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Before choosing course

Wireless Sensor Networks (WSNs) are networks of small, autonomous nodes equipped with wireless transmission and sensing capabilities for a huge variety of applications, such as energy efficient buildings, healthcare, transportation systems, industrial automation, and smart grids.WSNs have the potential of dwarfing the revolution that the Internet has brought to the world of computing, entertainment, work, and human interaction by the creation of the Internet of Things.

The focus of the course is on theoretical aspects of distributed algorithms, optimization, and on their application to WSNs. The course presents iterative methods for distributed computation and network optimization, and shows how to use these methods to design key aspects of WSNs protocols and applications. The course also includes a lecture for programming sensors, which may be useful for experimental research projects.

Course offering missing for current semester as well as for previous and coming semesters
* Retrieved from Course syllabus FEL3245 (Spring 2019–)

Content and learning outcomes

Course contents

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Intended learning outcomes

After finishing the course, the attendant will

  • Know the essential theoretical tools to cope with WSNs.
  • Know the fundamentals of parallel computation and network optimization.
  • Know how to design WSNs.
  • Develop a research project.
  • Develop presentation skills.

Course Disposition

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Literature and preparations

Specific prerequisites

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Recommended prerequisites

Fairly elementary, i.e., mathematical maturity with familiarity with linear algebra and analysis.


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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • EXA1 - Examination, 7,0 hp, betygsskala: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

The lectures will be mainly based on blackboard and slides. Students have to present a paper/book chapter per lecture.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Profile picture Carlo Fischione

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web FEL3245

Offered by

EECS/Decision and Control Systems

Main field of study

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Education cycle

Third cycle

Add-on studies

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Postgraduate course

Postgraduate courses at EECS/Decision and Control Systems