Internet of Things

Machine Learning

Inductive learning: Supervised and unsupervised learning, reinforcement learning. Decision Trees and pruning methods. Artificial neural networks: shallow networks, backpropagation algorithm, regularization methods, deep learning architectures. Support vector machines: linearly and non-linearly separable problems, kernel methods. Bayesian methods. Instance-based learning. Ensemble learning methods: bagging, boosting, random forests. Semi-supervised learning methods. Reinforcement learning: Q-learning, temporal difference learning, deep reinforcement learning. Experimental evaluation of classification methods. Application examples.

Design, Development, and Performance Evaluation of Next-Generation Networks

Presentation of the most advanced network technologies and methodologies (NAT, IP multicast, WEP, IEEE 802.1X, 802.21, etc.), architectures (MPLS, Diffserv, IntServ, etc.), protocols (RSVP, Mobile IP, IPv6, OSPF, BGP, etc.) and services (WebTV, IPTV, p2p, v2v, CDN).

Active services issues with capabilities such as self-organization, environmental intelligence and adaptation to underlying network infrastructures, spatial location awareness and multimodal interface extraction for costing, protection, mobility and service quality assurance algorithms.

Methods and tools for the performance assessment of modern heterogeneous systems.

Internet of Things Architectures and Services. Integration of IoT into existing network infrastructure and ROI.

Cloud Computing basics as a data service support technology with reference to deployment options in different network environments (eg. IoT/Edge/Cloud).

Pervasive Computing Systems

• Introduction to Pervasive Computing and Pervasive Computing Systems (PCS).
• Context aware systems.
• Challenges of PCS.
• Design issues of PCS.
• Architectures, programming models and frameworks.
• Privacy Issues in Pervasive Computing.
• Location tracking in Pervasive Computing.
• Processing sequential sensor data.
• Applications from smart objects collaboration.
• User interfaces in Pervasive Computing.
• End-user development of Pervasive Computing applications.

Algorithms, Combinatorial Optimization and Financial Applications

Combinatorial Optimization (CO) studies algorithms that compute the optimum solution amongst the feasible solutions of a combinatorial problem. A milestone of the theory was the understanding of the linear/convex problems. The combinatorial problems capture the intrinsic complexity of the most important problems for the computers. Due to this, in the last 50 years CO has played central role to explore the power and limitations of the computers. But, during this decade, due to the power of computers and the explosion of the Internet, modern problems have arisen. These 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 atomic 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 the selfish behavior of the users can lead to a suboptimal state the Internet/System. This manifests Mechanism Design as a key area to study.

IoT Technologies and Applications

• Design and Architecture of IoT systems,
• Abstract Information Models and Middleware Technologies,
• Large-Scale Distributed Experimental Infrastructures for IoT Systems,
• Management of Spectrum (TVWS), Energy and Transmissions (Handovers) in IoT settings,
• IoT Business Accelerators with an emphasis on agriculture,
• Comparison of IT Applications with intelligence and energy consumption metrics.
• Examples of commercial applications like FarmBeats.
• Telecommunications Specifications and new broadband network technologies to support IoT large-scale networks,
• Neuromorphic and Distributed computing systems in IoT systems,
• Security and device authentication issues in large-scale IoT networks.

IoT Communication Technologies

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 (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.

Embedded Systems and IoT

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 intended for edge nodes of an Internet of Things (IoT) type of network. More specifically, the course 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.

Future Internet Security and Privacy

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.

Robotics and Computer Vision

• Introduction to Robotics
• Introduction to Robotic Vision and its Applications
• Basic Principles of Image Processing
• Object Detection Algorithms
• Stereoscopic Vision Techniques
• Navigation & Localization (SLAM)
• Applications of Deep Learning in Robotic Vision
• Programming and Application of Computer Vision in Robotic Systems
• Integration of Vision Sensors and Data Processing
• Dynamic Detection and Tracking of Moving Objects
• Video Processing and Analysis in Robotics

Modern Networks and IoT Interfacing

• Fundamental principles underlying the design and management of wireless networks.
• Management of network resources in wide-coverage wireless networks (UMTS, LTE, and LTE-A).
• Fundamental principles and techniques for managing incoming network traffic, reducing transmission delay and ensuring Quality of Service (QoS) through effective data flow packet scheduling.
• Qualitative differentiation of Human-to-Human (H2H) and Machine-to-Machine (M2M) data flows.
• The distinctive features of IoT devices and how they impact the development of IoT network standards (NB-IoT, LTE-M).
• Evolving techniques for network resource management using virtualization technologies (Software-Defined Networking – SDN, Network Function Virtualization– NFV and Network Slicing).
• Enhancement of network accessibility through the densification of access points and the use of heterogeneous network technologies
• Research trends in the field of IoT interconnection networks.

