Internet of Things

Intelligent environments in next-generation networks

Starting Date: October 2021

Duration: 4 semesters (part time: up to 8 semesters)

ECTS: 120

until 5/9/2021

Digital Revolution and the Internet of Things

The objective of the MSc programme is to provide systematic and specialized training in the design and development of intelligent environments in next-generation networks, using the Internet of things as a baseline. Overall, the programme has been planned to take the following points into consideration:

  • The Internet of things forms a new stage in digital revolution, contributing to the expansion of the Knowledge-Based Society (Information Society).
  • Many facets of the Knowledge-Based Society have been using or will use the Internet of things in order to improve existing processes or create new and innovative services (smart home, smart cities, smart industry, energy, health, etc.).
  • The design and development of intelligent environments in next-generation networks requires the combined application of knowledge that derives from different disciplines.
  • Specialized staffing needs for career development opportunities.
  • Developments in ICT and increase of the provided critical / sensitive services.
  • The MSc offers a great opportunity for developing background knowledge for further PhD studies.

Graduates will:

  • have an understanding of the available resources and tools for developing an Internet of things (IoT) system, in terms of both hardware and software, network connection and data analysis.
  • set up and design systems that are based on IoT.
  • understand the principles of analysis, design, and implementation of intelligent environments in next generation networks
  • manage information and knowledge that is dispersed, in different forms, across complex and dynamic environments.
  • set up and design modern network and communication systems and interconnect them with IoT.
  • design and manage innovative, digital and interactive applications with a wide range of uses.
  • understand the place and the role of IoT in the broader ICT industry, as well as its possible future developments.
  • understand the role of hardware and sensor networks in an IoT system.
  • understand the role of big data, data mining and cloud computing in a typical IoT system.
  • understand the role of security and privacy in future Internet systems.
  • understand the capabilities of machine learning tools and techniques in implementing intelligent environments.
  • understand the limitations of wireless and local network access and the degree to which these limitations affect IoT performance.
  • design, implement and evaluate IoT systems, which may consist of sensors, processing systems of small, average and big processing power, wireless networking, interconnection with third-party platforms (e.g. social networking sites, web services, corporate systems), data analysis and display, for a wide range of uses.
  • participate in research and development projects in the field of information and communication systems, generating new areas of knowledge.
  • compare and evaluate digital goods and services based on a solid background knowledge of modern network technologies, as well as manage and supervise complex and demanding communications projects.
  • acquire new knowledge in order to adapt to complex and dynamic environments and users.

This postgraduate programme is in accordance to the clauses of Law 4485/17, in combination with Law 4009/11, and is subject to the Regulations of Studies for the postgraduate programmes of studies, effective after their validation from the University’s Special Configuration Senate.Greek Government Gazette issue for the establishment of the postgraduate programme: 2965/B/24.07.2018

Entry Requirements – Candidates Evaluation

  • Degree or diploma holders in informatics and communications, engineering, sciences, or equivalent fields of study, from Greek universities or Technical Educational Institutions, or equivalent international institutions that are officially recognized by the Greek state.
  • Graduates from Greek military higher education institutions (Article 88 of Law 3883/2010, Greek Government Gazette issue 167/24-9-2010, Α).
  • Graduates from the Officers School of the Hellenic Police Academy (Article 38 of Law 4249/2014, Greek Government Gazette issue 73 Α).
  • Students of Greek higher education institutions that are in their last year of studies, provided that they will have sent a certificate of completion of studies before the deadline date for application to the MSc programme.

Candidates are evaluated according to the criteria specified in Article 8 of the Regulations of Studies for the programme of postgraduate studies (available on the website).

