Vorlesungen: Internet of Things
The goal of the course is to provide a survey of the state-of-the-art on aspects of the Internet of Things. What is really the best way to operate communication networks? What are the fundamental limits to communicate over IoT networks? Which is the optimal strategy? Is secure or confidential communication possible? What features will future IoT networks have? Those are the questions addressed in this course, which provides strategic guidance and guidelines for the design of IoT networks. The following aspects will be introduced throughout the course:
- Network Coding: Network coding is a modern communications technique that can vastly reduce the duration of transmission. This is done by combining packets at intermediate nodes in a multi-user communication scenario. This combination is done in a certain way that allows all users to recover their packets. Both physical-layer and non-physical-layer network coding will be discussed.
- Full-duplex Communications: In order to handle the increasing demand for higher data rates in wireless communication systems, there is rising interest in transmitting and receiving at the same time and frequency, therefore reusing resources. The fundamental concepts and of full-duplex transceiver designs are introduced. Furthermore, recent advances of practical implementations are discussed as well as still existing challenges.
- Caching: Caching is the technique to reduce the peak traffic rates by pre-fetching the popular contents into end-users memoires. The goal of this module is to introduce the state-of-the-art tools used in the literature and to highlight the benefits offered by cooperative caching even though the users memories may not be connected physically.
- Delay-limited Communications: In many applications, it is essential that communication takes place within a finite duration in order to ensure low-latency. In this module concepts such das delay-limited capacity and coding for finite block length are introduced and discussed.
- Side-information in Communications: In the big data era, information is omnipresent, thus available at either or both sides of the communication. How to take this prior knowledge as an advantage to achieve a more robost, efficient and secure communication, is of great interest. In this module, the basic models and coding techniques will be introduced, as well as their real-life applications in watermarking (e.g. copyright protection) and data compression (e.g. 3D video coding).
- Physical Layer Security: In the conventional network, secrecy issues are dealt mainly in the higher layers of protocols stacks by cryptographic means. Physical layer security offers an alternate approach by utilizing the randomness of physical layer to hide information from adversaries. In this module the goal is to give an overview of basic models, for instance, wiretap channels and connections to practical system design.
- Channel Coding: Channel coding is a technique used for controlling errors in data transmission over noisy communication channels. In this module, the fundamental issue how the probability of error drops with respect to the code rate/length, as well the lastest development of the capacity-approaching codes, principally turbo codes and LDPC codes, will be addressed.
The course is relevant to graduate students interested in communication and signal processing. The presentation style of the lectures is rather informal, favoring broad intuition over mathematical rigor. The technical (and finer) details have to be investigated by the students within the project part of the course. The course is taught from a signal processing and communication theory perspective with occasional deviation to information theory. Fundamentals as well as several advanced topics such as
- convex optimization and algorithms,
- information theoretic privacy and security,
- network information theory,
- wireless communication theory
are covered, depending on the interests and background of the students. All the above mentioned topics are of high importance for current networks and also useful in developing new techniques and algorithms for future IoT networks.
- Mathematics I-IV
- Communications Engineering
- Signals and Systems
- Probability theory
- Information theory
- Wireless Communication
- Boyd, S., Vandenberghe, L. "Convex Optimization", Cambridge University Press, 2004
- Cover, T., Thomas, J. "Elements of Information Theory", Wiley & Sons, 2006
- El-Gamal, A., Kim, Y.-H. "Network Information Theory", Cambridge University Press, 2011
- Bertsekas, Dimitri P. "Nonlinear Programming", Athena Scientific, 1999
Weitere Informationen zu dieser Vorlesung finden Sie unter folgendem Link:
Fakultät für Elektrotechnik und Informationstechnik » Studium » Lehrveranstaltungen » Theoretical Information Technology