Semantic Web

• Introduction to Semantic Web, Understanding the Semantic Web: Concepts and Principles, Importance of Semantic Web in Data Integration and Knowledge Representation
• XML and XSD, XML fundamentals and syntax, XML Schema Definition (XSD) for data validation and structuring, Practical exercises on creating XML documents and corresponding XSD schemas
• RDFS (Resource Description Framework Schema) , Introduction to RDFS and its role in Semantic Web, Creating Ontologies with RDFS: Classes, Properties, and Relationships, Hands-on exercises for building basic RDFS ontologies
• SPARQL (SPARQL Protocol and RDF Query Language) , Understanding RDF and querying RDF data, SPARQL language syntax and querying RDF datasets, Practical applications and exercises using SPARQL for data retrieval
• Logic and Inference in Semantic Web, Overview of formal logic and reasoning in Semantic Web, Inference engines and reasoning with ontological knowledge, Practical sessions on applying logic for inferencing in Semantic Web contexts
• Neosemantics with Neo4j, Introduction to graph databases and Neo4j, Neo4j's role in Semantic Web with Neosemantics, Building graph-based semantic models and querying with Cypher
• Project and Case Studies, Applying the learned concepts in real-world applications, Presentations and discussions on case studies using Semantic Web technologies

Big Data and Data Mining

• Introduction to Big Data
• Big Data and Machine Learning
• Generalization, processing of big data, technologies for acquisition and management of large volumes of data
• Principal Component Analysis
• Hadoop, HDFS, Mapreduce, IBL, SVM
• Big data and recognition of social behavior
• Sentiment Classification, Affective Computing, IoT, and Big Data
• Model Evaluation Metrics
• Matrix Factorization Models (Python, R)
• Tensor Factorization Models
• Neural Network Models

Master's Thesis

The MSc Thesis can be:

1. Research/Theoretical: This type focuses on developing a new theoretical model or extending an existing one, and applying it to problem-solving.
2. Research/Development: This type concentrates on the development of a 'new' system, based on existing theory. The development usually forms the dominant part of the work, enabling the demonstration of its application.
3. Application: This type focuses on developing a large application useful in a specific area of interest, utilizing one or more software/hardware packages.

The thesis text should be at least 20,000 words and indicatively include the following sections:

• Introduction to the Topic
• Literature Review
• Theoretical Development (including Theoretical Background and Theoretical Proposal)
• Methodology/Research
• Results, Conclusions, Future Research, Bibliography
• Other Deliverables, Applications (if applicable)

Pervasive Computing Systems

• Introduction to Pervasive Computing and Pervasive Computing Systems (PCS).
• Context aware systems.
• Challenges of PCS.
• Design issues of PCS.
• Architectures, programming models and frameworks.
• Privacy Issues in Pervasive Computing.
• Location tracking in Pervasive Computing.
• Processing sequential sensor data.
• Applications from smart objects collaboration.
• User interfaces in Pervasive Computing.
• End-user development of Pervasive Computing applications.

Design, Development, and Performance Evaluation of Next-Generation Networks

Presentation of the most advanced network technologies and methodologies (NAT, IP multicast, WEP, IEEE 802.1X, 802.21, etc.), architectures (MPLS, Diffserv, IntServ, etc.), protocols (RSVP, Mobile IP, IPv6, OSPF, BGP, etc.) and services (WebTV, IPTV, p2p, v2v, CDN).

Active services issues with capabilities such as self-organization, environmental intelligence and adaptation to underlying network infrastructures, spatial location awareness and multimodal interface extraction for costing, protection, mobility and service quality assurance algorithms.

Methods and tools for the performance assessment of modern heterogeneous systems.

Internet of Things Architectures and Services. Integration of IoT into existing network infrastructure and ROI.

Cloud Computing basics as a data service support technology with reference to deployment options in different network environments (eg. IoT/Edge/Cloud).

IoT Communication Technologies

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 (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.

Embedded Systems and IoT

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 intended for edge nodes of an Internet of Things (IoT) type of network. More specifically, the course 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.

Machine Learning

Inductive learning: Supervised and unsupervised learning, reinforcement learning. Decision Trees and pruning methods. Artificial neural networks: shallow networks, backpropagation algorithm, regularization methods, deep learning architectures. Support vector machines: linearly and non-linearly separable problems, kernel methods. Bayesian methods. Instance-based learning. Ensemble learning methods: bagging, boosting, random forests. Semi-supervised learning methods. Reinforcement learning: Q-learning, temporal difference learning, deep reinforcement learning. Experimental evaluation of classification methods. Application examples.

