Máster Universitario en Internet das Cousas - IoT

In a generic way, the access profile corresponds to that of a graduate in a degree related to the field of ICT. You are expected to have knowledge of programming, algorithm design, computer networks, basic communication protocols between devices, electronic circuits, as well as knowledge of statistics.

• Compulsory: 39
• Optional: 15
• Compulsory external internships: 3
• Master's final dissertation: 6
o Total: 60

Los tres centros donde se impartirá el Máster cuentan con una página Web institucional donde se muestra toda la información relativa a sus titulaciones, clasificadas en Grados y Másteres:
• UVigo: https://teleco.uvigo.es/
en la sección Estudios Másteres.
• UDC: https://www.fic.udc.es/
en la sección Estudios.
• USC: https://www.usc.gal/es/centro/escuela-tecnica-superior-ingenieria
en la sección Estudios.

Access

Poderán acceder ás ensinanzas oficiais de Master:
1. As persoas que estean en posesión dun título universitario oficial español.
2. Aquelas que teñan un título expedido por unha institución de educación superior do EEES que faculta no país expedidor do título para o acceso a ensinanzas de mestrado.
3. Os titulados conforme a sistemas educativos alleos ao EEES sen necesidade de homologación dos seus títulos, previa comprobación pola Universidade de que os ditos títulos acreditan un nivel de formación equivalente aos correspondentes títulos universitarios españois e que facultan no país expedidor do título para o acceso a ensinanzas de posgrao.

Admission

Students requesting admission to this master's degree must preferably have one of the university degrees, bachelor's degrees or technical engineering degrees in the following areas, which we will call Preferred Degrees (including those degrees with equivalent or alternative names to those included in the listing):
· Telecommunication engineering
· Industrial and Automatic Electronic Engineering
· Informatics Engineering
· Data Science and Engineering
· Artificial intelligence
· Robotics
In the case of students who apply for admission and hold a different degree, their admission will be assessed on the basis that they can justify that their previous studies have led to the acquisition of the knowledge corresponding to the generic profile of a graduate in a degree related to the field of ICT, who is expected to have knowledge of programming, algorithm design, computer networks, basic communication protocols between devices, electronic circuits, as well as knowledge of statistics.
For this purpose, students must include in their application, in addition to their curriculum vitae, the Bachelor's Degree they hold, as well as details of the syllabus in which the teaching load of each of the subjects is shown, and a description of those subjects that are relevant for the justification of having acquired the knowledge corresponding to the generic profile described in the previous paragraph.
Please also attach your work experience related to IoT if applicable.

The scale with which applications for admission will be evaluated will be based on the following aspects:
Academic record: Up to 70% of the grade. For students from degrees other than the Preferred Degrees, their academic record will be divided by 2.
Research experience: Up to 30% of the grade.
Work experience: Up to 30% of the grade.
Other merits related to the field of IoT: Up to 10% of the grade.
The specific percentages associated with the scale for each academic year will be established and published prior to the beginning of the pre-registration and enrolment periods.

CMP1 - Generic - Design IoT devices by selecting the most appropriate sensors/actuators for each use.
CMP2 - Generic - Develop the necessary architecture to guarantee the interoperability of devices.
CMP3 - Generic - Build networks and define protocols that allow communication between IoT devices.
CMP4 - Generic - Evaluate the operation of IoT embedded electronic systems.
CMP5 - Generic - Determine mechanisms for collecting data in real time.
CMP6 - Generic - Integrate technologies such as Machine Learning, massive data processing, Distributed Record Technologies (DLT), edge computing, among others, for the development of smarter and more efficient IoT systems.
CMP7 - Generic - Guarantee the security of information generated by IoT devices.
CMP8 - Generic - Develop a business plan for a business project based on IoT.
CMP9 - Generic - Design databases for the storage and management of large amounts of IoT data.
CMP10 -Generic- Gain experience in the design, implementation, deployment and maintenance of IoT systems in a real working environment.
CMP11- Generic- Develop sufficient autonomy to participate in research projects and scientific or technological collaborations within their subject area, in interdisciplinary contexts and, where appropriate, with a high knowledge transfer component.
CMP12 -Generic- Integrate knowledge and deal with the complexity of making judgements based on incomplete or limited information, including reflections on the social and ethical responsibilities linked to the application of knowledge and judgements.

