The Curiosity Lab is our technology playground.

We explore the limits and possibilities of frontier technologies for the Internet of Things, enterprise blockchains and large-scale data analytics.

Through in-house R&D we develop proprietary intellectual property and software building blocks that are leveraged in venture projects and consulting work.

1. IoT & M2M Connectivity

• Secure over-the-air (OTA) vehicle connectivity, in-vehicle data access frameworks
• Internet of Things messaging protocols / Machine 2 Machine data communication, Low-power wide-area connectivity for constrained devices

2. Data Analytics

• Physical modelling and simulation
• Predictive analytics and machine learning
• Cloud platform archirectures for large-scale data stream processing

3. Distributed Ledger Technologies / Blockchain

• Private, public and consortium networks
• Ethereum, Hyperledger Sawtooth, Hyperledger Fabric

Consulting work for the European Commission’s Joint Research Centre:

• Blockchain for Transport (BC4T) – Applications of Blockchain (BC) technology in the road transport sector (N. Tsoniotis, 2021-2022)
• PoC implementation of vehicle-to-cloud communication for the collection of CO2 emissions data from light-duty vehicles in the EU (D. Kourtesis, 2021)
• Review of vehicle to infrastructure data communication standards and technologies to support onboard fuel consumption monitoring [including support to DG CLIMA in preparing new EC legislation] (D. Kourtesis, 2019-2020)
• Data analysis and projections regarding road vehicle fleet CO2 emissions in the post 2020 period (S. Tsiakmakis, 2020)
• Ridechain – Feasibility study and prototyping of a blockchain-based transport service pricing and allocation platform (N. Tsoniotis, S. Tsiakmakis, D. Kourtesis, 2018-2019)

Consulting work for the European Commission’s DG Climate Action:

• Measuring the fuel consumption of light- and heavy-duty vehicles and monitoring their real-world fuel consumption. Subcontractors of the Consortium for Ultra Low Vehicle Emissions (CLOVE) under contract No. 340201/2019/817718/SER/CLIMA.C.4.

Co-financed research projects:

• AVANGARD – Advanced manufacturing solutions tightly aligned with business needs. €23 mil. Euro project funded under Horizon 2020 – Technologies for Factories of the Future (2019-2022). Main contribution: Application of blockchain in value chains for electric vehicle manufacturing.

Distributed Ledger Technologies:

• Tsoniotis, N. (2022). Smart Cities as Identities. AI, Machine Learning, & Optimization Tools for Smart Cities, Springer.
• Tsoniotis, N., Kourtesis, D., Tsiakmakis, S., Chistaras, V., Makridis, M., Kounelis, I., Fontaras, G. (2019).  Feasibility study and prototyping of a blockchain-based transport-service pricing and allocation platform. JRC Technical Reports, Publications Office of the European Union
• Paliokas, I., Tsoniotis, N., Votis, K., & Tzovaras, D. (2019). A Blockchain Platform in Connected Medical-Device Environments: Trustworthy Technology to Guard Against Cyberthreats. IEEE Consumer Electronics Magazine, 8(4), 50-55.

Internet of Things:

• Kourtesis, D. & Fontaras, G. (2022). Collection of Fleet-wide Fuel and Energy Consumption Data from Light Duty Vehicles in the EU. JRC Science for Policy Report, Publications Office of the European Union.
• Mourtzios, C., Kourtesis, D. Papadimitriou, N., Antzoulatos, G., Kouloglou, I., Vrochidis, S. (2019). SMART-WATER, A Novel Telemetry and Remote Control System Infrastructure for Managing Water Consumption in Thessaloniki (Best Work-In-Progress Paper Award). In: Proceedings of the 13th International Conference on Interactive Mobile Communication Technologies and Learning, 1192.
• Antzoulatos G, Mourtzios C, Stournara P, Kouloglou IO, Papadimitriou N, Spyrou D, Mentes A, Nikolaidis E, Karakostas A, Kourtesis D, Vrochidis S. (2020). Making urban water smart: The SMART-WATER solution. Water Science and Technology.

Physical modelling and simulation:

• Tsiakmakis, S., Fontaras, G., Dornoff, J., Valverde, V., Komnos, D., Ciuffo, B., … & Samaras, Z. (2019). From lab-to-road & vice-versa: Using a simulation-based approach for predicting real-world CO2 emissions. Energy, 169, 1153-1165.
• Tsiakmakis, S., Fontaras, G., Ciuffo, B., & Samaras, Z. (2017). A simulation-based methodology for quantifying European passenger car fleet CO2 emissions. Applied Energy, 199, 447-465.
• Tsiakmakis, S., Mertzis, D., Dimaratos, A., Toumasatos, Z., & Samaras, Z. (2014). Experimental study of combustion in a spark ignition engine operating with producer gas from various biomass feedstocks. Fuel, 122, 126-139.

Predictive analytics & machine learning:

• Deligiannis, A., Argyriou, C., & Kourtesis, D. (2019). Predictive personalization of conversational customer communications with data protection by design. In Proceedings of the IEEE/WIC/ACM 2019 International Conference on Web Intelligence – Companion Volume (pp. 305-308).
• Deligiannis, A., Argyriou, C., & Kourtesis, D. (2020). Building a Cloud-based Regression Model to Predict Click-through Rate in Business Messaging Campaigns. International Journal of Modeling and Optimization. 10. 26-31.
• Deligiannis, A., Argyriou, C. & Kourtesis, D. (2020). Predicting the Optimal Date and Time to Send Personalized Marketing Messages to Repeat Buyers. International Journal of Advanced Computer Science and Applications. 11. 90-99.