Algorithms, Combinatorial Optimization and Financial Applications

Combinatorial Optimization (CO) studies algorithms that compute the optimum solution amongst the feasible solutions of a combinatorial problem. A milestone of the theory was the understanding of the linear/convex problems. The combinatorial problems capture the intrinsic complexity of the most important problems for the computers. Due to this, in the last 50 years CO has played central role to explore the power and limitations of the computers. But, during this decade, due to the power of computers and the explosion of the Internet, modern problems have arisen. These 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 atomic 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 the selfish behavior of the users can lead to a suboptimal state the Internet/System. This manifests Mechanism Design as a key area to study.

IoT Technologies and Applications

• Design and Architecture of IoT systems,
• Abstract Information Models and Middleware Technologies,
• Large-Scale Distributed Experimental Infrastructures for IoT Systems,
• Management of Spectrum (TVWS), Energy and Transmissions (Handovers) in IoT settings,
• IoT Business Accelerators with an emphasis on agriculture,
• Comparison of IT Applications with intelligence and energy consumption metrics.
• Examples of commercial applications like FarmBeats.
• Telecommunications Specifications and new broadband network technologies to support IoT large-scale networks,
• Neuromorphic and Distributed computing systems in IoT systems,
• Security and device authentication issues in large-scale IoT networks.

Future Internet Security and Privacy

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.

Robotics and Computer Vision

• Introduction to Robotics
• Introduction to Robotic Vision and its Applications
• Basic Principles of Image Processing
• Object Detection Algorithms
• Stereoscopic Vision Techniques
• Navigation & Localization (SLAM)
• Applications of Deep Learning in Robotic Vision
• Programming and Application of Computer Vision in Robotic Systems
• Integration of Vision Sensors and Data Processing
• Dynamic Detection and Tracking of Moving Objects
• Video Processing and Analysis in Robotics

Modern Networks and IoT Interfacing

• Fundamental principles underlying the design and management of wireless networks.
• Management of network resources in wide-coverage wireless networks (UMTS, LTE, and LTE-A).
• Fundamental principles and techniques for managing incoming network traffic, reducing transmission delay and ensuring Quality of Service (QoS) through effective data flow packet scheduling.
• Qualitative differentiation of Human-to-Human (H2H) and Machine-to-Machine (M2M) data flows.
• The distinctive features of IoT devices and how they impact the development of IoT network standards (NB-IoT, LTE-M).
• Evolving techniques for network resource management using virtualization technologies (Software-Defined Networking – SDN, Network Function Virtualization– NFV and Network Slicing).
• Enhancement of network accessibility through the densification of access points and the use of heterogeneous network technologies
• Research trends in the field of IoT interconnection networks.

Semantic Web

• Introduction to Semantic Web, Understanding the Semantic Web: Concepts and Principles, Importance of Semantic Web in Data Integration and Knowledge Representation
• XML and XSD, XML fundamentals and syntax, XML Schema Definition (XSD) for data validation and structuring, Practical exercises on creating XML documents and corresponding XSD schemas
• RDFS (Resource Description Framework Schema) , Introduction to RDFS and its role in Semantic Web, Creating Ontologies with RDFS: Classes, Properties, and Relationships, Hands-on exercises for building basic RDFS ontologies
• SPARQL (SPARQL Protocol and RDF Query Language) , Understanding RDF and querying RDF data, SPARQL language syntax and querying RDF datasets, Practical applications and exercises using SPARQL for data retrieval
• Logic and Inference in Semantic Web, Overview of formal logic and reasoning in Semantic Web, Inference engines and reasoning with ontological knowledge, Practical sessions on applying logic for inferencing in Semantic Web contexts
• Neosemantics with Neo4j, Introduction to graph databases and Neo4j, Neo4j's role in Semantic Web with Neosemantics, Building graph-based semantic models and querying with Cypher
• Project and Case Studies, Applying the learned concepts in real-world applications, Presentations and discussions on case studies using Semantic Web technologies

Big Data and Data Mining

• Introduction to Big Data
• Big Data and Machine Learning
• Generalization, processing of big data, technologies for acquisition and management of large volumes of data
• Principal Component Analysis
• Hadoop, HDFS, Mapreduce, IBL, SVM
• Big data and recognition of social behavior
• Sentiment Classification, Affective Computing, IoT, and Big Data
• Model Evaluation Metrics
• Matrix Factorization Models (Python, R)
• Tensor Factorization Models
• Neural Network Models

Master's Thesis

The MSc Thesis can be:

1. Research/Theoretical: This type focuses on developing a new theoretical model or extending an existing one, and applying it to problem-solving.
2. Research/Development: This type concentrates on the development of a 'new' system, based on existing theory. The development usually forms the dominant part of the work, enabling the demonstration of its application.
3. Application: This type focuses on developing a large application useful in a specific area of interest, utilizing one or more software/hardware packages.

