In this course you will be expected to write code. The outcome of your software project should be a concrete contribution to the RIOT code base, and take the shape of one or more pull request(s) to the RIOT github (https://github.com/RIOT-OS/RIOT). Before you start coding, refer to the starting guide

https://github.com/RIOT-OS/RIOT/wiki#wiki-start-the-riot

Applications of Telematics

The software project can be taken during the semester or in a block during the semester break.

In this software project we will work on different tasks that relate with communication in various ways. The projects are mostly connected with research activities of the dependable systems group and lend themselves for being continued into a final project.

The objective of this course is to work on a larger software project in a team. It is important to come up with a software solution to the studied problem, not to develop the entire solution yourself, i.e. you may use available tools and programs if they help you to arrive at the software solution.

You may propose your own project and the topics we will work on are among others the following:

1. Email Archiving

This project is in collaboration with the archive of the Archiv der Max-Planck Society. It continues efforts from the previous semester. The objective is to develop a tool for convenient archiving and management of large sets of emails with various attachment types as well as for search in those emails and visual presentation of information that can be derived from the data sets.

The following two projects develop our distribution fitting tool HyperStar for correlated data further:

2. Calculating Fitting Errors

PH(PHase-type) distribution [1] is widely used in the statistical model, and it is important to know how good a PH distribution fits given samples. The goal of this project is to implement a tool that can calculate quality measures described in [2]. There are two parts in the measures: relative error in the moments and density absolute area difference. The tool will take a PH distribution and a data set as input, and output the fitting errors between the PH distribution and the data set. There are some mathematical work in this project, so apache commons math [3] may be useful if you can not find any mathematics libraries.

[1] https://en.wikipedia.org/wiki/Phase-type_distribution

[2] Bobbio, Andrea, and Miklós Telek. "A benchmark for PH estimation algorithms: results for Acyclic-PH." Stochastic models 10.3 (1994): 661-677.

[3] http://commons.apache.org/proper/commons-math/

3. Evolutionary Algorithms for PH Distribution Fitting

The purpose of this project is to use an evolutionary algorithm to improve a PH(PHase-type) distribution [1] fitting result of a data set. The recommended evolutionary algorithms include but not limited to genetic algorithm (GA) [2], differential evolution (DE) [3] and particle swarm optimization (PSO) [4]. A PH distribution and a data set are given in as input, the PH distribution is a fitting result of the data set, but it may be not very accurate. An evolutionary algorithm should be implemented to improve the accuracy of the given distribution. The fitness function can be the relative error in the moments between the PH distribution and the data set. Apache commons math [5] and Jenetics [6] may help you doing the coding work.

[1] https://en.wikipedia.org/wiki/Phase-type_distribution

[2] https://en.wikipedia.org/wiki/Genetic_algorithm

[3] https://en.wikipedia.org/wiki/Differential_evolution

[4] https://en.wikipedia.org/wiki/Particle_swarm_optimization

[5] http://commons.apache.org/proper/commons-math/

[6] http://jenetics.io/

4. Comparison of Android secure containers

A secure container, in a mobile security context, is an authenticated and encrypted area of an employee's device that separates sensitive corporate information from the personal data and applications. The project's main goal is to implement several secure containers on Android platform and compare their features including used encryption methods, supported security policies and integration with MDM (mobile device management). The recommended containers include but not limited to Samsung Knox[1], BlackBerry Balance[2], IBM MaaS360[3] and McAfee Secure Container[4]. Another objective is to run benchmarks in these containers to compare the performance overhead brought by implementing containers.

[1]https://play.google.com/store/apps/details?id=com.sec.enterprise.knox.express
[2]https://play.google.com/store/apps/details?id=com.blackberry.workspace.launcher
[3]https://play.google.com/store/apps/details?id=com.fiberlink.maas360.android.control
[4]https://play.google.com/store/apps/details?id=com.mcafee.apps.easmail&hl=en
 

• A. S. Tanenbaum, Modern Operating Systems, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall Press, 2007.
• Shelby, Zach, and Carsten Bormann. 6LoWPAN: The wireless embedded Internet. Vol. 43. Wiley. com, 2011.
• A. Dunkels, B. Gronvall, and T. Voigt, "Contiki - a lightweight and flexible operating system for tiny networked sensors." in LCN. IEEE Computer Society, 2004, pp. 455-462.
• P. Levis, S. Madden, J. Polastre, R. Szewczyk, K. Whitehouse, A. Woo, D. Gay, J. Hill, M. Welsh, E. Brewer, and D. Culler, "TinyOS: An Operating System for Sensor Networks," in Ambient Intelligence, W. Weber, J. M. Rabaey, and E. Aarts, Eds. Berlin/Heidelberg: Springer-Verlag, 2005, ch. 7, pp. 115-148.
• Oliver Hahm, Emmanuel Baccelli, Mesut Günes, Matthias Wählisch, Thomas C. Schmidt, "RIOT OS: Towards an OS for the Internet of Things," in Proceedings of the 32nd IEEE International Conference on Computer Communications (INFOCOM), Poster Session, April 2013.
• M.R. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L.A. Grieco, G. Boggia and M. Dohler, "Standardized Protocol Stack For The Internet Of (Important) Things", IEEE Communications Surveys and Tutorials, December 2012.
• J. Wiegelmann, Softwareentwicklung in C für Mikroprozessoren und Mikrocontroller, Hüthig, 2009