Overview of study programme
The study programme consists of 66 credits and is divided into eight mandatory components worth a total of 36 credits, a project component worth 24 credits and an optional component (course/module) of at least 6 credits.
Students may choose to take the programme full-time or part-time in modules. Part-time students may only take Advanced IoT Networking Lab if IoT Communication Protocols and Context-Aware Wireless Embedded Systems are taken simultaneously and the IoT Project may only be taken in the graduation year.
Students who have already obtained credits for one or more of the programme components may choose to put together a personalised programme, in which the components they have already completed are replaced by optional courses. Personalised programmes must be requested at the time of study programme registration.
Students who are taking one or more optional components in the second semester may transfer an equal portion of their IoT Project to the first semester.
All programme components are taught in English, so students will need to have good spoken and written proficiency in this language. Students will receive a certificate when graduating.
Mandatory Programme Components
|Advanced Internet of Things Networking Lab||3 credits|
|Internet of Things Communication Protocols||6 credits|
|Context-Aware Wireless Embedded Systems||6 credits|
|Data Representation, Reduction and Analysis||6 credits|
|Internet of Things: Distributed Embedded Software||3 credits|
|Transforming Business||3 credits|
|Internet of Things: Security||3 credits|
|Internet Economics and Entrepreneurship||6 credits|
|Internet of Things Project||24 credits|
6 credits to be chosen from the list below
|Business Aspects of Software Industry||3 credits|
|Distributed Computing and Storage Architectures||4 credits|
|Industrial Seminars||3 credits|
|Big Data Science||6 credits|
|Knowledge Economy and the City||3 credits|
|Management and Performance Analysis
of Sensor Networks
|Transport Policy and Business: Concepts and Practice||3 credits|
Programme Components: Information
Advanced IoT Networking Lab
The Advanced IoT Networking Lab is an opportunity for students to put into practice the knowledge they acquired in the modules on Context-Aware Wireless Embedded Systems and IoT Communication Protocols. They are asked to build entire end-to-end IoT systems consisting of a combination of the following components: one or more embedded devices, a wireless IoT communication network, a gateway, a back-end system connected to the Internet, and a mobile application. In realising this system, they make use of off-the-shelf hardware, open-source software components and the latest IoT protocol standards.
Building these fully integrated systems enables students to acquire the necessary knowledge and insights into how total solutions come into being. Students are also required to analyse the strengths and weaknesses of their systems in terms of openness, scalability, management complexity, etc., as well as potential use cases that could be realised.
Context-Aware Wireless Embedded Systems
This module focuses on hardware-software interactions in low-power wireless embedded IoT systems. We discuss the impact of hardware and software design choices on performance and energy use. We also look at specific programming strategies for running embedded hardware systems efficiently and for determining the context for these systems (location, environmental parameters, etc.). We analyse how to use sensors efficiently and how to achieve optimum system integration with energy supply and communication modules in mobile embedded systems. The module also covers a number of indoor and outdoor localisation algorithms (proximity-based, attenuation-based, pattern-matching, etc.) and how hardware sensors can be used to improve these location estimates. All of these aspects of hardware-software interaction are taught through a mixture of lectures and practical exercises.
Data Representation, Reduction and Analysis
In the current era of data abundance, enormous quantities of data are continuously being collected from diverse information sources and in various domains, ranging from science and technology to business and telecommunications. Petabytes of high-dimensional data from multimodal imaging systems, social media, recommendation systems and large-scale research experiments all require advanced solutions for information representation, dimensionality reduction and data analysis. In response to these ‘big data’ challenges, this module teaches students the basics of signal processing and machine-learning tools, which allows them to detect, display, collect and process high-dimensional data from low-dimensional measurements.
Internet Economics and Entrepreneurship
This module addresses the characteristics of new media from various perspectives, including innovation economics and strategic management. Topics include web-based media and social media, but also smart environments and the Internet of Things. Using both a theoretical/conceptual framework and a number of case studies, we discuss important concepts related to innovation, standards, networks and platforms. The module also sheds light on certain business aspects that are relevant to entrepreneurship in new media, in relation to business models in new-media products and services. The students acquire deeper insights into these issues by applying their knowledge to a case study which changes from year to year.
Iot Communication Protocols
During the IoT Communication Protocols module, students learn which wireless communication systems and network protocols are available for building the Internet of things. The module discusses the challenges and current potential of connecting devices, as well as of building IoT systems that can work together. We look at the impact of the various communication protocols currently in use, ranging from MAC protocols and IP connectivity to embedded web services technologies. The technologies and protocols covered in the course include Zigbee, Bluetooth, IEEE802.11ah, SigFox, LoRa, SigFox, 6LoWPAN, CoAP, OMA LWM2M, etc. The students will also gain practical experience of using these protocols during lab sessions. At the end of this module, students will be able to assess the impact of IoT system design decisions (e.g. choice of communication technology, interaction model, etc.) and to choose and design communication protocols that suit the requirements of the application and the limitations of the embedded devices.
IoT Distributed Embedded Software
The module teaches students how to develop heterogeneously distributed software applications. They learn to take into account the particular constraints of each device (e.g. energy, computational power, price of resources) and the scalability requirements of IoT applications (rapid upscaling) when distributing software over various heterogeneous platforms. They also learn to test applications correctly. Besides component-based testing, simulation-based hyper-scale testing plays an important role in this. By the end of the module, the students should understand that they can only assess the limits and potential of an IoT application if it has been tested at the correct scale, and that simulation offers a useful solution.
