Reti di Sensori Wireless per Internet of Things
Wireless Sensor Networks for the Internet of Things Paola Pierleoni
This course requires the knowledge of the basic concepts of signal and telecommunications theory.
KNOWLEDGE AND UNDERSTANDING:
To know and understand the issues related to the design of wireless sensor networks related to their pervasive influence, the characteristics of the transmission medium, the variety of network architectures and possible applications. Increase knowledge of standard protocols and emerging ones in the scientific literature regarding the Internet of Things, by analyzing the performance achievable under changing choices in each level of the protocol architecture. Study the so-called smart objects and how networks of smart objects can be interconnected using the IP protocol to be able to understand and define innovative technics in the design of IoTs systems.CAPACITY TO APPLY KNOWLEDGE AND UNDERSTANDING:
To be able to make informed choices on the basis of quality of service and traffic characteristics for the specific application, and to use this knowledge for the development and implementation of original solutions in various application fields and, if possible, in research ones. In general, to evaluate, analyze and solve problems in new and emerging areas such as the Internet of Things using the newest technologies.TRANSVERSAL SKILLS:
A smart object is essentially a device equipped with sensing / actuator units, a microprocessor, a communication device and a power source. The scope of the study is therefore highly interdisciplinary, involving sectors such as micro- and nano-electronics, embedded systems, wireless sensor networks, ubiquitous computing, mobile computing, computer networking, mobile telephony, telemetry, etc. The description of projects already carried out and the implementation of new ones through laboratory experiences will provide cross skills in different ICT disciplines giving the students the ability to use, develop and manage disparate technologies and skills within broader contexts related to their field of study. It will start from the analysis of the problem to get through the design, implementation, optimization and verification of the final system performance.
(Lectures, 48 hours)
General: Internet of Things (IoTs).
Wireless Sensor Network (WSNs) and Wireless Body Sensor Networks (WBSNs). Sensor Network protocol stack: Physical Layer, Data Link Layer, Network Layer, Transport and Application Layers. Cross Layer optimization. Energy Management. Relevant Standards for each level of the protocol stack. Design constrains and WBSNs applications.
Internet of Things over IP protocol Architecture. Fundamental TCP/IP architectural design principles. Recalls of IPv4. QoS: delay, jitter, packet loss. Transport protocols: TCP, UDP. IPv6 and TCP/IP protocol stack. Fragmentation. High level protocols.
Smart Objects hardware and software. Operanting Systems for Smart Objects. IPv6 for Smart Objects and the Internet of Things.
uIPv6. The 6LowPAN Adaptation Layer. RPL: routing in Smart Objects networks. CoAP. Standardization. Interoperability. Non-IP Smart Objects tecnologies.
Web services for Smart Objects. Connectivity models for Smart Objects networks. Security for Smart Objects. Theory about inertial sensors. Accelerometer, gyroscope, magnetometer and barometer. Orientation filters: acquisition, calibration, data fusion. Acquisition, processing and transmission of biometric signals: ECG, EMG, breathing etc.
Indoor localization techniques. The application of Smart Objects networks for indoor localization.
Monitoring of environmental parameters for Smart Cities, Smart Lighting, Smart Home, precision agriculture, logistics applications, etc.
Laboratory activities (24 hours).
BLE. Android operating system and applications. Arduino: tutorial on Arduino board and its programming in a series of practical realizations (applications of smart lighting and power metering systems, proximity detection, IMU , wearable biomedical devices , etc.). Wi-IMU: GUI, cuboid. orientation filters and complementary filter. Classification techniques. Fall Detection algorithms. MatLab simulations on filters and algorithms. Application of inertial systems for the monitoring of neurological diseases and clinical tests. Postural detection. Activity level and tracking. Design of Web server services for sensor data. Contiki. Development and application of a WSN system for indoor localization. Examples of web-based management systems for sensor networks. WebRTC and its capabilities for real-time transmission of data acquired from the biometric sensors. Proposition of facultative experimental projects to the students.
Development of the examination
LEARNING EVALUATION METHODS
The students learning assessment is done through a verbal examination that covers specific topics of the course. This assessment can optionally also include the presentation and discussion of a project chosen among those proposed by the teacher. The project is typically a practical implementation of one of the topics covered in the course. It will be presented in the form of technical report and / or hw / sw prototype version, typically a Wireless Sensor Network subsystem.
The project can be done in groups. The size of each group shall be agreed with the teacher on the basis of the complexity of the chosen project. The discussion of the project and the verbal examination must take place with the participation of all students belonging to the same group.
LEARNING EVALUATION CRITERIA
The student must demonstrate the understanding of the fundamental concepts of network architectures and protocol stacks discussed during the course to successfully pass the assessment of learning. In addition, the student must identify the problems and the design criteria with reference to the different application fields, the specific quality of service and traffic parameters. The student must know how to apply, in independent way, those criteria and procedures to the design of simple network architectures, offering the most suitable protocols for each layer of the stack, taking into account the issues involved.
The student, during the verbal examination, may present and discuss an optional project, showing knowledge, methodological skills and technological constraints of the proposed solution.
LEARNING MEASUREMENT CRITERIA
The verbal examination is evaluated by a score of thirty.
FINAL MARK ALLOCATION CRITERIA
During the verbal examination the student must obtain a score of at least eighteen points in order to have a positive evaluation. The student must demonstrate an overall knowledge of the topics and present them in a correct manner and with the use of proper technical terminology to successfully pass the verbal examination. In case of submission of a project, it must fullfil the minimal functional requirements agreed with the teacher.The student must demonstrate a thorough understanding of topics presented with a mastery of technical language to get the maximum score. Praise is given to students who perform correctly the verbal examination and show a particular brilliance and mastery of the topics.
Jean-Philippe Vasseur, Adam Dunkels, Interconnecting Smart Objects with IP: The Next Internet, Elsevier.
Ilya Grigorik, High Performance Browser Networking, O'Reilly.
Rob Manson, Getting Started with WebRTC - Explore WebRTC for real-time peer-to-peer
communication, PACKT Publishing.
- Ingegneria Elettronica (Corso di Laurea Magistrale (DM 270/04))