Syllabus

Compulsory Courses
Course ID: 4001 Machine Learning ECTS: 7.5
Inductive learning: supervised, unsupervised and reinforcement learning. Concept learning. Decision trees. Artificial neural networks. Bayesian learning. Instance-based learning (k-nn locally weighted regression, radial basis functions). Support vector machines (linearly separable and non-linearly separable problems, kernel methods). Ensemble learning (bagging, boosting, random forests). Genetic algorithms and genetic programming. Semi-supervised learning methods. Reinforcement learning (Q-learning, temporal difference learning). Experimental evaluation of classification methods (ROC curves, cost curves). Application examples.
Course ID: 4002 Design, Development and Performance Evaluation of Next-Generation Networks ECTS: 7.5
Presentation of the most advanced network technologies and methodologies (NAT, IP multicast, WEP, ΙΕΕΕ 802.1Χ, 802.21, etc.), architectures (MPLS, DiffServ, IntServ, etc..), protocols (RSVP, Mobile IP, IPv6, OSPF, BGP, etc..) and services (WebTV, IPTV, p2p, v2v, CDN). Topics on active services with capabilities such as self-organisation, environmental intelligence and adaptation to underlying network infrastructure, spatial position awareness and multimodal interfaces exporting for costing algorithms, protection, mobility and quality of service assurance. Methods and tools for assessing performance in modern heterogeneous systems. Architectures and Internet of things services. Integrating IoT to existing network infrastructures and ROI.
Course ID: 4003 Pervasive Computing Systems ECTS: 7.5
Introduction to pervasive computing and pervasive computing systems (PCS). Context aware systems. Design issues of PCS. Architectures, programming models and frameworks. Location tracking in pervasive computing. Processing sequential sensor data. Applications from smart objects collaboration. User interfaces in pervasive computing. End-user development of IoT applications.
Course ID: 4004 Algorithms, Combinatorial Optimization and Financial Applications ECTS: 7.5
Combinatorial optimization (CO) studies algorithms that compute the optimum solution amongst the feasible solutions of a combinatorial problem. A milestone of the theory is the understanding of linear/convex problems. Combinatorial problems capture the intrinsic complexity of the most important problems for computers. Due to this, during the last 50 years CO has played a central role in exploring the power and limitations of computers. However, new problems have arisen during the current decade, due to the power of computers and the explosion of the Internet. These problems concern the independent, rational interplay of a large number of computers in the Internet, which are motivated by greedy objectives or coordinated play. These problems lie within an interdisciplinary area of research, such as CO, computer science, game theory and economic theory. An important subject is the study of bimatrix games because these essentially capture the selfish behavior of individual players. Also important is the study of selfish network flows in large-scale networks and the computation of their steady states. It is obvious that, in many situations, users’ selfish behavior can lead the Internet/system to a suboptimal state. This clearly shows that mechanism design is a key area of study.
Compulsory Courses
Course ID: 4005 IoT Technologies and Applications ECTS: 7.5
IoT design and architectures. Abstract information models and middleware. Distributed experimental platforms for IoT testing. TVWS, energy and mobility IoT management. IoT-based business accelerators. Digital agriculture based on IoT systems. Commercial applications for IoT agriculture (e.g. FarmBeats).
Course ID: 4006 IoT Communication Technologies ECTS: 7.5
Propagation mechanisms, propagation loss models, fadings, channel characterization and impact on communications systems. Transmission and multiple access techniques, spectrum propagation techniques, CDMA, OFDM and OFDMA. Diversity techniques (SIMO and MISO systems) and spatial multiplexing (MIMO systems). IEEE 802.11 wireless local area networks, wireless ad-hoc networks and sensor networks. Low-power and low-distance protocols (Bluetooth Low Energy, Zigbee, NFC, SigFox, NB-IoT and Low-Power Wide Area), as well as long-distance ones (LTE version 12 and 13 / LTE-M2M). Relay technologies and architectures. Device-to-device (D2D), machine-to-machine (M2M) and vehicle-to-vehicle (V2V) communications and case studies.
Course ID: 4007 Embedded Systems and IoT ECTS: 7.5
The goal of this course is to familiarize students with issues concerning hardware/software design, interfacing and interaction, practical microprocessor-based system design, practical digital hardware design for embedded systems using modern logic synthesis tools, as well as with the implementation of embedded, low-power digital systems to be used as nodes of an Internet of things (IoT) type of network. More specifically, it includes: introduction to embedded systems, hardware/software interfaces, PS/2 keyboard, serial communication, USB, Ethernet, video handling, memories and their utilization in embedded systems, microprocessors, microcontrollers, FPGAs and ASICs, programming of embedded systems with OS for network applications, microprocessor/sensor interfacing, use of messaging protocols in IoT networks.
Course ID: 3008 Future Internet Security and Privacy ECTS: 7.5
Future Internet security. Foundations of information privacy. Privacy enhancing technologies. RFID technology: Security and privacy protection. Sensor networks security. Cloud computing models. Risks and vulnerabilities. New security solutions. Security and privacy protection for smart environments, implantable devices and embedded systems.
Compulsory Courses
Course ID: 4008 Robotics and Computer Vision ECTS: 7.5
The course includes basic elements of robotics, while it also discusses computer vision suitably adapted for robotic systems. Simplified computational vision and image processing algorithms that can run in real time on a robotic system, given the limitations and lack of precision that often exist. Students learn to design, create, plan and evaluate robotic vision systems.
Course ID: 4009 Modern Networks and IoT Interfacing ECTS: 7.5
Managing network resources in third and fourth generation broadband wireless networks (UMTS, LTE, LTE-A). Qualitative differentiation of H2H (human-to-human) and M2M (machine-to-machine) data flows. Management of inbound network load, data transmission delay, quality of service (QoS) and data flow scheduling. Fifth-generation networks and tactile Internet. Transmitting kinaesthetic and tactile feedback, fields of application and technological challenges. Innovative network resource management techniques based on network virtualization technologies (software-defined networking – SDN, network enhancement virtualization – NFV, network slicing), and network access improvement by densifying the access points and utilizing heterogeneous network technologies seamlessly.
Course ID: 4010 Semantic Web ECTS: 7.5
Introduction to the Semantic Web, vision and principles. Technologies for structured documents, data and knowledge representation (XML, RDF, RDFS, OWL). Ontology engineering and Semantic Web applications. Ontology-based access, integration and retrieval of (large volumes) heterogeneous data and knowledge (SPARQL, OBDA). Linked (big) data. Semantic Web and Internet οf things (IoT), Semantic Web of things. Semantic interoperability in IoT. Semantic modelling of trust in IoT.
Course ID: 4011 Big Data and Data Mining ECTS: 7.5
Big data and social behaviour identification, dimensionality reduction in big data, big data from web retrieval and manipulation technologies, opinion mining and sentiment analysis: sentiment classification, aspect-based opinion mining, summary creation, argument extraction, information fusion: schema preprocessing, template matching, web mining: data collection, preprocessing, data modelling. Opinion mining: sentiment classification, opinion comparison. Wrappers: instance-based wrapper learning, DOM trees and automatic creation from trees. Web crawling: general purpose crawlers, focused crawlers, local crawlers. Link analysis: social networks mining, bibliographic references matching, information retrieval algorithms. Semi-supervised learning: expectation – maximization, transduce support vector machines, mining from positive and unlabelled examples. Unsupervised learning: geometrical methods, generalized models, visualization through integration (SOMs, multidimensional scaling, and projections), collaborative filtering. Supervised learning: random forests, adaboost/bagging/boosting, Bayesian networks. Sequential mining.
Course ID: 4000 Msc Thesis Compulsory ECTS: 30

