The School of Internet of Things organises its research around four core technology clusters that provide the technical foundation for AIoT and the Internet of Intelligence. These clusters cover edge intelligence and autonomous systems, trustworthy and secure AIoT, intelligent sensing and embedded intelligence, and intelligent connectivity and system infrastructure.
Built on these four technical clusters, the School develops an AIoT+X application research layer that connects core technologies with real-world domains such as smart manufacturing, healthcare, smart cities, robotics, transportation, energy, logistics, digital twins, and human-centred intelligent systems.
This “4+1” structure supports the School’s long-term strategy: to grow from research clusters into research labs, from labs into research centres, and eventually toward institute-level platforms for AIoT and the Internet of Intelligence.
Cluster Structure: 4+1 Model
A. Edge Intelligence & Autonomous Systems
This cluster focuses on the deployment of AI in real-world edge and autonomous systems. It studies how intelligence can be distributed across edge devices, autonomous platforms, cyber-physical systems, and real-time control environments.
Key research areas include edge computing, distributed intelligence, autonomous cyber-physical systems, robots, UAVs, vehicles, multi-agent coordination, adaptive edge intelligence, and real-time AI decision and control.
B. Trustworthy & Secure AIoT Systems
This cluster focuses on the security, safety, resilience, privacy, and governance of AIoT systems. It addresses how AIoT systems can be designed, deployed, audited, and operated in a trustworthy and secure manner.
Key research areas include AIoT security and privacy, secure AI deployment pipelines, trusted execution environments, resilient distributed systems, trustworthy AI, auditing, risk control, privacy-preserving distributed learning, and secure operations of AIoT infrastructure.
This cluster focuses on machine perception and embedded intelligent devices. It provides the sensing, perception, and device-level intelligence required for AIoT systems to interact with the physical world.
Key research areas include smart sensing systems, multimodal sensing, optical/RF/environmental sensing, computer vision, image processing, sensor fusion, perception AI models, embedded AI platforms, low-power intelligent devices, hardware-software co-design, energy harvesting, and wireless power for IoT systems.
D.Intelligent Connectivity & System Infrastructure
This cluster focuses on the network fabric and large-scale system infrastructure required for real-world AI deployment. It studies how intelligent systems are connected, orchestrated, managed, and scaled across devices, networks, edge/cloud platforms, and service infrastructures.
Key research areas include IoT networking architectures, RF and antenna systems, 5G/6G and beyond, edge-cloud integration, distributed system infrastructure, AIoT platforms and orchestration, lifecycle management of intelligent systems, and data/service platforms enabling real-world AI deployment.
AIoT+X Application Research Layer
AIoT+X is not a separate technology cluster in isolation. It is an application and integration layer built on the four core technology clusters. It connects edge intelligence, trustworthy systems, sensing and embedded intelligence, and intelligent connectivity with real-world X-domain challenges.
Through AIoT+X, the School promotes applied research in smart manufacturing, healthcare, smart cities, robotics, autonomous systems, energy, logistics, digital twins, intelligent products, smart living, and human-centred technologies.
This layer is especially important for industry collaboration, TIX-PhD projects, company-sponsored PhD research, joint labs, and applied research platforms.
Application Domains：
From Clusters to Labs, Centres, and Institutes
The School’s research clusters are designed not only as thematic groupings, but also as a long-term development mechanism. Over the next three years, the School will use these clusters to guide faculty recruitment, PhD training, proposal development, industry collaboration, lab formation, and platform development.
The development pathway is:
This pathway supports the School’s strategic ambition to build an internationally visible AIoT and Internet of Intelligence research engine, strengthen its faculty and PhD ecosystem, and develop scalable research platforms with industry impact.
The School of Internet of Things organises its research around four core technology clusters that provide the technical foundation for AIoT and the Internet of Intelligence. These clusters cover edge intelligence and autonomous systems, trustworthy and secure AIoT, intelligent sensing and embedded intelligence, and intelligent connectivity and system infrastructure.
Built on these four technical clusters, the School develops an AIoT+X application research layer that connects core technologies with real-world domains such as smart manufacturing, healthcare, smart cities, robotics, transportation, energy, logistics, digital twins, and human-centred intelligent systems.
This “4+1” structure supports the School’s long-term strategy: to grow from research clusters into research labs, from labs into research centres, and eventually toward institute-level platforms for AIoT and the Internet of Intelligence.
Cluster Structure: 4+1 Model
A. Edge Intelligence & Autonomous Systems
This cluster focuses on the deployment of AI in real-world edge and autonomous systems. It studies how intelligence can be distributed across edge devices, autonomous platforms, cyber-physical systems, and real-time control environments.
Key research areas include edge computing, distributed intelligence, autonomous cyber-physical systems, robots, UAVs, vehicles, multi-agent coordination, adaptive edge intelligence, and real-time AI decision and control.
B. Trustworthy & Secure AIoT Systems
This cluster focuses on the security, safety, resilience, privacy, and governance of AIoT systems. It addresses how AIoT systems can be designed, deployed, audited, and operated in a trustworthy and secure manner.
Key research areas include AIoT security and privacy, secure AI deployment pipelines, trusted execution environments, resilient distributed systems, trustworthy AI, auditing, risk control, privacy-preserving distributed learning, and secure operations of AIoT infrastructure.
C.Intelligent Sensing, Perception & Embedded Intelligence
This cluster focuses on machine perception and embedded intelligent devices. It provides the sensing, perception, and device-level intelligence required for AIoT systems to interact with the physical world.
Key research areas include smart sensing systems, multimodal sensing, optical/RF/environmental sensing, computer vision, image processing, sensor fusion, perception AI models, embedded AI platforms, low-power intelligent devices, hardware-software co-design, energy harvesting, and wireless power for IoT systems.
D.Intelligent Connectivity & System Infrastructure
This cluster focuses on the network fabric and large-scale system infrastructure required for real-world AI deployment. It studies how intelligent systems are connected, orchestrated, managed, and scaled across devices, networks, edge/cloud platforms, and service infrastructures.
Key research areas include IoT networking architectures, RF and antenna systems, 5G/6G and beyond, edge-cloud integration, distributed system infrastructure, AIoT platforms and orchestration, lifecycle management of intelligent systems, and data/service platforms enabling real-world AI deployment.
AIoT+X Application Research Layer
AIoT+X is not a separate technology cluster in isolation. It is an application and integration layer built on the four core technology clusters. It connects edge intelligence, trustworthy systems, sensing and embedded intelligence, and intelligent connectivity with real-world X-domain challenges.
Through AIoT+X, the School promotes applied research in smart manufacturing, healthcare, smart cities, robotics, autonomous systems, energy, logistics, digital twins, intelligent products, smart living, and human-centred technologies.
This layer is especially important for industry collaboration, TIX-PhD projects, company-sponsored PhD research, joint labs, and applied research platforms.
Application Domains：
From Clusters to Labs, Centres, and Institutes
The School’s research clusters are designed not only as thematic groupings, but also as a long-term development mechanism. Over the next three years, the School will use these clusters to guide faculty recruitment, PhD training, proposal development, industry collaboration, lab formation, and platform development.
The development pathway is:
This pathway supports the School’s strategic ambition to build an internationally visible AIoT and Internet of Intelligence research engine, strengthen its faculty and PhD ecosystem, and develop scalable research platforms with industry impact.