The thesis text should be at least 20,000 words and indicatively include the following sections:

• Introduction to the Topic
• Literature Review
• Theoretical Development (including Theoretical Background and Theoretical Proposal)
• Methodology/Research
• Results, Conclusions, Future Research, Bibliography
• Other Deliverables, Applications (if applicable)

Robotics and Computer Vision

• Introduction to Robotics
• Introduction to Robotic Vision and its Applications
• Basic Principles of Image Processing
• Object Detection Algorithms
• Stereoscopic Vision Techniques
• Navigation & Localization (SLAM)
• Applications of Deep Learning in Robotic Vision
• Programming and Application of Computer Vision in Robotic Systems
• Integration of Vision Sensors and Data Processing
• Dynamic Detection and Tracking of Moving Objects
• Video Processing and Analysis in Robotics

Modern Networks and IoT Interfacing

• Fundamental principles underlying the design and management of wireless networks.
• Management of network resources in wide-coverage wireless networks (UMTS, LTE, and LTE-A).
• Fundamental principles and techniques for managing incoming network traffic, reducing transmission delay and ensuring Quality of Service (QoS) through effective data flow packet scheduling.
• Qualitative differentiation of Human-to-Human (H2H) and Machine-to-Machine (M2M) data flows.
• The distinctive features of IoT devices and how they impact the development of IoT network standards (NB-IoT, LTE-M).
• Evolving techniques for network resource management using virtualization technologies (Software-Defined Networking – SDN, Network Function Virtualization– NFV and Network Slicing).
• Enhancement of network accessibility through the densification of access points and the use of heterogeneous network technologies
• Research trends in the field of IoT interconnection networks.

Semantic Web

• Introduction to Semantic Web, Understanding the Semantic Web: Concepts and Principles, Importance of Semantic Web in Data Integration and Knowledge Representation
• XML and XSD, XML fundamentals and syntax, XML Schema Definition (XSD) for data validation and structuring, Practical exercises on creating XML documents and corresponding XSD schemas
• RDFS (Resource Description Framework Schema) , Introduction to RDFS and its role in Semantic Web, Creating Ontologies with RDFS: Classes, Properties, and Relationships, Hands-on exercises for building basic RDFS ontologies
• SPARQL (SPARQL Protocol and RDF Query Language) , Understanding RDF and querying RDF data, SPARQL language syntax and querying RDF datasets, Practical applications and exercises using SPARQL for data retrieval
• Logic and Inference in Semantic Web, Overview of formal logic and reasoning in Semantic Web, Inference engines and reasoning with ontological knowledge, Practical sessions on applying logic for inferencing in Semantic Web contexts
• Neosemantics with Neo4j, Introduction to graph databases and Neo4j, Neo4j's role in Semantic Web with Neosemantics, Building graph-based semantic models and querying with Cypher
• Project and Case Studies, Applying the learned concepts in real-world applications, Presentations and discussions on case studies using Semantic Web technologies

Big Data and Data Mining

• Introduction to Big Data
• Big Data and Machine Learning
• Generalization, processing of big data, technologies for acquisition and management of large volumes of data
• Principal Component Analysis
• Hadoop, HDFS, Mapreduce, IBL, SVM
• Big data and recognition of social behavior
• Sentiment Classification, Affective Computing, IoT, and Big Data
• Model Evaluation Metrics
• Matrix Factorization Models (Python, R)
• Tensor Factorization Models
• Neural Network Models

Master's Thesis

The MSc Thesis can be:

1. Research/Theoretical: This type focuses on developing a new theoretical model or extending an existing one, and applying it to problem-solving.
2. Research/Development: This type concentrates on the development of a 'new' system, based on existing theory. The development usually forms the dominant part of the work, enabling the demonstration of its application.
3. Application: This type focuses on developing a large application useful in a specific area of interest, utilizing one or more software/hardware packages.

The thesis text should be at least 20,000 words and indicatively include the following sections:

• Introduction to the Topic
• Literature Review
• Theoretical Development (including Theoretical Background and Theoretical Proposal)
• Methodology/Research
• Results, Conclusions, Future Research, Bibliography
• Other Deliverables, Applications (if applicable)