@article{Kelli2021b,

title = {Ids for industrial applications: A federated learning approach with active personalization},

author = { V. Kelli and V. Argyriou and T. Lagkas and G. Fragulis and E. Grigoriou and P. Sarigiannidis},

url = {https://www.researchgate.net/publication/355191910_IDS_for_Industrial_Applications_A_Federated_Learning_Approach_with_Active_Personalization},

doi = {10.3390/s21206743},

year  = {2021},

date = {2021-01-01},

journal = {Sensors},

volume = {21},

number = {20},

abstract = {Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

note = {cited By 0},

keywords = {Active learning, Critical infrastructure, Federated learning, IDS, IoT, machine learning, Personalization},

pubstate = {published},

tppubtype = {article}

}

@article{Spyridis2021b,

title = {Towards 6g iot: Tracing mobile sensor nodes with deep learning clustering in uav networks},

author = { Y. Spyridis and T. Lagkas and P. Sarigiannidis and V. Argyriou and A. Sarigiannidis and G. Eleftherakis and J. Zhang},

url = {https://www.researchgate.net/publication/352197709_Towards_6G_IoT_Tracing_Mobile_Sensor_Nodes_with_Deep_Learning_Clustering_in_UAV_Networks},

doi = {10.3390/s21113936},

year  = {2021},

date = {2021-01-01},

journal = {Sensors},

volume = {21},

number = {11},

abstract = {Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

keywords = {6G, Deep Learning, Graph convolutional network, IoT, RSSI, Sensor tracking, unmanned aerial vehicles},

pubstate = {published},

tppubtype = {article}

}

@article{Tsouros2019b,

title = {A review on UAV-based applications for precision agriculture},

author = { D.C. Tsouros and S. Bibi and P.G. Sarigiannidis},

url = {https://www.researchgate.net/publication/337187714_A_Review_on_UAV-Based_Applications_for_Precision_Agriculture},

doi = {10.3390/info10110349},

year  = {2019},

date = {2019-01-01},

journal = {Information (Switzerland)},

volume = {10},

number = {11},

abstract = {Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations. © 2019 by the authors.},

keywords = {Image Processing, IoT, precision agriculture, remote sensing, Review, smart farming, UAS, UAV, Unmanned Aerial System, Unmanned Aerial Vehicle},

pubstate = {published},

tppubtype = {article}

}

@article{Lagkas2018b,

title = {UAV IoT framework views and challenges: Towards protecting drones as “things”},

author = { T. Lagkas and V. Argyriou and S. Bibi and P. Sarigiannidis},

url = {https://www.researchgate.net/publication/328968155_UAV_IoT_Framework_Views_and_Challenges_Towards_Protecting_Drones_as_Things},

doi = {10.3390/s18114015},

year  = {2018},

date = {2018-01-01},

journal = {Sensors (Switzerland)},

volume = {18},

number = {11},

abstract = {Unmanned aerial vehicles (UAVs) have enormous potential in enabling new applications in various areas, ranging from military, security, medicine, and surveillance to traffic-monitoring applications. Lately, there has been heavy investment in the development of UAVs and multi-UAVs systems that can collaborate and complete missions more efficiently and economically. Emerging technologies such as 4G/5G networks have significant potential on UAVs equipped with cameras, sensors, and GPS receivers in delivering Internet of Things (IoT) services from great heights, creating an airborne domain of the IoT. However, there are many issues to be resolved before the effective use of UAVs can be made, including security, privacy, and management. As such, in this paper we review new UAV application areas enabled by the IoT and 5G technologies, analyze the sensor requirements, and overview solutions for fleet management over aerial-networking, privacy, and security challenges. Finally, we propose a framework that supports and enables these technologies on UAVs. The introduced framework provisions a holistic IoT architecture that enables the protection of UAVs as “flying” things in a collaborative networked environment. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.},

keywords = {Drones, IoT, Privacy, security, UAV},

pubstate = {published},

tppubtype = {article}

}