TinyAIoT: Ressource-efficient AI Models for IoT Sensors

Modern IoT applications often rely on sensors that run on microcontroller units and communicate via network protocols such as LoRaWAN or Bluetooth Low Energy. To operate autonomously for extended periods of time, application resource requirements must be minimized.

This master’s thesis investigates the development of resource-efficient IoT applications through the utilization of AI models. These models aim to save energy by reducing the computational load, camera resolution or data transmission, while maintaining the ability to perform specific tasks. You will develop, implement and compare different resource-efficient AI models with sensors such as cameras, distance sensors, vibration sensors and test your implementation in different application scenarios.

Fostering Navigational Map Reading Competence

The ability to orient oneself and read maps is essential to successfully navigate in unfamiliar environments. It is well known that the ability to orient oneself with maps varies from person to person. While there are numerous navigation systems to help us find our way, very few efforts have been made to use GI technologies to promote orientation and map reading skills and overcome the individual differences.

GeoGami is a location-based game using digital maps to systematically teach navigational map reading competence. The thesis will investigate how to design trainings to promote people’s navigational map reading competence with digital maps. How to design trainings for specific sub-competencies of navigational map reading such as self-localization, map alignment or object recognition? How to design virtual environments to provide an optimal environment to systematically test navigational map reading competence?

Spatial Movement Behaviour

Every day, we move through space and time. To reach more distant destinations, we use navigation aids. But also in our immediate environment – for example while visiting a museum – we move purposefully along a path. A thesis can investigate spatial movement behaviour from different perspectives:

How can we observe spatial movement and gain a deeper understanding of human behaviour? Indoor, we may use depth cameras such as Kinect to collect information about people interacting with each other. Outdoor, we can complement GPS tracks with information on the viewing direction and information on your environment. Virtual environments can be used to systematically modify the environment w.r.t. visibility, spatiousness etc. to investigate its influence on the wayfinding behaviour.

A thesis may investigate the above questions from a technical perspective (e.g. building a system to collect relevant information), from a data perspective (e.g. how to interpret movement data collected by technology-supported observation) or from an experimental perspective (e.g. exploring wayfinding strategies and movement behaviour).

Spatial Learning Analytics

Learning Analytics is a method to collect, measure, analyze and visualize data about learners and their context. It enables the understaning of the learning process and allows an adaption of learning paths based on the collected data. It also gives feedback to the learner and teacher about the learning process.

The spatial intelligence lab has developed several learning platforms (GeoGami, Blockly for programming senseBox), where data revealing information about the learning proces sis collected. The thesis will investigate how real time data on the learning process can be used to guide the learning process using learning analytics.

SketchMapia – A Research Software to Assess Human Spatial Knowledge

Sketch mapping, i.e. freehand drawings of maps on a sheet of paper, is a popular and powerful method to explore a person’s spatial knowledge. Although sketch maps convey rich spatial information, such as the spatial arrangement of places, buildings, streets etc., the methods to analyse sketch maps are extremely simple. At the spatial intelligence lab, we developed a software suite, called SketchMapia, that supports the systematic and comprehensive analysis of sketch maps in experiments.

In this master thesis, you develop systematic test data for a sketch map analysis method and evaluate the SketchMapia analysis method w.r.t. its compleness, correctness and performance against other sketch map analysis methods. 

Spatio-Temporal & Semantic Analysis of Spatial Movement

When we compare the preformance of people navigation through the environment, we usually look at the distance travel, time spent or number of times a person is lost resulting in erroneous navigation decisions. This is a rather simple analysis: using a navigation app, we can collect much more information about the actual spatial behaviour.

Our GeoGame GeoGami, records the behaviour of people during a navigation tasks. Besides the actual travelled trajectory, GeoGami records the orientation, …

A thesis could explore how to make use of the collected data and aggregate it to meaningful measures of wayfinding performance. The thesis can also explore different visualizations of individuals and of individuals in comparison to others in a group.