Uses satellite imaging, radio collaring, camera traps, and predictive modelling to assess and mitigate the impact of Pan-Borneo Highway construction on Bornean elephants in Sabah, Malaysia.

Investigates neuromorphic event-driven sensing for wildlife monitoring by developing a toolkit and synthetic dataset pipeline that converts conventional camera trap footage into event-camera representations, in collaboration with the Danau Girang Field Centre in Borneo, Malaysia.

Explores the use of event-driven cameras and neuromorphic computing for privacy-preserving, low-power sensing in smart home and smart building applications, through a UK-Japan collaboration between Cardiff University and NAIST.

Extends the CASPER anomaly detection framework for industrial robots using externally-mounted IMU sensors, funded by NVIDIA with RTX PRO 6000 and Jetson Orin AGX hardware.

A Lego-based augmented reality smart city demonstrator used for public engagement, IoT education, and visualising cyber-physical risks across urban infrastructure.

Coordinates UK research on edge AI resilience, focusing on cyber-disturbances, data quality, and time-critical applications.

This project creates low-power sensing systems using spiking neural networks to detect threats in public spaces.

This project develops tangible interfaces to assist young people with neurodiversity to manage online harms.

This project develops a multi-model AI system to accurately identify and count wildlife in camera trap images.

This project develop an augmented reality-based Smart City demonstrator using Lego towards public engagement

Cardiff’s PETRAS regional showcase brought researchers, industry, and policymakers together to share “Connected Spaces” projects and identify future collaborations.

Provides an isolated lab of 170+ smart home devices connected through openHAB for experimentation, data collection, and cyber-physical anomaly detection research.

Commercialises the CASPER cyber-physical anomaly detection work into a product proposition through the UK CyberASAP accelerator.

Co-creating an Edge Analytics course with partners in the UK and India to equip engineers with skills in machine learning and IoT for edge devices while building long-term academic collaboration.

Integrates CASPER anomaly detection research with Thales UK’s autonomous logistics platform to support resilient decision making during UGV missions.

Builds a linked-data observatory and analytics platform to support forest conservation, community engagement, and scientific research.

Investigates IoT and data-driven approaches to enable circular supply chains in construction, helping the built environment meet net-zero goals.

Explores how to add layers of resilience to smart homes and offices by complementing traditional systems with independent sensing and analytics.

Proposes a secondary IoT sensor layer that monitors physical signals in buildings to uncover cyber attacks that evade traditional network monitoring.

Trials immersive tourism and farming security services across rural Monmouthshire and Blaenau Gwent, leveraging 5G, edge analytics, and cyber security to grow the local economy.

Designing resilient sensing and communications infrastructure for Sabah’s Lower Kinabatangan Wildlife Sanctuary so conservation teams can gather data without relying on constant connectivity.

Building semantic, on-demand data offerings for smart city marketplaces so consumers buy exactly the IoT data they need while reducing bandwidth and pricing friction.

Protecting critical infrastructure by using explainable AI at the edge to analyse data flows and detect vulnerabilities across industrial IoT systems.

Establishing a reconfigurable edge-computing testbed of Raspberry Pi and Jetson devices to explore latency-sensitive analytics, communications, and cybersecurity scenarios across campus spaces.

Develops modular IoT training programmes for diverse learner groups—from kids to makers—covering long-lived principles alongside rapidly evolving tools.

Develops privacy-by-design guidelines and assessment methodology to help IoT software engineers incorporate privacy awareness during application design.

Builds the Sensing-as-a-Service model to enable data trading between IoT data owners and consumers, unlocking siloed datasets for broader value.

Explores fog and edge computing architectures for IoT, evaluating distributed analytics platforms and their applications.

Explores how natural-language sticky notes can act as end-user programming for smart homes, bridging human-written reminders and IoT automation.

The Open Internet of Things (OpenIoT) project is an award-winning, EU FP7-funded open-source platform that pioneered Sensing-as-a-Service (S2aaS) for the Internet of Things. Developed between 2011 and 2014, OpenIoT provides a cloud-based middleware infrastructure that abstracts the complexity of heterogeneous IoT deployments, enabling developers and researchers to access sensor data and services without requiring knowledge of specific sensor hardware or communication protocols. The platform leverages W3C Semantic Sensor Network (SSN) ontologies for semantic annotation, discovery, and context-aware configuration of sensors across distributed environments. Built upon established projects including Global Sensor Networks (GSN) and Linked Sensor Middleware (LSM), OpenIoT implements a utility-based pay-as-you-go service model that democratises IoT infrastructure. The project received the 2013 Black Duck “Rookie of the Year” award as the best IoT open-source platform and was recognised for “best semantic interoperability” at the 2014 IoT Hackathon. OpenIoT has been deployed across real-world smart city scenarios including campus monitoring, crowd-sensing, and assisted living. The platform served as a reference implementation for IoT standards and the European Platform Initiative, generating significant research impact across semantic interoperability, context-aware computing, and cloud-based sensor service delivery.