aims to develop intelligent, adaptive systems that enable real-time monitoring, efficient data transmission, and predictive control across urban infrastructure, energy systems, and environmental applications. This theme integrates advanced photonic technologies, intelligent sensing frameworks, and structural health monitoring systems to support the development of future-ready, data-driven environments. A key focus of this research is the deployment of smart sensor networks in civil infrastructure and buildings, using technologies such as fibre optic and radio sensing, machine learning, and digital twins.
To achieve these goals, we are developing high-speed, energy-efficient technologies and intelligent sensing frameworks that form the foundation of scalable, low-latency sensor networks. This includes the creation of advanced photonic devices, such as electro-optic modulators and signal processors, for real-time data communication, as well as adaptive control systems that integrate environmental and energy data to enable predictive decision-making.
This research aims to improve intelligent sensing frameworks with a focus on real-time monitoring and predictive control in urban infrastructure, by integrating advanced photonic technologies, fibre optic and radio sensing, and machine learning, which enhances structural integrity assessment and enables sustainable, data-driven environments for resilient cities, by making them safer, more efficient, and better prepared for extreme weather or damage related to heavy use.
Professor Xiaoke Yi, Dr Ali Hadegieh, Dr Fengji Luo,
Harvard University
Our research aims to revolutionise how data is processed and transmitted across vast networks of interconnected devices. By focusing on processing data closer to its source, the research addresses critical challenges in latency, reliability, and scalability, especially for mission-critical applications such as smart healthcare, industrial automation, and energy systems. This aligns with our broader strategy to lead in next-generation wireless communications, distributed computing, and smart infrastructure.
Our work spans 5G and 6G mobile networks, wireless sensor systems, networked control systems, and edge computing architectures.
We are developing advanced wireless networking platforms, including a software-defined 5G architecture that supports network slicing for diverse industry use cases, from healthcare to manufacturing. Projects include the design of next-generation RF receivers for low-latency, high-throughput IoT communications, and wireless networked control systems for industrial automation and smart grids.
In parallel, we are tackling the computational demands of IoT through edge computing, big data analytics, and distributed algorithms. Their work enables real-time data fusion and decision-making at the edge, reducing reliance on cloud infrastructure and improving system responsiveness. Techniques such as over-the-air computation (AirComp) and passive radio technologies are being explored to support large-scale, energy-efficient IoT deployments.
This research aims to improve intelligent, real-time data processing in connected systems with a focus on enhancing safety, efficiency, and sustainability in complex environments, by developing low-latency wireless networks, intelligent surfaces, and AI-driven edge computing. This reduces delays and boosts responsiveness in places like cities and mines, leading to everyday benefits such as faster emergency response, smarter traffic systems, and more reliable digital infrastructure.
Professor Yonghui Li, Professor Branka Vucetic, Professor Albert Zomaya, Associate Professor Dong Yuan, Dr Wibowo Hardjawana, Dr Wanchun Liu, Dr Suranga Seneviratne, Dr Kanchana Thilakarathna
Roobuck, University of Athens, Alphanest, Swinburne University
Our research aims to create intelligent, real-time virtual replicas of physical systems to support dynamic decision-making, predictive modelling, and system optimisation. This aligns with our strategic priorities in digital transformation, smart infrastructure, and interdisciplinary innovation. Digital twins are being developed across sectors including manufacturing, defence, healthcare, urban planning, and coastal infrastructure, enabling more efficient, sustainable, and resilient systems.
We are building digital twin frameworks that integrate real-time data from sensors, AI-driven analytics, and simulation environments. Projects include the Terra framework, which enables simulation-to-real AI for robotics and autonomous systems, and urban digital twins that model Sydney’s infrastructure using live data on traffic, emissions, and crime to support sustainable city planning. In construction, digital twins are used to optimise building performance and reduce environmental impact through virtual design and lifecycle assessment. In coastal infrastructure, we are developing digital twins to model human-infrastructure interactions, using generative AI and scenario modelling to inform adaptive planning and policy.
This research aims to improve intelligent modelling of physical systems with a focus on real-time decision-making and predictive analysis, by developing digital twin frameworks that integrate live sensor data, AI-driven analytics, and simulation environments. This enables more efficient and sustainable planning in areas like city infrastructure, construction, and coastal management, leading to everyday benefits such as reduced traffic congestion, smarter building design, and better preparedness for environmental challenges.
Professor Matthew Cleary, Professor Itai Einav, Professor Jian Guo Zhu, Professor Omid Kavehei, Professor Jin Ma, Professor Mikhail Prokopenko, Professor Gregor Verbic, Associate Professor Joseph Lizier, Associate Professor Mahyar Shirvanimoghaddam, Dr Clement Canonne, Dr Sinan Li, Dr Neda Mohammadi, Dr Shahadat Uddin