I-CP1 - Industrial IoT - Design and deploy large-scale IIoT data processing systems.
I-CP2 - Industrial IoT - Design, deploy and optimize Green IoT systems.
I-CP3 - Industrial IoT - Analyse and interpret IIoT data flows in an industrial company.
I-CP4 - Industrial IoT - Design a robotic industrial twin.
I-CP5 - Industrial IoT - Design and implement video analysis and processing algorithms for IIoT environments.
I-CP6 - Industrial IoT - Gain experience in the design, implementation, deployment and maintenance of IIoT systems within a real work environment.
S-CP1 - Society 5.0 - Design and deploy networks of IoT devices in the field of Smart Cities and Buildings.
S-CP2 - Society 5.0 - Implement video analysis and processing algorithms for Society 5.0 applications.
S-CP3 - Society 5.0 - Design and use IoT systems for asset location in healthcare environments.
S-CP4 - Society 5.0 - Design and deploy large-scale IoT data processing systems for Society 5.0 applications.
S-CP5 - Society 5.0 - Gain experience in the design, implementation, deployment and maintenance of IoT systems applied to Society 5.0 environments within a real work environment.
V-CP1 - Connected Vehicle - Design and deploy device networks in the connected car field.
V-CP2 - Connected Vehicle - Implement video analysis and processing algorithms in the field of the connected vehicle.
V-CP3 - Connected Vehicle - Design and deploy large-scale IoT data processing systems for connected vehicle applications.
V-CP4 - Connected Vehicle - Design and deploy IoT systems for ITS.
V-CP5 - Connected Vehicle - Deploy and use IoT systems for UAVs.
V-CP6 - Connected Vehicle - Design and deploy services for the connected vehicle.
V-CP7 - Connected Vehicle - Gain experience in the design, implementation, deployment and maintenance of IoT systems applied to the connected vehicle within a real work environment.

A/S1 - Generic - Select the most appropriate IoT cloud platform for each scenario.
A/S2 - Generic - Select the most appropriate architecture and distributed or decentralized system for each IoT scenario.
A/S3 - Generic - Analyse the cybersecurity risks of an IoT system.
A/S4 - Generic - Develop low consumption IoT systems.
A/S5 - Generic - Develop embedded systems for IoT applications.
A/S6 - Generic - Manage the storage and distribution of spatial and temporal data.
A/S7 - Generic - Select network topologies and routing and application protocols appropriate for IoT scenarios.
A/S8 - Generic - Plan connectivity scenarios for IoT networks.
A/S9 - Generic - Establish financing sources for an innovative business plan based on developments in IoT technologies.
A/S10 - Generic - Manage spatial data and data series with time stamps.
A/S11 - Generic - Implement supervised/unsupervised machine learning algorithms with classical and deep neural networks.
A/S12- Generic- Apply acquired knowledge and solve problems in new or unfamiliar environments within broader, multidisciplinary contexts, being able to integrate knowledge and skills in the following areas
A/S13- Generic- Communicate (orally and in writing) conclusions - and the knowledge and rationale underpinning them - to specialist and non-specialist audiences in a clear and unambiguous way.
A/S14- Generic- Predict and control the evolution of complex situations by developing new and innovative work methodologies adapted to the specific scientific/research, technological or professional field, generally multidisciplinary, in which their activity is carried out.

I-A/S1 - Industrial IoT - Apply statistical techniques to large-scale IIoT data sets.
I-A/S2 - Industrial IoT - Programme Single-Board Computers (SBCs) for the deployment and management of IIoT sensor and actuator nodes.
I-A/S3 - Industrial IoT - Integrate telemetry data into commercial IIoT platforms.
I-A/S4 - Industrial IoT - Implement specific protocols for the industrial control of robotic systems.
I-A/S5 - Industrial IoT - Use techniques to clean and preprocess IIoT data for machine learning algorithms.
I-A/S6 - Industrial IoT - Apply techniques to track objects in IIoT environments through image analysis.

