@article{articlec,

title = {The road to 6G: a comprehensive survey of deep learning applications in cell-free massive MIMO communications systems},

author = {Lazaros Alexios Iliadis and Zaharias Zaharis and Sotirios Sotiroudis and Panagiotis Sarigiannidis and George Karagiannidis and Sotirios Goudos},

url = {https://www.researchgate.net/publication/362590698_The_road_to_6G_a_comprehensive_survey_of_deep_learning_applications_in_cell-free_massive_MIMO_communications_systems},

doi = {10.1186/s13638-022-02153-z},

year  = {2022},

date = {2022-08-01},

journal = {EURASIP Journal on Wireless Communications and Networking},

volume = {2022},

abstract = {The fifth generation (5G) of telecommunications networks is currently commercially deployed. One of their core enabling technologies is cellular Massive Multiple-Input-Multiple-Output (M-MIMO) systems. However, future wireless networks are expected to serve a very large number of devices and the current MIMO networks are not scalable, highlighting the need for novel solutions. At this moment, Cell-free Massive MIMO (CF M-MIMO) technology seems to be the most promising idea in this direction. Despite their appealing characteristics, CF M-MIMO systems face their own challenges, such as power allocation and channel estimation. Deep Learning (DL) has been successfully employed to a wide range of problems in many different research areas, including wireless communications. In this paper, a review of the state-of-the-art DL methods applied to CF M-MIMO communications systems is provided. In addition, the basic characteristics of Cell-free networks are introduced, along with the presentation of the most commonly used DL models. Finally, future research directions are highlighted.},

keywords = {6G, Cell-free massive MIMO, Deep Learning, User-centric cell-free massive MIMO},

pubstate = {published},

tppubtype = {article}

}

@article{Kotsiopoulos2021,

title = {Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm},

author = { T. Kotsiopoulos and P. Sarigiannidis and D. Ioannidis and D. Tzovaras},

url = {https://www.researchgate.net/publication/346545781_Machine_Learning_and_Deep_Learning_in_Smart_Manufacturing_The_Smart_Grid_Paradigm},

doi = {10.1016/j.cosrev.2020.100341},

year  = {2021},

date = {2021-05-01},

journal = {Computer Science Review},

volume = {40},

pages = {100341},

publisher = {Elsevier BV},

abstract = {Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.},

keywords = {Deep Learning, Industrial AI, Industry 4.0, machine learning, Smart Grid},

pubstate = {published},

tppubtype = {article}

}

@article{RadoglouGrammatikis2021,

title = {SPEAR SIEM: A Security Information and Event Management system for the Smart Grid},

author = { P. Radoglou-Grammatikis and P. Sarigiannidis and E. Iturbe and E. Rios and S. Martinez and A. Sarigiannidis and G. Eftathopoulos and I. Spyridis and A. Sesis and N. Vakakis and D. Tzovaras and E. Kafetzakis and I. Giannoulakis and M. Tzifas and A. Giannakoulias and M. Angelopoulos and F. Ramos},

url = {https://www.researchgate.net/publication/350287201_SPEAR_SIEM_A_Security_Information_and_Event_Management_system_for_the_Smart_Grid},

doi = {10.1016/j.comnet.2021.108008},

year  = {2021},

date = {2021-04-01},

journal = {Computer Networks},

pages = {108008},

publisher = {Elsevier BV},

abstract = {The technological leap of smart technologies has brought the conventional electrical grid in a new digital era called Smart Grid (SG), providing multiple benefits, such as two-way communication, pervasive control and self-healing. However, this new reality generates significant cybersecurity risks due to the heterogeneous and insecure nature of SG. In particular, SG relies on legacy communication protocols that have not been implemented having cybersecurity in mind. Moreover, the advent of the Internet of Things (IoT) creates severe cybersecurity challenges. The Security Information and Event Management (SIEM) systems constitute an emerging technology in the cybersecurity area, having the capability to detect, normalise and correlate a vast amount of security events. They can orchestrate the entire security of a smart ecosystem, such as SG. Nevertheless, the current SIEM systems do not take into account the unique SG peculiarities and characteristics like the legacy communication protocols. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) SIEM, which focuses on SG. The main contribution of our work is the design and implementation of a SIEM system capable of detecting, normalising and correlating cyberattacks and anomalies against a plethora of SG application-layer protocols. It is noteworthy that the detection performance of the SPEAR SIEM is demonstrated with real data originating from four real SG use case (a) hydropower plant, (b) substation, (c) power plant and (d) smart home.},

