Master's Programme in Information and Communication Technology
Artificial Intelligence, 5-year master programme
Language of instruction
MA-430-G Probability Theory and Stochastic Processes. Algebra and Calculus for Engineers, Basic programming skills, Basic knowledge of Computer Networks.
On successful completion of this course, the students should be able to:
- understand the essential theoretical tools (communications, networking protocols, optimization, signal processing and control) that are necessary to cope with Wireless Embedded Systems and Sensor Networks
- provide the basic understanding and engineering criteria about how to design, implement (programming) and deploy practical wireless sensor networks (WSNs) for different applications
- understand the advantages and limitations of different technologies used in the design and implementation of WSNs
The course covers the following topics: Introduction, motivation and applications of WSNs, node architecture, hardware platforms, operating systems, characteristics of MAC protocols for WSNs, contention-based MAC protocols, contention-free MAC protocols, routing protocols for WSNs (datacentric routing, proactive routing, on-demand routing), power management schemes, time synchronization protocols, collaborative data gathering, cooperative in-network data processing, iterative methods for distributed computation and inference (e.g. decentralized estimation, detection, control, learning), consensus and gossip self-organized algorithms, cross-layer design, application of network optimization tools, sensor network programming, node-centric programming, dynamic wireless re-programming, introduction to middleware and WSN management. Some of the concepts will be illustrated with practical examples drawn from state-of-the-art standards and implementation on real mote devices.
Lectures, laboratory assignments, self-study and group work.
The work load for the average student is approximately 200 hours.
Assessment methods and criteria
The evaluation will consist of two parts: a) final written exam of 3 hours (60 %), b) Portfolio with homework and laboratory assignments (40 %). Graded assessment. Information about the portfolio will be given in LMS at the startup of the course.
The study programme manager, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.