Part-time students

1st Semester
  • Pervasive Computing Systems
  • Design, Development and Performance Evaluation of Next-Generation Networks
2nd Semester
  • IoT Communication Technologies
  • Embedded Systems and IoT
3rd Semester
  • Machine Learning
  • Algorithms, Combinatorial Optimization and Financial Applications
4th Semester
  • IoT Technologies and Applications
  • Future Internet Security and Privacy
5th Semester

(two courses)

  • Robotics and Computer Vision
  • Modern Networks and IoT Interfacing
  • Semantic Web
  • Big Data and Data Mining
6th Semester
  • MSc Thesis
7th Semester

(two courses)

  • Robotics and Computer Vision
  • Modern Networks and IoT Interfacing
  • Semantic Web
  • Big Data and Data Mining
8th Semester
  • MSc Thesis

Academic Personnel

The MSc’s courses are taught by eleven faculty members. Their academic and research profiles are briefly listed below.

CourseSemesterCourse InstructrorWebpage
Machine Learning1Professor
Efstathios Stamatatos
URL
Design, Development and Performance Evaluation of Next Generation Networks1Professor
Charalampos Skianis
URL
Pervasive Computing Systems1Dr. Dimitrios Stratogiannis 
Algorithms, Combinatorial Optimization and Financial Applications1Assistant Professor
Alexios Kaporis
URL
IoT Technologies and Applications2Professor
Georgios Kormentzas
URL
Embedded Systems and IoT2Assistant Professor
Emmanouil Kalligeros
URL
IoT Communication Technologies2Professor
Demosthenes Vouyioukas
URL
Future Internet Security and Privacy2Assistant Professor
Georgios Stergiopoulos
URL
Robotics and Computer Vision3Associate Professor
Ergina Kavallieratou
URL
Modern Networks and IoT Interfacing3Assistant Professor Dimitrios SkoutasURL
Semantic Web3

Associate Professor
Theodoros Kostoulas

Associate Professor
Panagiotis Symeonidis

URL

 

URL

Big Data and Data Mining3

Associate Professor
Theodoros Kostoulas

Associate Professor
Panagiotis Symeonidis

URL

 

URL

 

Fees and Funding

Successful applicants for the postgraduate studies programme are required to pay tuition fees, in accordance with Article 35 of Law 4485/2017. The tuition fees amount to €3,000 and can be paid in four installments. The tuition fees cover operational expenses of the postgraduate programme of studies, including needs in specialist teaching staff from universities in Greece or abroad. Tuition fees payment can be made as follows: €500 within 15 days after the student’s application has been accepted, €1,000 upon the student’s admission to the 1st semester of studies (early October), €1,000 at the beginning of the second semester (February) and €500 upon the student’s admission to the 3rd semester of studies. No refunds can be given for fees or part of fees that have already been paid.

