Väitös 12.6.2026: M.Sc Xinyu Tan (Informaatioteknologian tiedekunta, tietotekniikka)

M.Sc. Xinyu Tanin tietotekniikan väitöskirjan "A Design, Modeling, and Programming Framework for Resource-Constrained WSN Applications Starting from Statecharts" tarkastustilaisuus.

Vastaväittäjänä toimii professori Juha Plosila (Turun yliopisto) ja kustoksena professori Ismo Hakala.

Väitöstilaisuuden kieli on englanti.

Väitöstilaisuutta voi seurata Ulappa-salissa (Kokkolan yliopistokeskus Chydenius).

Alla englanninkielinen väitöstiedote kokonaisuudessaan.

New visual programming framework makes building and updating the Internet of Things easier and more efficient

In his dissertation, Xinyu Tan developed a visual programming framework that simplifies the development and maintenance of applications for small, low-power Internet of Things (IoT) devices.

What did you study?

We studied how to make programming small, resource-constrained devices in Wireless Sensor Networks (WSNs) and the Internet of Things easier. These devices, like environmental sensors, have very little memory and battery power. Traditionally, programming them requires highly specialized skills and creates a difficult trade-off between easy-to-understand code and energy-efficient execution.

What were the results of your study or what is its main finding?

The main result is a complete, integrated development framework that uses "statecharts"—a visual way to map out how a device reacts to events. We created Statechart4IoT, a web-based tool that lets developers design device behavior graphically. These visual models are then compressed into tiny files that can be distributed and executed directly on devices using a specially designed, memory-efficient operating system called StateOS. This proved to be much more memory-efficient than traditional programming methods.

How can the results be applied?

What new insights did the research contribute to the topic? This framework significantly lowers the barrier to entry for developing IoT applications. Because it uses state machine-based visual models, experts in other fields and hobbyists can easily collaborate on designing the IoT device they need. Furthermore, the tiny file sizes allow devices to be updated dynamically over the air without draining their batteries. This makes maintaining and upgrading massive sensor networks much cheaper and more reliable.