2021
V. Moysiadis, P. Sarigiannidis, V. Vitsas, A. Khelifi
Smart Farming in Europe Journal Article
In: Computer Science Review, vol. 39, pp. 100345, 2021.
Περίληψη | BibTeX | Ετικέτες: Μεγάλα Δεδομένα και Ευφυείς Εφαρμογές στο Διαδίκτυο των Πραγμάτων, Cloud Computing, Image Processing, machine learning, smart farming, Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Wireless Sensor Networks (WSNs) | Σύνδεσμοι:
@article{Moysiadis2021,
title = {Smart Farming in Europe},
author = { V. Moysiadis and P. Sarigiannidis and V. Vitsas and A. Khelifi},
url = {https://www.researchgate.net/publication/346716261_Smart_Farming_in_Europe},
doi = {10.1016/j.cosrev.2020.100345},
year = {2021},
date = {2021-02-01},
journal = {Computer Science Review},
volume = {39},
pages = {100345},
publisher = {Elsevier BV},
abstract = {Smart Farming is the new term in the agriculture sector, aiming to transform the traditional techniques to innovative solutions based on Information Communication Technologies (ICT). Concretely, technologies like Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Image Processing, Machine Learning, Big Data, Cloud Computing, and Wireless Sensor Networks (WSNs), are expected to bring significant changes in this area. Expected benefits are the increase in production, the decrease in cost by reducing the inputs needed such as fuel, fertilizer and pesticides, the reduction in labor efforts, and finally improvement in the quality of the final products. Such innovative methods are crucial in recent days, due to the exponential increase of the global population, the importance of producing healthier products grown with as much fewer pesticides, where public opinion of European citizens is sensitized. Moreover, due to the globalization of the world economy, European countries face the low cost of production of other low-income countries. In this vein, Europe tries to evolve its agriculture domain using technology, aiming at the sustainability of its agricultural sector. Although many surveys exist, most of them tackle in a specific scientific area of Smart Farming. An overview of Smart Farming covering all the involved technologies and providing an extensive reference of good practices around Europe is essential. Our expectation from our work is to become a good reference for researchers and help them with their future work. This paper aims to provide a comprehensive reference for European research efforts in Smart Farming and is two-fold. First, we present the research efforts from researchers in Smart Farming, who apply innovative technology trends in various crops around Europe. Second, we provide and analyze the most significant projects in Europe in the area of Smart Farming. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.},
keywords = {Big Data, Cloud Computing, Image Processing, machine learning, smart farming, Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Wireless Sensor Networks (WSNs)},
pubstate = {published},
tppubtype = {article}
}
2020
D. Pliatsios, P. Sarigiannidis, K. Psannis, S. K. Goudos, V. Vitsas, I. Moscholios
Big Data against Security Threats: The SPEAR Intrusion Detection System Conference Paper
2020 3rd World Symposium on Communication Engineering (WSCE), IEEE, 2020.
Περίληψη | BibTeX | Ετικέτες: Μεγάλα Δεδομένα και Ευφυείς Εφαρμογές στο Διαδίκτυο των Πραγμάτων, Cyber Attack, intrusion detection system, Smart Grid | Σύνδεσμοι:
@conference{Pliatsios202012,
title = {Big Data against Security Threats: The SPEAR Intrusion Detection System},
author = { D. Pliatsios and P. Sarigiannidis and K. Psannis and S. K. Goudos and V. Vitsas and I. Moscholios},
doi = {10.1109/wsce51339.2020.9275580},
year = {2020},
date = {2020-10-01},
booktitle = {2020 3rd World Symposium on Communication Engineering (WSCE)},
journal = {2020 3rd World Symposium on Communication Engineering, WSCE 2020},
pages = {12-17},
publisher = {IEEE},
abstract = {The environmental concerns, the limited availability of conventional energy sources, the integration of alternative energy sources and the increasing number of power-demanding appliances change the way electricity is generated and distributed. Smart Grid (SG) is an appealing concept, which was developed in response to the emerging issues of electricity generation and distribution. By leveraging the latest advancements of Information and Communication Technologies (ICT), it offers significant benefits to energy providers, retailers and consumers. Nevertheless, SG is vulnerable to cyber attacks, that could cause critical economic and ecological consequences. Traditional Intrusion Detection Systems (IDSs) are becoming less efficient in detecting and mitigating cyberattacks, due to their limited capabilities of analyzing the exponentially increasing volume of network traffic. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) platform, which features a Big Data enabled IDS that timely detects and identifies cyber attacks against SG components. In order to validate the efficiency of the SPEAR platform regarding the protection of critical infrastructure, we installed the platform in a small wind power plant. © 2020 IEEE.},
keywords = {Big Data, Cyber Attack, intrusion detection system, Smart Grid},
pubstate = {published},
tppubtype = {conference}
}
A. Protopsaltis, P. Sarigiannidis, D. Margounakis, A. Lytos
Data Visualization in Internet of Things: Tools, Methodologies, and Challenges Conference Paper
Proceedings of the 15th International Conference on Availability, Reliability and Security, ARES '20 Association for Computing Machinery, Virtual Event, Ireland, 2020, ISBN: 9781450388337.