The postgraduate studies programme may offer a number of grants, conditional on academic performance, to full-time students. The amount of funding, requirements, method of funding, as well as the rights and responsibilities of grant-holders are decided by the Department’s Assembly (par. 4, Article 35 of Law 4485/2017).

Full-time/part-time study

The full-time study programme is completed in four academic semesters and combines blended and model types of learning, including: [a] a four-day intensive lecture cycle held at the Department’s premises at the beginning or the end of every academic semester (October, January, June), [b] an online learning environment that supports the learning process during the first three semesters. The fourth semester is reserved for the completion of the MSc thesis. Class attendance and participation in any educational activity, such as projects, assignments, etc., is compulsory.

The full-time study programme is completed in four academic semesters, including the writing of the thesis. The maximum permissible time for the completion of the full-time programme is eight semesters.

Working students may choose to study part-time. Working students have to prove that they work at least 25 hours per week and provide a work contract or certificate. Part-time study is also suitable for non-working students who cannot meet the demands of studying full-time, due to health or family issues, military service, or other personal reasons. Those students should send a written request to the Department’s Assembly before the start of the MSc programme. The duration of the part-time study programme must be no longer than eight academic semesters.

How to Apply

Applicants are required to fill in a candidacy application form, attaching the following documents:

  • A copy of their ID or passport
  • A CV
  • A copy of their degree/diploma or a certificate of studies completion
  • Peer-reviewed publications, if there are any
  • Certificates of professional or research activities, if there are any
  • Two references
  • A copy of the candidate’s undergraduate dissertation or its title and abstract if it has not been completed; in case the candidate was not required to write a dissertation during their undergraduate studies, they should mention so in writing
  • Proof of English language competency equivalent to or higher than B2 level (according to the Common European Framework of Reference for Languages)
  • Any other document that the candidate believes it might support their application

The application and all digital copies of documents must be submitted online on the “Nautilus” platform (https://nautilus.aegean.gr/). Hard copies can be submitted in person during admission.

Regulations of Studies

Download the updated Regulations of Studies for the postgraduate programme of studies (in Greek):

Κανονισμός Σπουδών – ΠΜΣ Διαδίκτυο των Πραγμάτων

Interviews

Download articles for the postgraduate programme’s interviews:

Articles – MSc Internet of Things

Theses

Please follow the link for the suggested master’s thesis subjects.

You can find information about the authoring of the master’s thesis here, as well as a template here.

Academic Calendar

14:00 – 17:00 & 18:00 – 21:00

Tuesday 15/6: Future Internet Security and Privacy
Tuesday 22/6: Embedded Systems and IoT
Wednesday 23/6: IoT Technologies and Applications
Thursday 24/6: IoT Communication Technologies

Remote lectures: 18:00 – 20:00

Monday: Embedded Systems and IoT (Kalligeros)
Tuesday: Future Internet Security and Privacy(Rizomyliotis)
Wednesday: IoT Communication Technologies (Vouyioukas)
Thursday: IoT Technologies and Applications (Kormentzas)

1 15-19/2/2021 No classes
2 22-26/2/2021 1st Lecture: Remote
3 1-5/3/2021 2nd Lecture: Remote
4 8-12/3/2021 3rd Lecture: Remote
5 15-19/3/2021 No classes
6 22-26/3/2021 4th Lecture: Remote
7 29/3 – 2/4/2021 5th Lecture: Remote
8 5-9/4/2021 6th Lecture: Remote
9 12-16/4/2021 7th Lecture: Remote
10 19-23/4/2021 8th Lecture: Remote
11 26-30/4/2021 No classes
12 3-7/5/2021 No classes
13 10-14/5/2021 9th Lecture: Remote
14 17-21/5/2021 10th Lecture: Remote
15 24-28/5/2021 11th Lecture: Remote
16 31/5 – 4/6/2021 No classes
(Week of substitutions)
17 7-11/6/2021 No classes
Spring Semester Exams
Public holiday 15/3/2021
Public holiday 25/3/2021
Easter Holidays 26/4 – 7/5/2021
Public holiday 1/5/2021