Belépés címtáras azonosítással
magyar nyelvű adatlap
angol nyelvű adatlap
Smart City Services and Applications
A tantárgy neve magyarul / Name of the subject in Hungarian: Okosváros szolgáltatások és alkalmazások
Last updated: 2024. március 3.
Dr. Rolland Vida, Associate Professor, TMIT
Dr. Attila Vidács, Associate Professor, TMIT
Dr. Gabr Fehér, Associate Professor, TMIT
A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.
A kötelező előtanulmányi rend az adott szak honlapján és képzési programjában található.
The aim of the course is to show how adaptive services and applications tailored to the current environmental conditions, user needs and infocommunication resources can make the operation of cities smarter, more efficient and sustainable. Within the scope of the course, on the one hand, we present services that are based on the sensing infrastructure of smart cities, installed sensors and sensor networks. On the other hand, we present different intelligent transport systems and services. We touch on the topics of smart buildings, smart homes, and the smart electrical grid. We present the possibilities and benefits of community participation, as well as issues of security and data protection. Finally, we review various international examples, smart city systems that have already been implemented or are being planned.
Detailed topics of the lectures:,
1. Smart city goals, strategies, master plans. Island-like systems v. smart city services and applications that build on each other and are in synergy with each other.
2. Operational models of urban sensor networks. Event, time and query based control. Data aggregation within a network. Mobility in sensor networks, base station vs. sensor mobility, virtual mobility,
3. Localization and tracking in urban sensor networks, location-aware operation. Modeling of sensor networks, simulation tools (tossim). Test networks (IoT-LAB). Standardization issues.,
4. Intelligent transport systems in smart cities. Efficient public transport. Ride sharing solutions, incentive mechanisms, HOV lanes. Matchmaking optimization between drivers and passengers.,
5. Car sharing services. Station-based vs. free floating, centralized vs. peer-to-peer car sharing. Fleet sizing issues.,
6. Smart parking systems, indoors and outdoors. Adaptive pricing solutions. International case studies.
7. Electric vehicles in smart cities. Construction and optimization of a charging network. Vehicle-to-Grid.
8. Smart grid, smart metering. Two-way electricity supply, integration of renewable energy sources, service models.
9. Smart buildings, smart homes. Water management and waste management in smart cities.
10. Environmental protection in smart cities. Reducing the carbon footprint of cities. Monitoring the formation of heat islands, air pollution issues.
11. Smart city management. Encourage community participation. Crowdsourcing and crowdsensing applications. Crowdfunding.
12. Security of smart cities. Security of sensor networks and IoT systems. Secure data transfer. Vehicle communication security, car hacking. Protection of critical infrastructure, cyber attacks. Data publicity issues, Open Data. Protection of the private sphere, anonymity, privacy.
13. Case studies, smart cities around the world. Singapore, Vienna/Aspern, Songdo, London, Barcelona, Santander, Toyota Woven City, Masdar, Dubai Expo City, Neom/The Line.
The detailed topics of the practical works:
1. Detailed description, discussion and assignment of semester homework. Detailed description of selectable devices and platforms
2. Map and map data management in services. Using maps in applications. Presentation of some available services, access to services: route planning, timetables.
3. Investigating parking space occupancy in a smart city. Sensor and camera solutions. Low-power, long-distance radio connections.
4. Homework intermediate control point: presentation and joint discussion of system plans, technological solution details
5. Analysis of smart city street traffic camera images using deep learning methods. Training a classification neural network for cars and pedestrians.
6. Object detection with deep learning methods. Use of object detection in smart city traffic.
7. Presentation, joint discussion and evaluation of homework.
Lectures
Classroom computer practice
Independent work (homework)
During the semester: Writing a partial exam and completing a homework. The condition for the signature and the possibility to take the exam is the successful completion of the partial exam and the homework.
During the exam period: Written exam.