keywords = {Anomaly Detection, Cybersecurity, Deep Learning, Intrusion detection, machine learning, SCADA, Security Information and Event Management, Smart Grid},

pubstate = {published},

tppubtype = {article}

}

@article{Sun2021,

title = {End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance},

author = { Z. Sun, Y. Spyridis, T. Lagkas, A. Sesis, G. Efstathopoulos and P. Sarigiannidis},

url = {https://www.researchgate.net/publication/349726654_End-to-End_Deep_Graph_Convolutional_Neural_Network_Approach_for_Intentional_Islanding_in_Power_Systems_Considering_Load-Generation_Balance},

doi = {10.3390/s21051650},

year  = {2021},

date = {2021-02-01},

journal = {Sensors},

volume = {21},

number = {5},

pages = {1650},

publisher = {MDPI AG},

abstract = {Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result.},

keywords = {Deep Learning, Graph convolutional networks, graph partition, Intentional islanding, Load-generation balance, Power system, Spectral clustering},

pubstate = {published},

tppubtype = {article}

}

@article{Sotiroudis202130441,

title = {Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach},

author = { Sotirios P. Sotiroudis and Panagiotis Sarigiannidis and Sotirios K. Goudos and Katherine Siakavara},

url = {https://www.researchgate.net/publication/349369501_Fusing_Diverse_Input_Modalities_for_Path_Loss_Prediction_A_Deep_Learning_Approach},

doi = {10.1109/ACCESS.2021.3059589},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Access},

volume = {9},

pages = {30441--30451},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

abstract = {Tabular data and images have been used from machine learning models as two diverse types of inputs, in order to perform path loss predictions in urban areas. Different types of models are applied on these distinct modes of input information. The work at hand tries to incorporate both modes of input data within a single prediction model. It therefore manipulates and transforms the vectors of tabular data into images. Each feature of the tabular data vector is spread into several pixels, corresponding to the calculated importance of the particular feature. The resulting synthetic images are then fused with images representing selected regions of the area's map. Compound pseudoimages, having channels of both map-based and tabular data-based images, are then being used as inputs for a Convolutional Neural Network (CNN), which predicts the path loss value at a specific point of the area of interest. The results are clearly better than those obtained from models based on a single mode of input data, as well as from the results produced by other bimodal-input approaches. This approach could be applied for path loss prediction in urban environments for several state-of-art wireless networks like 5G and Internet of Things (IoT). © 2013 IEEE.},

keywords = {Convolutional Neural Networks, Data to Image Transformation, Deep Learning, Path loss, Pseudoimages, Radio Propagation},

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{Kotsiopoulos2020,

title = {Deep multi-sensorial data analysis for production monitoring in hard metal industry},

author = { T. Kotsiopoulos and L. Leontaris and N. Dimitriou and D. Ioannidis and F. Oliveira and J. Sacramento and S. Amanatiadis and G. Karagiannis and K. Votis and D. Tzovaras and P. Sarigiannidis},

url = {https://www.researchgate.net/publication/344881380_Deep_multi-sensorial_data_analysis_for_production_monitoring_in_hard_metal_industry},

doi = {10.1007/s00170-020-06173-1},

year  = {2020},

date = {2020-01-01},

journal = {International Journal of Advanced Manufacturing Technology},

abstract = {The industry practice of machining hard metal parts using CNC lathe turning machines is through grinding and milling procedures. The typical practice for quality control is through manual inspection, as automated solutions are difficult to integrate in production and do not reach the same level of accuracy. In this scope, the proposed system aims to automate the manufacturing process for the machine condition monitoring and 3D inspection of defective hard metal parts, by utilizing deep neural networks (DNNs) and investigating the defects on real production samples. Concretely, data are collected with (a) shop floor sensors, (b) high-resolution laser microprofilometer and (c) ultrasound scanner. The proposed system analyzes the collected data through AI models for quality control. Moreover, a fusion scheme is proposed to further improve accuracy. The system is validated on the classification of defective and non-defective samples, using metrics including accuracy, F-score, precision and recall for the performance evaluation. © 2020, Springer-Verlag London Ltd., part of Springer Nature.},

keywords = {Deep Learning, Deep multi-sensorial data analysis, Hard metal industry, Production monitoring, Smart manufacturing},

pubstate = {published},

tppubtype = {article}

}