Master UCM Permanent formation :
IOT Marketing Management


The Formation degree in IOT  (IOT=Internet of Things) Marketing Management is aimed at graduate students and professionals who want to broaden and renew their knowledge of the new challenges posed by the growing complexity of markets and their impact on the global economy. This program, in addition, seeks to teach the latest tools to meet the needs of potential customers, without neglecting the profitability and sustainability of the company.

Course 2021/2022. 

  • Continuing Education: Degrees that require a university degree – Own Master’s Degree.
  • Centro responsible: Faculty of Commerce and Tourism.
  • Code: 2021-24100-055
  • Director: Ms. Maria Francisca BLASCO LOPEZ
  • Co-direction: Mr. Juan Carlos IZQUIERDO VILLAVERDE

Study plan Internet of Things


  1. Node architecture IOT
  2. Development boards and processors for data acquisition nodes
  3. Cross-development environment and hardware debugging/verification
  4. Commonly used sensors – characteristics of a sensor
  5. Sensor-processor interfaces: ADC/DAC, I2C buses, SPI.
  6. Design of acquisition systems: design specifications.
Networks, protocols and interfaces I
  1. General concepts of the Internet and IoT in particular: layers, protocols, packets, services, quality parameters in packet networks, applications, P2P communications, sensor networks, multimedia.
  2. Link level protocols
  3. Network protocols: IPv6. Routing protocols for IoT.
  4. Transport services: TCP, UDP, socket programming.
  5. Mobile networks: roaming and handoffs, mobile IP, ad hoc networks.
  6. Communication assessment and management tools.
Networks, protocols and interfaces II
  1. WBAN and LowPAN networks.
  2. LoWAN networks
  3. Layering in data communication. Protocols.
  4. Link level limitations in data transfer. The move towards application level protocols.
  5. Major application level protocols
  6. Tools for data flow management: NODE-RED.
Specific competences
  1. Ability to use HW devices for the Internet of Things.
  2. Ability to choose and evaluate communication and computing infrastructure for Internet of Things systems.
  3. Ability to programme non-conventional sensors and actuators.
  4. Ability to use the different network protocols used in the Internet of Things.
  1. Bulk data processing
  2. Introduction to Big Data
  3. NoSQL databases
  4. Big Data architectures: cost and requirements
  5. Big Data in the cloud
  6. Scientific data analysis
  7. Machine Learning


Intelligent infrastructure design for the Internet of Things
  1. Specification and design of backend and front-end for intelligent IoT systems. o Web applications and services o Multi-platform developments
  2. Access to open and/or heterogeneous data sources to support intelligent processes.
  3. Design and implementation of intelligent distributed systems. o Mechanisms for integrating intelligence using distributed technologies.
  4. Platforms for the creation of enterprise software (CORBA, RMI, .NET, J2EE) and emerging approaches (Blockchain).
  5. Integration with corporate software (ERP, CRM, BPM, CMS) with emphasis on integration at the data source level.


Artificial Intelligence applied to the Internet of Things
  1. Computational perception on data from heterogeneous sources: machine vision, natural language and other sensory capabilities.
  2. intelligent interfaces
  3. Machine Learning: Deep Learning.
  4. Knowledge modelling and representation.
  5. Reasoning and decision making techniques.


Specific competences
  1. Knowing the application of the main techniques for the design of intelligent systems in the context of the Internet of Things.
  2. Knowledge of concepts and application domains of the Internet of Things: robotics, home automation, smart cities, intelligent transport, monitoring (medical, environmental, people), etc.
  3. Ability to develop the architecture and components aimed at creating intelligent distributed systems.
  4. Ability to analyse, plan and evaluate the processes of acquisition, abstraction and preparation of open and heterogeneous data obtained from sensors, in particular images, numerical and textual signals.
  5. Ability to handle and classify massive heterogeneous data in NoSQL databases.
  6. Ability to develop and evaluate advanced analytical, data mining and machine learning techniques and predictive models on Big Data.
  7. Choose and apply complex techniques of abstraction and visualisation of massive data.
  8. Select and apply inference and reasoning techniques for intelligent systems in real time.
  1. Security and legality
  2. Basic concepts of security.
  3. Security in communications (encryption, signatures, digital certificates, PKI and mutual authentication).
  4. Systems security (hardware, users, secure programming and application execution).
  5. Network Infrastructure and Services Security (attacks on network protocols at different levels, firewalls, IDS/IPS and VPN).
  6. European legislation related to the Internet of Things. Data protection. Patents and intellectual property.


Specific competences
  1. Understand the general aspects of security and privacy in the Internet of Things.
  2. Ability to configure distributed networks of devices in a secure manner.
  1. Introduction to Edge Computing. Motivation.
  2. Edge-AI. Infrastructures and frameworks for machine learning at the edge.
  3. Specific purpose architectures for Edge Computing.
  4. Deployment of services at the edge. Virtualisation and containers.
  5. Security in Edge Computing.


Specific competences
  1. Understand the main drivers of edge computing in real IoT environments.
  2. Ability to deploy applications at the edge in a secure and scalable way.
  3. Know the hardware-level alternatives for edge computing acceleration.
  4. Critically apply specific security measures for edge computing.


Are you interested?