Intelligent Systems

Master Project - Intelligent Systems


Exercise (infMPInS-01a)

4 SWS, ECTS Credits: 10
Language of instruction: english
Time: Thursday 09:00 - 11:30 place to be announced
Lecture period winter term 2021-2022: 18.10.2021 - 22.2.2022
For more information UnivIS or Modulinformationssystem Informatik


October, 5th 2021 at 2:15 pm: Kick-off meeting for the Master Project Intelligent Systems  via this zoom link.
Meeting ID: 993 7924 7325 Passcode: 900778



This project is offered as a master project (If you are interested in a Bachelor/Master Thesis within the scope of this project, please contact Torben Schnuchel for more information)


This project is offered as a group task; a team of 3 to 6 students is required (deliverables are adjusted based on the number of participants).


Smartphones nowadays are equipped with a variety of sensors that developers have access to (Accelerometer, GPS, Bluetooth, ...). Since they collect this data from individuals, they are particularly well suited in crowdsourcing scenarios where multiple participants share data to build a central knowledge base. One exciting application scenario of crowdsensing is the monitoring of traffic to infer information about traffic flow, road conditions, crashes, etc. in a distributed manner.


What you are working with:
- Android Framework
- Open Source IOT Backend

What is your task as a group?

In this scenario, your imaginary customer is a city administration that wants to create a solution to collect information about the street network of a city. They want to enable automatic detection of road damage and thus be able to coordinate and optimize road maintenance. They want to try an approach that is based on crowdsourcing; this way, people can choose to contribute their data to the city through an app while driving. The shared data consists of the raw readings of the smartphone's sensors instead of aggregated statistics so that analysts can extract precise patterns from the data.

1) Create a dataset

You will be provided with a client/server setup to record data consisting of a sensing framework for Android smartphones and an open-source IOT backend. You as a team will then plan a data recording by specifying a track with varying street conditions (asphalt, sand, potholes, ...) and finally create a dataset on that specific track. To train a model for detecting the condition of the road approx 30-60min of data per student should be sufficient.

2a) Implementation/Evaluation of Road Surface prediction

- Study scientific literature and identify 3 models (e.g. different architectures of neural networks) that you will train on the task of road surface detection
- Train the models on the task of predicting the road surface and adjust the hyperparameters to the task

2b) Implementation/Evaluation of Privacy

- This kind of data sharing comes with severe privacy issues. Instead of sharing the raw data, you will investigate, if it is possible to
    1) predict the user from the raw data
    2) predict the user from intermediate representations of the model (e.g. activation of hidden layers)

2c) Optional: Evaluation Pruning (depending on the number of students)

Motivation: Deploy road detection models on smartphones. To do so, the models must shrink in terms of the number of parameters to account for the limited computing resources of smartphones.
- Rank the parameters of the networks according to some measure (e.g. magnitude, activation, ...)
- Evaluate the performance of networks with varying degree of pruning

Fundamentals of the module

Besides the actual development of the software, the module aims at practical experiences in the following aspects:
- Specification and definition of “product” using standard software engineering tools
- Setup of scientific experiments using standard Data-Science libraries like numpy/pandas/keras/tensorflow/...
- (Self-)Organisation as a team, management of the process, deadline supervision
- Entire software development process until delivery to the customer
- Documentation of the product
Your supervisors are continuously monitoring the process in the role of a customer, i.e. you are expected to regularly demonstrate the progress


Interested students may contact the group (i.e., Torben Schnuchel) for more details.