Περίληψη | BibTeX | Ετικέτες: Anomaly Detection, Μεγάλα Δεδομένα και Ευφυείς Εφαρμογές στο Διαδίκτυο των Πραγμάτων, Data visualization, Internet of Things (IoT) | Σύνδεσμοι:
@conference{Protopsaltis2020,
title = {Data Visualization in Internet of Things: Tools, Methodologies, and Challenges},
author = { A. Protopsaltis and P. Sarigiannidis and D. Margounakis and A. Lytos},
url = {https://www.researchgate.net/publication/343935293_Data_Visualization_in_Internet_of_Things_Tools_Methodologies_and_Challenges},
doi = {10.1145/3407023.3409228},
isbn = {9781450388337},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the 15th International Conference on Availability, Reliability and Security},
publisher = {Association for Computing Machinery},
address = {Virtual Event, Ireland},
series = {ARES '20},
abstract = {As the Internet of Things (IoT) grows rapidly, huge amounts of wireless sensor networks emerged monitoring a wide range of infrastructure, in various domains such as healthcare, energy, transportation, smart city, building automation, agriculture, and industry producing continuously streamlines of data. Big Data technologies play a significant role within IoT processes, as visual analytics tools, generating valuable knowledge in real-time in order to support critical decision making. This paper provides a comprehensive survey of visualization methods, tools, and techniques for the IoT. We position data visualization inside the visual analytics process by reviewing the visual analytics pipeline. We provide a study of various chart types available for data visualization and analyze rules for employing each one of them, taking into account the special conditions of the particular use case. We further examine some of the most promising visualization tools. Since each IoT domain is isolated in terms of Big Data approaches, we investigate visualization issues in each domain. Additionally, we review visualization methods oriented to anomaly detection. Finally, we provide an overview of the major challenges in IoT visualizations.},
keywords = {Anomaly Detection, Big Data, Data visualization, Internet of Things (IoT)},
pubstate = {published},
tppubtype = {conference}
}
A. Lytos, T. Lagkas, P. Sarigiannidis, M. Zervakis, G. Livanos
Towards smart farming: Systems, frameworks and exploitation of multiple sources Journal Article
In: Computer Networks, vol. 172, 2020.
Περίληψη | BibTeX | Ετικέτες: Agriculture, Μεγάλα Δεδομένα και Ευφυείς Εφαρμογές στο Διαδίκτυο των Πραγμάτων, Internet of things, machine learning, smart farming | Σύνδεσμοι:
@article{Lytos2020,
title = {Towards smart farming: Systems, frameworks and exploitation of multiple sources},
author = { A. Lytos and T. Lagkas and P. Sarigiannidis and M. Zervakis and G. Livanos},
url = {https://www.researchgate.net/publication/339221382_Towards_Smart_Farming_Systems_Frameworks_and_Exploitation_of_Multiple_Sources},
doi = {10.1016/j.comnet.2020.107147},
year = {2020},
date = {2020-01-01},
journal = {Computer Networks},
volume = {172},
abstract = {Agriculture is by its nature a complicated scientific field, related to a wide range of expertise, skills, methods and processes which can be effectively supported by computerized systems. There have been many efforts towards the establishment of an automated agriculture framework, capable to control both the incoming data and the corresponding processes. The recent advances in the Information and Communication Technologies (ICT) domain have the capability to collect, process and analyze data from different sources while materializing the concept of agriculture intelligence. The thriving environment for the implementation of different agriculture systems is justified by a series of technologies that offer the prospect of improving agricultural productivity through the intensive use of data. The concept of big data in agriculture is not exclusively related to big volume, but also on the variety and velocity of the collected data. Big data is a key concept for the future development of agriculture as it offers unprecedented capabilities and it enables various tools and services capable to change its current status. This survey paper covers the state-of-the-art agriculture systems and big data architectures both in research and commercial status in an effort to bridge the knowledge gap between agriculture systems and exploitation of big data. The first part of the paper is devoted to the exploration of the existing agriculture systems, providing the necessary background information for their evolution until they have reached the current status, able to support different platforms and handle multiple sources of information. The second part of the survey is focused on the exploitation of multiple sources of information, providing information for both the nature of the data and the combination of different sources of data in order to explore the full potential of ICT systems in agriculture. © 2020 The Authors},
keywords = {Agriculture, Big Data, Internet of things, machine learning, smart farming},
pubstate = {published},
tppubtype = {article}
}
Διεύθυνση
Internet of Things and Applications Lab
Department of Electrical and Computer Engineering
University of Western Macedonia Campus
ZEP Area, Kozani 50100
Greece
Πληροφορίες Επικοινωνίας
tel: +30 2461 056527
Email: ithaca@uowm.gr