@conference {858,
	title = {PROVATO: A Data-Centric Edge-AI Architecture forPrecision Livestock Monitoring Using IoT},
	booktitle = {Hellenic Data Management Symposium (HDMS)},
	year = {2025},
	abstract = {<p>This work presents PROVATO (PRecisiOn liVestock monitoring with AI and IoT tOols), an edge AI and IoTpowered platform for real-time livestock monitoring. The system enables farmers to track animal health and behaviour, ensuring scalable data processing and AI inference at both the edge and the cloud. This approach supports decision-making, reduces resource waste, and improves animal welfare.</p>
},
	keywords = {IoT, livestock monitoring, livestock wellfare},
	author = {Andreas Doumouras and Evangelos Mitikas and Ermis Doulos and Manos Schoinoplokakis and Giannis Nikolentzos and Christos Tryfonopoulos and Spiros Skiadopoulos and Costas Vassilakis and Paraskevi Raftopoulou*}
}
@conference {591,
	title = {A crawler architecture for harvesting the clear, social, and dark web for IoT-related cyber-threat intelligence},
	booktitle = {In Proceedings of the IEEE Workshop on Cyber Security \& Resilience in the Internet of Things (CSRIoT @ IEEE Services)},
	year = {2016},
	month = {July 2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Milan, Italy},
	abstract = {The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information that {\textendash}given the appropriate tools and methods{\textendash} may be identified, crawled and subsequently leveraged to actionable cyber- threat intelligence. In this work, we focus on the information gathering task, and present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker fo- rums/marketplaces in the dark web. The proposed architecture adopts a two-phase approach to data harvesting. Initially a machine learning-based crawler is used to direct the harvesting towards websites of interest, while in the second phase state- of-the-art statistical language modelling techniques are used to represent the harvested information in a latent low-dimensional feature space and rank it based on its potential relevance to the task at hand. The proposed architecture is realised using exclusively open-source tools, and a preliminary evaluation with crowdsourced results demonstrates its effectiveness.},
	keywords = {crawling architecture, cyber-security, cyber-threat intelligence, IoT, language models, Machine Learning},
	url = {http://users.uop.gr/ trifon/papers/pdf/csriot19-KCTS.pdf},
	author = {Paris Koloveas and Thanasis Chantzios and Christos Tryfonopoulos and Spiros Skiadopoulos}
}
