Nessie platform is an information system, developed within NTUA, able to acquire, process and store, and in general to manage, high-resolution data from IoT agends, such as sensors and smart meters, coupled with analytics (to translate the data into information) and visualisation capabilities to present the information to the end-users.
Nessie platform is composed by 6 independent components: a) the data collector/parser components that collects raw data from a source, such as an FTP server; b) the Data Mapper component that formats the data in way that is supported by the third component, the Database server; c) The GIS-enabled PostgreSQL Database Server that stores the data; d) The Apache Web Server that supports the web character of Nessie; e) the Back-end engine, the heart of Nessie, that supports the processing of stored data and implements a series of embedded analytics; f) And finally, the web interface that delivers information to the end-user.
Owner of the product
National Technical University of Athens (NTUA)
Actors, their roles and interactions
Nessie is a highly customisable system which can be adapted to support the specific requirements of any smart monitoring and control system. The system is continously being upgraded in the framework of EU- and National-funded projects, and so far it has been deployed to support smart applications for the real-time monitoring and control of water consumption (for both water utilities and end-users/householders), remote control of Sewer Mining Units and management of Subsurface Water Systems. Currently, special focus has been given to connect Nessie with FIWARE platform (in the framework of Fiware4Water project) to support the development of interoperable and standardised digital solutions.
Unique selling points
Nessie system is a in-house product, developed within NTUA.
Minimum technical requirements:
- Python version v2.8
- Django Web Application Framework v2.2
- Pandas Data Analysis Library for Python (for time-series data manipulation) v0.24
- NumPy (scientific computing library for Python) v1.18
- REST Framework for greater portability and easier maintenance 3.11.
- GIS-ENABLED PostgreSQL (v>9.5)
- Makropoulos, C., Kossieris, P., Kozanis, S., Katsiri, E., Vamvakeridou-Lyroudia, L., 2014. From smart meters to smart decisions: web-based support for the water efficient household. 11th International Conference on Hydroinformatics, Proceedings HIC2014, 17-21 August 2014, New York, USA, 2014.
- Kossieris, P., Panayiotakis, A., Tzouka, K., Gerakopoulou, P., Rozos, E., Makropoulos, C., 2014. An eLearning Approach for Improving Household Water Efficiency. Procedia Engineering, 89, 1113–1119. https://doi.org/10.1016/j.proeng.2014.11.232.
- Kossieris, P., Kozanis, S., Hashmi, A., Katsiri, E., Vamvakeridou-Lyroudia, L.S., Farmani, R., Makropoulos, C., Savic, D., 2014. A Web-based Platform for Water Efficient Households. Procedia Engineering, 89, 1128–1135. https://doi.org/10.1016/j.proeng.2014.11.234.
- Kossieris, P., Pantazis, C., Makropoulos, C., 2021. Data-models for FIWARE-enabled smart applications for raw-water supply system modelling, management and operation. Advances in Hydroinformatics: SIMHYDRO 2021, SimHydro 6th International Conference, June 16-18 2021 (in press).