Curriculum on Self-Organizing Networked Systems

Curriculum

Core Courses

The following courses are mandatory. About 60 ECTS.

Introduction to Self-Organizing Networked Systems

• Part I (30h): What is a self-organizing system? Methodology and theory. Giving the big picture. Few mathematics, non-technical. History. Evolution, cybernetics, chaos, emergence, entropy. Links to following courses. Similar to course "complexity and evolution" in Brussels.
• Part II: Case studies. A ring lecture (colloquium) with speakers from different universities and different fields (which can also attended by all university members):
  • Self-Organization in Communication and Computer Networks
  • Social insects
  • Immune system
  • Social group dynamics
  • Self-Organization in Biology
  • Brain dynamics
  • Robotics
  • Chemical reactions
  • Self-organization in linguistics
  • Overview of cellular and evolutionary biology

Dynamical Systems

Lecture and Lab

Content: similar to "Nonlinear Dynamics and Chaos" by Steven H. Strogatz

Textbooks:

• Nonlinear Dynamics and Chaos by Steven H. Strogatz

Network Theory

Lecture and Exercises

Content: Networks from the real world. Network topology: Graph theory basics, random graphs, phenomena small wold and scale-freeness. Network functions/processes/algorithms: E.g. search, percolation.

Textbooks:

• The structure and function of complex networks by Newman
• Dynamical Processes on Complex Networks

Information and Communication Theory

Lecture and Exercises

Textbooks:

• Understanding Information Transmission
• Elements of Information Theory

Stochastics and Statistical Physics

Lecture and Exercises

Content: Selected topics from the following fields: Review of random variables, combination of RVs, Stochastic Processes (Concepts, random walks and other applications, Markov chains, Markov processes and queueing theory). Statistical Physics .

Textbooks:

• Probability, Random Variables, and Stochastic-Processes by Papoulis
• Statistical Physics of Particles by Mehran Kardar
• Fundamentals of Statistical and Thermal Physics by Frederick Reif

Intelligent Systems

Neural networks: introduction to biological neural networks, Artificial Neural Networks, topologies (Multilayer, recurrent, fully meshed), Self-Organizing Feature Maps, spiking neural networks, emergent patterns in ANN

Machine learning: Genetic algorithms, simulated annealing, Sintflut algorithm, rule-based learning, bayesian networks

Modeling and Simulation

Contents:

• Model building, modeling and specification languages (e.g. UML)
• How to do good simulations and data analysis, model validation?
• How to do good empirical studies?
• Data/results representation
• Cellular automata, evolutionary algorithms, multi-agent systems, PDEs
• Group work

Related books:

• The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling

Algorithms and Data Structures

Lecture and Lab.

Content: Sorting and searching, tree-based structures, graph algorithms (over), recursive algorithms, complexity classes and computational effort.

Textbooks:

• The Algorithm Design Manual by Steven S. Skiena
• Introduction to Algorithms by Charles E. Leiserson, Ronald L. Rivest, Clifford Stein

Introduction to Control Theory and Optimization

control loop, stability, distributed control, event-based control, MIMO control systems

Social interactions

• ...
• Group dynamics
• Game theory: Cooperation (Prisoneer's Dilemma), economic behavior

Catch-up Courses

Depending on the background of the student, she or he attends a subset of the following courses:

Specialization 1: Communication Networks

Mobile and Wireless Systems

Contents: Introduction and overview. Radio propagation. Coding, modulation, and duplexing. Multiple access and cellular concept. Medium access control (MAC) protocols. Wireless LAN 802.11. Network architecture and mobility protocols. Security in mobile networks. Multihop networks. Economic, health, and social aspects.

Group Work

Curriculum table

Subject	Type	g1 grad	g2 und.grad	g2 grad	g2 grad spec.	g3	g4
Dynamical Systems	Lecture	X		X
Graph and Network Theory		X
Optimization		X
Probability and Stochastic		X
Information Theory	Lecture			X
Algorithms 1	Lecture & Lab			X
Algorithms 2	Lecture & Lab			X
Distributed Algorithms	X
Game Theory	X
Numerical Simulations	Lecture & Labs			X
Modeling and Simulation	Lecture & Labs	X
Topics course SO in nature/society	Lecture			X
Network 1	X				X
Network 2	X				X
Statistical physics 1 & 2					X
Embedded Systems					X
Sensors and Robotics					X
Calculus 1 & 2	Lecture		X
Statistics	Lecture		X
Diff. Equations	Lecture		X
Linear Algebra	Lecture		X
Scientific Programming	Lecture	X	X
Natural sciences	Lecture		X
Mobile&Wireless Systems		X
Sensor Networks		X
Peer-to-Peer Networks		X
Information Theory and Coding		X
Protocol Engineering		X
Physics		bacc
Applied Mathematics		bacc
Biology		bacc

Group 1: Bauschert, Bettstetter, Pletzer, Quaritsch, Yanmaz

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Group 2: Anton, Manfred, Felix, Johannes,Alain

All courses should specifically deal with applications towards SOS.