V-A/S1 - Connected Vehicle - Apply statistical techniques to large-scale data in connected vehicle IoT applications.
V-A/S2 - Connected Vehicle - Apply image analysis techniques in the field of the connected vehicle.

S-A/S1 - Society 5.0 - Programme and deploy IoT wearables for health.
S-A/S2 - Society 5.0 - Apply statistical techniques to large-scale IoT data sets and for Society 5.0 applications.
S-A/S3 - Society 5.0 - Apply video analysis techniques for Society 5.0 applications

KC2 - Generic - Recognise the characteristics of new IoT architectures (e.g., decentralised, distributed).
KC1 - Generic - Identify the different types of services and deployment models of cloud computing systems for IoT.
KC10 - Generic - Know and understand the basic notions of Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP).
KC11 - Generic - Know and understand the fundamental concepts about machine learning for IoT.
KC12 - Generic - Acquire advanced knowledge and demonstrate, in a scientific and technological or highly specialised research context, a detailed and grounded understanding of the theoretical and practical aspects of the methodology of work in one or more fields of study.
KC3 - Generic - Identify the basic concepts of cybersecurity for IoT.
KC4 - Generic - Determine the sensor and actuator devices necessary for IoT applications.
KC5 - Generic - Recognise the structure of embedded IoT systems.
KC6 - Generic - Recognise the operation of the different IoT network and application protocols.
KC7 - Generic - Identify the characteristics of the different types of networks and network technologies for IoT.
KC8 - Generic - Identify the different types of innovation and entrepreneurship, and their application to business projects based on IoT.
KC9 - Generic - Recognise the basic aspects of intellectual and industrial protection.
I-K3 - Industrial IoT - Remember the different existing architectures for the deployment, monitoring, and management of continuous robotic systems.
I-K1 - Industrial IoT - Know and understand the main Big Data architectures for IIoT and their data processing mechanisms, as well as the main statistical and storage/management techniques.
I-K2 - Industrial IoT - Know and understand the essential concepts about Green IoT and the main energy optimization strategies.
I-K4 - Industrial IoT - Know and understand the basic operation of video cameras and motion detectors in the IIoT field, as well as the applications of video analysis in said field.
I-K5 - Industrial IoT - Know and understand the basic concepts about IIoT system integration.
I-K6 - Industrial IoT - Know and understand the fundamentals of data preprocessing for industrial plants.
S-K1 - Society 5.0 - Know and understand the basic fundamentals of IoT communication, traceability and wearable technologies for self-quantified, participatory and intelligent health.
S-K2 - Society 5.0 -Know and understand the basic fundamentals of sensors and automation for smart cities.
S-K3 - Society 5.0 - Identify technological trends for the management and construction of smart cities.
S-K4 - Society 5.0 -Know and understand the basic concepts of home and building automation including sensorisation, architectures and services.
S-K5 - Society 5.0 - Know and understand the main energy models and the concept of smart electrical network (smart grid) from the point of view of smart buildings and homes.
S-K6 - Society 5.0 - Identify the main Big Data architectures for IoT for Society 5.0 applications and their data processing mechanisms, as well as the main statistical and storage/management techniques.
S-K7 - Society 5.0 -Know and understand the basic operation of video cameras and motion detectors in the field of applications for Society 5.0.
S-K8 - Society 5.0 - Know and understand the concepts and systems related to the deployment of networks in the field of applications for Society 5.0.
V-K1 - Connected Vehicle - Know and understand the main Big Data architectures for connected vehicle applications and their data processing mechanisms, as well as the main statistical and storage/management techniques.
V-K2 - Connected Vehicle - Know and understand the basic fundamentals of Intelligent Transportation Systems.
V-K3 - Connected Vehicle - Know and understand the essential concepts and enabling technologies in the field of UAVs for IoT.
V-K4 - Connected Vehicle - Know and understand the architecture of the connected and autonomous vehicle and its main elements.
V-K5 - Connected Vehicle - Know and understand the basic operation of video cameras and motion detectors in the connected vehicle field, as well as the applications of video analysis in said field.
V-K6 - Connected Vehicle - Know and understand the basic concepts related to the deployment of networks in the field of the connected vehicle.