ADSE1310 Internet of Things Course description

In this course, the students will gain an understanding of some of the most important principles of the Internet of Things (IoT). IoT enables the development of unique, innovative products and services. The students will become familiar with architecture and intelligent algorithms that govern IoT and pervasive computing. The goal of the course is to give the students an overall understanding of IoT: from a technical point of view - and in relation to the consequences for society (for example privacy preservation, security) - when billions (or trillions) of units communicate with each other in ‘the cloud’.

On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:

Knowledge

The PhD candidate

• is able to conduct bioinformatics analysis projects in agreement with best practice (transparency and reproducibility) in the field of bioinformatic science's philosophy
• is in the forefront of knowledge about the current high throughput sequencing (HTS) technologies and understands the differences, benefits and drawbacks of these HTS technologies
• can evaluate and make sound decisions on which platform and bioinformatic approach to use for different HTS projects.
• Can contribute to development of new knowledge and interpret results from various HTS applications

Skills

The PhD candidate can

• Plan a HTS research project and choose optimal sequencing platform
• Carry out the relevant bioinformatic analyses both on the command-line (unix) and R-studio, and utilize web-based resources like Galaxy server and Genbank E-utilities.
• Interpret the results of bioinformatics analysis of HTS (e.g. reliability, sensitivity and specificity) and judge their value for answering biological questions
• Disseminate the results of HTS based research

General competence

The PhD candidate can

• argue in favour of particular HTS technologies or bioinformatic approaches on the basis of current knowledge
• argue in favour of the kind of materials and the number of samples to select/include in different kinds of HTS projects
• can participate in discussions on HTS methodology

No requirements over and above the admission requirements.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence:

Knowledge

The student

• has a basic technical understanding of the functioning of Internet og computer networks
• has an overview of the most important principles in pervasive computing, and the Internet of Things, which include wearable devices, context aware computing, health monitoring, smart houses, crowd sensing, smart grids and ambient intelligence
• understands the basic technical principles behind different algorithms for autonomous control in the Internet of Things
• has an understanding of how the Internet of Things and pervasive computing affect security and the protection of privacy in our society
• has good knowledge of the scientific advances and technology leaps that enabled the Internet of Things

Skills

The student

• masters basic concepts and has an overview of algorithms used on the internet and in data communication
• is capable of conceptualising architecture for solutions based on the Internet of Things and pervasive computing

General competence

The student

• is capable of designing solutions based on the principles behind the Internet of Things and pervasive computing

Lectures and supervisory sessions. The students work individually.

The following coursework is compulsory and must be approved before the student can take the exam:

• Three compulsory assignments. Each assignment corresponds to 30 hours’ work.

The deadlines for submitting the compulsory assignments and other details are stipulated in the teaching plan made available at the start of the semester.

Exam spring 2022:

Portfolie exam.

The portfolio consist of different parts over a period of three months. All parts of the portfolie must be passed in order to pass the course.

The portfolie will be graded overall.

The exam result can be appealed.

In case of postponed exam a processed version of the portfolio can be submitted.

[Exam earlier:]

An individual three-hour written exam.

The exam result can be appealed.

All aids are allowed as long as the rules for source referencing are followed.

The course will introduce students to basic bioinformatics including best practice when setting up and managing bioinformatics projects. The course covers introduction to high throughput sequencing technologies and will give students hands-on experience with the analysis of data from various sequencing platforms. Applications that are included in the practical part are processing of raw data reads, control of quantity and quality of data (FASTQC), expression analysis of small RNA sequencing data (miRNA) and transcriptome sequencing/microarray (mRNA-seq, cDNA) data, and detection of variation (e.g. SNPs) after resequencing (variant calling).

Admission requirements

This course is primarily aimed at PhD candidates admitted to the PhD programme in Health Sciences and PhD students from Memorial University, Newfoundland. General terms for admission to the course is a completed master's degree in molecular biology or equivalent qualification (e.g. completed MABIO4400). Priority will be given to PhD candidates from OsloMet and Memorial University, Newfoundland.

Note that all students must have a laptop not more than 2 years old (windows 7 or more recent or mac with OS X). The laptop must be able to connect to wireless network.