1. Introduction: IoT technology and business strategy:
Students are expected to have factual knowledge, skills and competences of: IoT terms and basic concepts; technological trends which have led to IoT; embedded systems in terms of interface; impact of IoT on organizations/society; main application sectors; basics of hardware components (microcontrollers and microprocessors, sensors, actuators); basics of communication technologies and platforms; commonalities and differences between IoT and other technologies (e.g. Cloud computing, Big Data, Industry 4.0); IoT national and international policies. Additionally, they are expected to have factual knowledge of: features of product and services interconnected; IoT benefits and challenges; new business opportunities and competitive risks; IoT business solutions main features; IoT Solutions from user, business, operational, revenue and cost perspectives; introduction of IoT into business: i. strategy and alignment ii. Organization iii. Budgeting iv. Product development v. manufacturing vi. Distribution vii. Customer satisfaction viii. IoT solution; creation of successful IoT business for SMEs (how to implement an IoT business).
2. Device architecture and sensors for microprocessors
Student is expected to demonstrate specialized knowledge, skills and competences on: basic concepts of device architecture; basics of sensors and actuators issues; analog sensors: voltage vs current; digital sensors: on/off, parallel, serial, asynchronous vs synchronous; Pulse Width Modulation; buses (Binary Unit Systems): I2C, SPI; connection technology.
3. Networking and Security
Students are expected to have factual knowledge, skills and competences of: networking protocols for IoT environments; communication protocols for IoT environments; IoT security basics; Hardware and Software vulnerabilities; Security risks regarding the implementation of networking and communication protocols.
4. IoT data analysis:
Students are expected to demonstrate specialized knowledge, skills and competences on: cloud storage and cloud analytics basics; data management tools: big data for IoT, Big Data Analytics techniques, basics of Hadoop Data Management, basics of “R” for statistical purposes; introduction to machine learning; machine learning classification techniques; Bayesian prediction; Image and video analytic for IoT; Options for the implementation of machine learning for IoT; Biometric ID integration with IoT; Real time analytic/stream analytic; Scalability issues for IoT and machine learning; Visualization analytic; Structured and unstructured predictive analytics; Recommendation engines; Pattern direction; Frameworks for distributed data analysis.
5. IoT platforms:
At the end of this unit the student is expected to demonstrate comprehensive knowledge, skills and competences on: connecting Iot devices to local or global network; Low and High level Protocols dedicated to IoT devices; IoT platforms: ThinkSpeak, ThinkWorx, Ubidots, etc.