Mobile and ubiquitous electronic system

The electronics course deals with Internet of Things technologies and the whole IoT concept, from connectivity to end-to-end embedded systems. In this context, it considers the AI ​​+ IoT + 5G model, concept and smart devices, intelligent systems in IoT, IoT objects and intelligence in terms of the degree of object intelligence, location of intelligence in IoT systems, aggregation of intelligence. Within IoT connectivity, we look at the global transport level of CIoT, IIoT, KPI, Low-Power IoT, cellular / non-cellular technologies for IoT, ZigBee, BLE, LP-Wifi, GPP, LTE, NB-IoT, 5G for IoT, Wifi, LP -Wifi, Wi-FAR, Wi-SUN, NFC, RFID, VLC, WUSB, PLC, characteristics and use of communication options in IoT. We take a closer look at the functionality of M2M communication, MTC, M2M concept, key features, architecture, M2M device, M2M application, M2M requirements, problems in M2M, M2M / IoT, LPWAN, Lora, LoraTM, LoRaWAN, SigFox, 6LoWPAN. At the level of M2M / IoT protocols, we look at IoT architecture globally, SOAP, REST, MQTT, COAP, OMA LWM2M. The following is an overview of software platforms for IoT architecture (global): software levels and their characteristics, embedded systems, gateway software, cloud IoT, Eclipse IoT, Azure IoT, Google Cloud, ThingWorx, Watson IoT and Bluemix, Amazon AWS , functionality comparison and technical aspects of development frameworks. In terms of security, we come across blockchain IoT technology: use of technology in IoT, blockchain architecture, technology features, technology functionality, technology strengths / weaknesses, major technology concepts, technology convergence with IoT, types of blockchain platforms, transaction mechanism, Watson IoT blockchain technology and IoT application development, blockchain technology and smart home. The second part of the course deals with IoT data analytics, introduction, fields of application of analytics in IoT, classification of analytics, characteristic of IoT data, big data (what are and what are their characteristics), IoT data, processing of IoT data and algorithms, realization of analytics in IoT environment, the role of the Hadoop platform, the integration of analytics into IT systems and the cloud. At the machine learning level, classification of Big Data learning methods, deep learning, neural network architectures, deep learning characteristics, DNN, CNN, R-CNN, RNN, DBN, LSTM, AE, VAE, GAN, RBM, DL models and overview categorization and characteristics, frameworks for DL ​​models. We conclude with the use of analytics, ML, DL in IoT systems: in automotive software, self-driving vehicles, ADAS systems, end-to-end AI systems, ITS intelligent transport systems, UAV systems.