This project aims to integrate hardware sensors and drone images to develop a scalable forest health index.

Designing a smart building platform that uses mobile IoT sensors to reduce deployment complexity while maintaining comfort, sustainability, and wellbeing insights.

Builds an AI-assisted tool that helps novice software engineers learn privacy-by-design practices and legal compliance through guided design activities.

Develops guidance, datasets, and tooling to evaluate IoT anomaly detection techniques across heterogeneous smart home testbeds.

Builds semantic interoperability and conversational interfaces so non-experts can query building performance data across heterogeneous smart spaces.

Uses knowledge-based modelling and the MAPE-K loop to orchestrate self-adaptive cyber-physical security for smart homes without relying on the cloud.

Investigates how low-cost cameras can complement sensor networks to detect anomalies in smart environments.

Develops FedBio-IoT, a federated self-configuring IoT architecture using nature-inspired algorithms for runtime configuration and context-aware adaptation.

Deploying IoT sensing within Cardiff University’s Abacws building to characterise study-space usage and guide collaborative service design decisions.

Making linked data observatories accessible to bioscience researchers by blending graphical and conversational interfaces backed by large language models.

Designs resilient, low-power communications for monitoring traps, poaching activity, and sensors across dense jungle terrain in the Lower Kinabatangan Wildlife Sanctuary.

Co-designs privacy tooling and conversational assistants that help older adults manage smart-home data while maintaining independence.

Combines network traffic analysis with independent sensor observations to detect cyber-physical anomalies in smart homes using DIY IoT nodes.

Building a linked-data Forest Observatory that unifies heterogeneous bioscience and environmental datasets to help predict poaching activity in Sabah, Malaysia.

Builds CASPER, a context-aware anomaly detection platform that observes industrial robots with independent IoT sensors to spot attacks that evade traditional network monitoring.

Designs interactive technologies that encourage office occupants to adapt to mild discomfort, reducing energy consumption while maintaining wellbeing in built environments.

Designing tangible interfaces such as PrivacyCube and PriviFy to help households understand and manage how smart-home devices handle their data.

Equipping IoT developers with reusable privacy-preserving components and gamified tooling so applications comply with global regulations by design.

Analysing CAN bus traffic and enhancing controller design to detect and prevent cyberattacks against connected vehicles.

Investigates optimal placement and scheduling of service function chains (SFCs) across multi-cloud environments under security constraints.

Builds Privacy Captain, a knowledge-based AI assistant that annotates IoT designs with privacy patterns to reduce breakdowns in software design processes.

Develops interactive tools (Privacy Parrot) that bring privacy-preserving techniques into early IoT software design, supporting collaborative, privacy-aware workflows.

Benchmarks multi-criteria decision-making techniques (SAW, TOPSIS, VIKOR) for discovering IoT resources in the Cloud of Things, evaluating capacity and diversity trade-offs under varying user constraints.