Edge Computing & Embedded AI
Resource-efficient machine learning directly on microcontrollers and single-board computers, enabling local, privacy-preserving IoT intelligence.
Research Interests
My research explores the Internet of Things end-to-end: from resource-constrained edge hardware and embedded AI, through secure and cloudless architectures, to the human and societal questions of who owns, controls, and trusts the systems we build.
Secure IoT Architectures
Secure-by-design principles for IoT prototyping environments, covering device identity, encrypted communication, and threat-aware development workflows.
Cloudless IoT & Data Sovereignty
Local-first IoT architectures that keep data on-premises, reduce cloud dependency, and give users genuine control over their own systems and data.
IoT Retrofit & Legacy System Integration
Methods and tools for incrementally connecting existing infrastructure — buildings, machines, and equipment — to IoT ecosystems without full replacement.
Industrial IoT & Digital Twins
Combining real-time sensor data with digital twin models to enable predictive maintenance, process monitoring, and operator training in industrial environments.
Urban Data Platforms & Smart City
Open, interoperable platforms for aggregating and acting on urban sensor data, with a focus on municipal applicability and cross-domain collaboration.
Model-Driven IoT System Design
Applying model-based systems engineering — including SysML2 — to specify, validate, and reason about the structure and behavior of IoT architectures.
Responsible IoT & User Acceptance
Investigating why users resist or disengage from IoT systems, and what design, ownership, and governance models lead to technology that people actually want to live with.