This is the final component of the postgraduate programme. With the help of an individual supervisor, the students use the knowledge they acquired in the technical modules taught during the first semester to work out a total solution in the IoT context. The idea is that students demonstrate, in their total solution, portfolio, paper and public defence, that they are capable of selecting, adapting and combining the right technologies from each of the three key areas (communication, data and software). Their solutions should work reliably within the constraints of the IoT environment and take the social and economic aspects of the application into account.
One secret to maintaining a thriving business is recognizing when it needs a fundamental change. Seven out of ten companies are engaging in business model innovation, and an incredible 98% are modifying their business models to some extent. Business model innovation is about fundamentally rethinking your business around a clear—though not always obvious—customer need, then realigning your resources and processes with this new value proposition. In the module “Transforming Business” you will learn about different business model innovations from a value creation, financial and human resources perspective. You will apply this in a business case and enhance your understanding of developing and transforming to a new business in discussion with your fellow students. Not only the strategic aspect, namely “what could a future oriented business model look like”, but also the people aspect, namely “how can you get your team on board” are subjects of the module.
This module teaches students to have a clear understanding of the available IoT platforms and their security and privacy aspects in order to evaluate the existing systems. They will get to know technology that tackles frequent security issues: security techniques on a system level, on a network level, on an application level in order to assess the applicability (and the corresponding trade-offs). Students will become familiar with the most important building blocks of a security solution within the IoT context: authentication, authorisation, cryptographic components, key management, etc. They will be able to select these building blocks (keeping in mind the pros and cons) for a specific architecture. This module also teaches students to apply existing methods for security and privacy analysis and design to IoT systems and their applications. Students, as security architects, will be able to apply the aforementioned knowledge to a specific case study and also be able to assess which innovations will be available within the next 5 years based on an overview of worldwide leading research.
Big Data Science
The aim of this course is to acquaint students with the most important aspects of big data science. Students who pass the course will be able to make well-considered choices about data-management solutions. They will have a solid understanding of a wide range of big data algorithms and be capable of developing custom-made visualisations.
Business Aspect of the Software Industry
Software is a pervasive technology: everybody knows Microsoft and Google; 40% of all high-tech start-ups in Flanders are software companies, software is a major part of the activity of many other companies. This course provides insights in the unique properties of this sector and the IT sector in general, and prepares students for a career (as entrepreneur or business professional) in these fascinating industries.
Distributed Computing and Storage Architectures
Modern applications, including video recommendation systems, video search and retrieval, and large-scale scientific experiments, involve the acquisition and analysis of petabytes of high-dimensional data. Distributed computing refers to a large collaboration between networked processing units that allows for their processing capacity to be put at the service of a large problem. Nowadays, many systems and applications are being distributed for a variety of reasons: Fault-tolerance, processing performance, security as well as geographical spreading of the data or the problem requirements.
This course digs into the internals of distributed computing and storage architectures, with particular emphasis on algorithms and techniques that underlie today’s distributed computing systems. Topics addressed in this course include: Modeling of distributed computation, introduction to clouds (map reduce and key-value store), distributed shared memory, distributed compression algorithms with application in distributed file synchronization, authentication, distributed process scheduling, distributed optimization algorithms (e.g., Gossip, consensus, pulse-coupled oscillators).
Ten different speakers from the professional IoT field.
Knowledge Economy and the City
This course investigates the knowledge economy – as an object of theory, empirical phenomenon and socio-economic and policy imaginary – from a cultural economic perspective. The knowledge economy has become a central point of reference in describing contemporary sociospatial transformations. City regions occupy a key position within this narrative as the knowledge economy tends to concentrate within cities and also contributes to further urbanization. This raises questions concerning the spatiality of knowledge production, circulation and use that will be investigated in more detail in this course.
The course adopts the format of a reading seminar in which short lectures on the topic are combined with reading and discussion of relevant literature. Drawing on interdisciplinary literatures from geography, spatial planning, urban studies, ethnography, science and technology studies and organization studies, the course will focus on four important spaces of knowledge: educational spaces, creative and cultural spaces, experimental spaces and smart spaces. The aim of this investigation is to gain a more critical understanding of the presumed role played by ‘knowledge’ in urban processes and to analyse the ways in which these various spaces of knowledge shape our understanding of the contemporary city.
Management and Performance Analysis of Sensor Networks
The lecture offers the students a background needed for the management and deployment of “The Internet of Things”. It introduces the different aspects of network management such as configuration, performance and security management. Those aspects will be applied on a Wireless Sensor Network example.
- 3D printing techniques: overview, limitations and advantages of each technique
- FDM (fused deposition modelling) in detail:
- Material types and properties
- Extruder working principle
- Techniques for guaranteeing the quality of objects printed with FDM
- OpenSCAD 3D modelling software
- Laser cutting:
- Working principle
- PCB design:
- Design software (Eagle)
- Choosing components
- Soldering techniques
- From idea to PCB (component choice, etc.)
Transport Policy and Business: Concepts and Practice
- The Traffic and Transport System – E. Onghena
- Logistical Costs – H. Van Lier
- Waiting time in transport – E. van Hassel
- Routing – K. Sörensen
- Investing in vehicles – K. De Langhe
- Company case: Transport Joosen
- Demand for transport: modeling – D. Borremans
- Charging and pricing policy – T. Verlinden
- Infrastructure policy – E. Moschouli
- Regulatory policy – F. Troch
- Policy case – City of Antwerp
- Urban logistics: delivery concepts – I. Cardenas
- Urban logistics case: Bubblepost
- Innovation in transport: IT platforms – V. Carlan
- Port of Antwerp case – Nallian