AIS2103 - IoT and Network Programming


New from the academic year 2022/2023

Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Portfolio assessment 100/100

Course content

The course contains a selection of the following topics, with an emphasis on Industrial IoT and network programming from an automation perspective:

  • Definitions, terminology, concepts, and standards within IoT, with an emphasis on modern communication technologies.
  • Enabling technologies, application areas, paradigms, infrastructure, and architecture for IoT, including data transmission, data storage, data analysis (including Edge, Fog, and Cloud Computing).
  • Typical sensors and actuators and basic measurement engineering.
  • Basic signal processing (e.g., antialiasing, noise reduction, Kalman filter), data processing and error detection (incl. wild-point detection) for IoT.
  • Common hardware, architecture, and programming for IoT, including embedded systems such as AVR (Arduino), ESP (Espressif), and ARM (Raspberry Pi).
  • Introduction to communication and networks for IoT, including common protocols (e.g., MQTT), technologies (mobil, WiFi, Bluetooth/NFC, satellite, LoRa/LoRaWAN, and more), topologies, layer models.
  • Practical introduction to basic Big Data, data analysis, and machine learning with the use of cloud services (e.g., Microsoft Azure).
  • Security and privacy for IoT.
  • Basic introduction to energy usage of IoT devices.
  • Practical problem solving with digital tools (e.g., Node-RED) and network programming for IoT.

More details on the curriculum will provided during the start of semester.

Learning outcome


  • The candidate can explain definitions, terminology, concepts, and standards within IoT, with an emphasis on modern communication technologies.
  • The candidate is familiar with a selection of enabling technologies, application areas, paradigms, infrastructure, architecture, sensors, and actuators for IoT.
  • The candidate is familiar with a selection of protocols, communication standards, topologies, and layered models for data communication within IoT, and can recognise and compare use of Edge, Fog, and Cloud Computing.
  • The candidate can compare common hardware, sensors, and actuators within IoT and suggest relevant solutions for well-known problems.
  • The candidate can explain common challenges related to security and privacy within IoT, explain common security mechanisms, and point out relevant solutions.


  • The candidate can perform measurements and basic analysis using common sensors, perform basic analog and digital signal processing, error detection, and data processing, and perform simple data logging, data transmission, and remote control within IoT.
  • The candidate is able to use a cloud solution with support for machine learning for simple data analysis.
  • The candidate can implement simple cyber-physical systems for IoT with emphasis on industrial use, including use of digital tools, network programming, and programming common hardware.

General competence

  • The candidate can employ relevant theory and modern methods across disciplines for the construction of innovative, sustainable, and ethically sound composed systems that also addresses security and safety.
  • The candidate can present relevant problems and communication-related solutions for people with a technical background.

Learning methods and activities

Learning activities generally include a mix of lectures, tutorials and practical lab/project work. A constructivist approach for learning is endorsed, with focus on problem solving and practical application of theory.

Further on evaluation

The final grade is based on an overall evaluation of the portfolio, which consists of a number of works delivered through the semester. The portfolio contains assignments that are carried out, digitally documented and submitted during the term. Both individual and team assignments may be given. Assignments are designed to help students achieve specific course learning outcomes, and formative feedback is given during the period of the portfolio. The re-sit exam is an oral exam.

Specific conditions

Admission to a programme of study is required:
Automation and Intelligent Systems (BIAIS)

Required previous knowledge

The course has no prerequisites.

It is a requirement that students are enrolled in the study programme to which the course belongs.

Course materials

An updated course overview, including curriculum, is presented at the start of the semester and will typically also include English material.

Credit reductions

Course code Reduction From To
IELEA2001 7.5 AUTUMN 2021
IDATA2304 7.5 AUTUMN 2021
More on the course



Version: 1
Credits:  7.5 SP
Study level: Intermediate course, level II


Term no.: 1
Teaching semester:  SPRING 2023

Language of instruction: English, Norwegian

Location: Ålesund

Subject area(s)
  • Computer and Information Science
  • Applied Information and Communication Technology
  • Engineering Cybernetics
  • Engineering
Contact information
Course coordinator:

Department with academic responsibility
Department of ICT and Natural Sciences


Examination arrangement: Portfolio assessment

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD Portfolio assessment 100/100
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU