BIP 'Data and AI' Erasmus+ at Fontys Eindhoven

1. Project Context

The week at Fontys was built around a practical challenge rather than a purely academic exercise. From the beginning, Eindhoven was presented not only as a strong technical city, but as a place where innovation, design, and public infrastructure are closely connected. That framing mattered, because it made the assignment feel grounded from the start. Even though the programme focused on Data and AI, the discussions were consistently tied back to real civic problems, public value, and realistic urban use cases.

That gave the project a different quality from a normal classroom task. We were not simply asked to discuss technology in abstract terms. We had to think about how data-driven systems might fit into an actual city, what they would improve, and whether the proposal would make sense beyond presentation slides.

2. SmartStreets

Our team developed a concept called SmartStreets. The idea was to rethink an ordinary street pole as a multi-purpose urban service point rather than a single-function object. We wanted the concept to respond to everyday problems in a city context instead of using AI as a decorative feature. That pushed us toward a proposal where data, sensing, and adaptive behaviour were directly tied to public infrastructure.

The concept became stronger once we stopped treating the different parts as separate ideas. Instead of presenting “a smart lamp” and “a smart bin” as unrelated features, we framed SmartStreets as one integrated infrastructure node. That gave the proposal a clearer identity and made it easier to explain why the municipality might care about it.

2.1 Adaptive Lighting

The first part of the concept focused on adaptive street lighting. We started from a simple observation: many public lights remain at full brightness even when there is little or no activity. That results in unnecessary energy use and a system that reacts more to fixed schedules than to actual conditions.

Our proposal used sensor input, movement data, weather conditions, and contextual signals to adjust lighting more intelligently. In quieter situations, lights could remain dimmer, while movement, poor weather, or low visibility could trigger stronger illumination when needed. The idea was not to reduce safety, but to make lighting more responsive and more efficient at the same time.

2.2 Smart Waste Collection

The second part of SmartStreets focused on public waste collection. We looked at recurring issues such as overfilled bins, misuse, poor sorting, and collection routines that do not always reflect actual demand. Instead of treating public waste management as a fixed operational cycle, we explored how it could become more adaptive.

Our proposal included smart bins that could support sorting, monitor fill levels, and provide data that helps improve collection planning. In that sense, the goal was not just cleaner bins, but better decision-making around when and where collection should happen.

What made this part convincing was its connection to the rest of the concept. The value was not only in smarter bins themselves, but in combining waste handling with lighting, sensing, and local infrastructure into a single coherent system.

3. My Technical Role

As the only computer science student in the team, I took on the technical lead role within the project. That meant translating broader team ideas into something more structured, feasible, and technically coherent. While the project was collaborative, my contribution was mainly to shape the technical side of the concept and make sure the solution stayed realistic.

I provided the main input on how the system could work from a software and AI perspective, including how sensor data, adaptive behaviour, and smart infrastructure could be framed as one integrated concept rather than disconnected features. I also helped guide technical decisions so that the proposal remained credible enough for presentation, especially once we had to explain it to an audience beyond our own student group.

This role also meant acting as a bridge between disciplines. Because not everyone in the group approached the project from a technical perspective, I often had to explain why certain ideas were realistic, where simplifications were necessary, and how to present technical behaviour in a way that remained understandable without becoming vague.

4. Prototype Development

One of my main responsibilities in the project was developing the live prototype used to present the concept. This was important because SmartStreets could easily have remained just a strong presentation idea. By turning it into a live demo, the project gained something more concrete and easier to communicate.

The prototype helped translate the concept into a visible system rather than a purely verbal proposal. It made it possible to show how the different parts of SmartStreets fit together and how the idea could be experienced as a unified service rather than a collection of isolated features.

For me, this was one of the most important parts of the project. It moved my contribution beyond discussion and into actual implementation. In that sense, my role was not only to support the concept technically, but also to build a working representation of it that strengthened the final pitch.

5. International Teamwork

One of the most valuable parts of the week was working in a team of students from different universities and academic backgrounds. That mix shaped the project from the beginning. Not everyone approached the challenge from a technical angle, and that was useful. Some people focused more strongly on cost, public value, stakeholder perspective, or feasibility in ways that would probably not have emerged as quickly in a more homogeneous group.

Because of that, collaboration required more than simply dividing tasks. We had to explain our reasoning clearly, make assumptions visible, and keep the project understandable for everyone involved. That made the process slower at times, but also better. Instead of moving forward on instinct alone, we had to justify why a feature mattered, how realistic it was, and whether it made sense in a public-city context.

That interdisciplinary aspect became one of the hidden strengths of the programme. The week was not only about AI, data spaces, or smart-city ideas in isolation. It was also about learning how to build something meaningful with people who use different vocabularies and bring different priorities, which was quite surprising at the beginning.

Figure 1: Me with my colleagues and professors from Hochschule Campus Wien.

6. Programme Experience

Although the group project was the centre of the week, the programme around it made the experience feel complete. Each day followed a clear structure that balanced focused teamwork with broader input. We attended lectures and workshops on data spaces, AI, design, and collaboration, but the programme also went beyond purely technical themes. Sessions on cultural awareness and presentation training made it more fun and different.

I appreciated that balance. It kept the week from becoming too narrow and repeatedly reminded us that innovation depends not only on technical knowledge, but also on communication, ethics, public understanding, and the ability to work across cultures. Outside the formal sessions, there was also time to explore Eindhoven and talk informally with students from other universities.

The visit to the municipality and the final pitch preparation also changed the tone of the week. Once the project stopped feeling like an internal exercise and became something we would present to a jury with municipal stakeholders, the standard became more serious. We naturally became more careful about clarity, realism, and public impact.

Figure 2: Presenting our project.

Figure 3: A multicultural food exchange bringing together students from all universities.

7. Reflection

If I had to summarise the week in one sentence, I would describe it as a short but intense experience that brought together technical thinking, teamwork, real-world context, and interdisciplinary collaboration more effectively than I expected.

Altough I would still have welcomed more technical depth in some parts of the programme. What made the week memorable was not only the topic of Data and AI itself, but the fact that those ideas had to be relevant for a real city or urban setting. SmartStreets may have started as a student concept, yet the process behind it felt genuinely worthwhile and unique because it required technical reasoning, design thinking, civic perspective, and close collaboration across different non-technical backgrounds.