To have completed the course on Protocols and Communication Systems, or to have completed the courses on High Frequency Technologies or Antennas in their corresponding bachelor degree.

Basic skills CB6 To acquire and understand basic knowledge that provides a basis and opportunity to be original in the development and/or application of ideas, often in a research context. CB7 To allow the students to apply the acquired knowledge for the resolution of problems in new or not well-known environments, inside wide or multidisciplinary contexts related to their area of study. CB8 To allow the students to integrate the acquired knowledge, and to deal with the complexity of making judgments from incomplete or limited information, that must include issues of social and ethical responsibility associated with the application of said knowledge and judgments. CB9 To qualify the students to communicate their conclusions and the knowledge and reasons that support them, in a clear and non-ambiguous way, to specialized and non-specialized audiences. General skills CG1 Capacity to identify, define and formulate problems related to IoT applications. This capacity should include the simultaneous assessment of all the problem factors, technical and environmental, and civil liability. CG3 Proactive capacity to approach and solve the problems formulated in new and unknown environments, in the context of IoT. CG4 Capacity for teamwork, integrating multidisciplinary points of view. Specific skills CE3 Capacity to identify the security risks of communications in IoT environments, and to identify the communication protocols suited to mitigate the identified risks. CE4 Capacity to design and implement communication networks for IoT environments. CE11 Capacity to design and control the last generation wireless networks in IoT applications. CE12 Capacity to implement the communication of devices, both between them and in a global way, in IoT environments. Learning outcomes The results of the learning process should be the following: - Knowledge of the different communication architectures for IoT, and how are they integrated into more general mobile communication architectures.- ¿ - Capacity to develop radiofrequency equipment and subsystems for IoT. - Capacity to design electromagnetic sensors and antennas for the radiofrequency systems used for IoT. - Capacity to integrate sensors and antennas in transmitters and receivers for IoT. - Capacity to analyze, design, and plan complete mobile communication systems, considering the specifications and fundamental quality factors.

The subject is structured into two parts: a practical part, which is the most important from the point of view of evaluation, and a theoretical part, where basic concepts and hot topics in IoT are taught. Theoretical part: 1. Regulation and frequency standards 2. Overview of basic concepts of radiation 3. Small antennas 4. Frequency ranges 5. Near Field Communications (NFC) 6. Electromagnetic sensors 7. Harvesting 8. Measurement of antennas Practical part: MATLAB design of a radiolink with a practical case of use

Formative activities included in the curriculum AF1 Theoretical class AF4 Laboratory practices AF5 Office hours AF6 Teamwork AF7 Individual work of the student AF8 Midterm and final exams Activity code Total hours Classroom hours % classroom hours AF1 18 18 100 AF4 3 3 100 AF6 3 1 33 AF7 48 0 0 AF8 3 3 100 TOTAL 75 25 33% Methodology MD1 Master class supported by computing and audiovisual media, where the main topics are exposed, and bibliography is provided to complement the learning by the students. MD2 Critical reading of recommended texts: press articles, reports, textbooks and/or academic papers, both to be later discussed in class, or to complement and consolidate the knowledge of the topic. MD3 Resolution of practical cases, problems, etc., proposed by the teacher, individually or in groups. MD4 Exposition and discussion in class of topics related to the course and practical cases, under teacher moderation. MD5 Elaboration of assignments and reports individually or in groups. The use of Artificial Intelligence tools is selectively allowed in this subject. The faculty may indicate a list of works and exercises that the student can perform using AI tools, specifying how they should be used, and how the student should describe the use made of them. If the use of AI by the student gives rise to academic fraud by falsifying the results of an exam or work required to accredit academic performance, the provisions of the Regulation of the University Carlos III of Madrid of partial development of the Law 3/2022, of February 24th, of University Coexistence will be applied.