The Connected Smart Cities for Greece 2.0 (Greece 2.0 NATIONAL RECOVERY AND RESILIENCE FUND) is a Flagship Action which aims to create a common conceptual service description model and data model for all Smart Cities in Greece. Connected Smart Cities for Greece 2.0 organizes a Tutorial entitled:
“Hands-on session with Greek Smart Cities platforms”
The session comprises presentations and demonstrations from indicative partners that will share their developed platforms with the attending audience.
Hands-on Activity: Building and Publishing Smart City Services on a Greek Smart-Cities Ecosystem Meta-Platform
Asterios Leonidis, Coordinator, ICS, Forth
In this hands-on activity at the IEEE Smart Cities Conference, participants will go through the complete process of integrating and deploying a new smart city service within a universal Smart Cities meta-platform. The session begins with the creation of a dedicated broker to onboard a new service into the platform, establishing it as part of the broader smart city ecosystem. Participants will then develop a simple web application that consumes the newly integrated service and visualizes its data, while also implementing Single Sign-On (SSO) to ensure secure and authorized access. Once the application is ready, it will be published in the Smart Cities App-Store, making it accessible to various user groups including the general public, registered citizens, and city officials. Finally, participants will switch roles to act as end-users, logging into the App-Store via the platform’s SSO mechanism to discover and interact with the newly deployed web application.
Time Series Analysis with Smart City Data from the Greek Smart Cities Ecosystem
Ioannis Tsampras, Tanya, Politi, Spyros Denazis, University of Patras
In this tutorial, you’ll learn how to connect to an NGSI-LD broker, query entities and their temporal evolution and consume data for a Smart City ML enabled forecast service. It will explain NGSI-LD and its temporal features, connect to the relevant broker in the project environment and collect data from multiple smart city entities. The demonstration will emphasize on the role of smart data models, interface standardization, federation and how they facilitate prediction services in diverse smart city environments.
Optimized Waste Collection in Kozani, A Smart City Demo using IoT and AI Technologies
Panteleimon Iosif, Emmanouil Karantoumanis, Panagiotis Sarigiannidis, Nikolaos Ploskas, University of Western Macedonia
As part of our Smart City initiative, we have developed a complete data-driven solution for optimizing municipal waste collection operations in the municipality of Kozani. The system is based on an IoT infrastructure that monitors the fill level of waste containers using smart sensors. The data is collected and exposed through open APIs using the NGSI-LD standard via the Scorpio Broker, following the WasteContainer data model. After collecting and preprocessing historical and real-time data, we trained machine learning models to predict the expected fill levels of each container over the next few days. These forecasts enable proactive planning by feeding into an optimization engine that calculates the optimal number of vehicles and corresponding routing plans. The goal is to minimize operational costs, avoid unnecessary pickups, and ensure timely waste collection. The proposed demo presents this full pipeline, including integration with the broker, the preprocessing and prediction phase, and the route optimization process.
Building monitoring & management’ services for Smart Cities
Vaggelis Spiliotis, Marina Solovyeva, Efstathios Stamatopoulos, National Technical University of Athens
Indoor environmental quality monitoring: This service enables real-time visualization of indoor environmental quality conditions across different areas of buildings by integrating sensor data, assessing the measurements based on international standards, and detecting anomalies or irregular patterns that may indicate suboptimal practices.
Energy management and forecasting: This service focuses on enhancing the efficiency and intelligence of energy usage in buildings by combining real-time data visualization, advanced analytics, and machine learning techniques, primarily utilized for energy consumption forecasting. Benchmark comparison and pattern visualization assist users identify periods with issues or excessive energy usage that may compromise efficiency.
A modular, real-time Digital Twin for smart buildings
Elissavet Pekridou, Christos Hitiris, Cleopatra Gkola, Dimitrios J. Vergados και Angelos Michalas, University of Western Macedonia:
A modular and interoperable Digital Twin has been designed for smart buildings, enabling real-time environmental and energy monitoring, user interaction, and decision support. The system ingests IoT sensor data via MQTT, stores it in a structured database, and exposes it through open RESTful APIs, supporting both data access and control flows. It incorporates predictive analytics and enables bidirectional communication for generating alerts, receiving user feedback, and supporting potential actuation. Semantic interoperability is ensured through integration with a FIWARE-compatible NGSI-LD context broker, allowing data to be published in alignment with established Smart Data Models. The demo will focus on two key components of the system: the dashboard, a web-based interface for real-time data visualization, intelligent alerts, and decision-making; and an immersive VR application where users can explore a virtual smart lab and interact with contextual sensor information.