Καθηγητής Βασίλειος Καρυώτης

@article{panagiotopoulos2026demystifying,

title = {Demystifying the Digital Transformation of Humanitarian Supply Chains Through AidTech},

author = {Apostolos Panagiotopoulos and Vasileios Karyotis and Georgios N. Dimitrakopoulos and Vassiliki Choleva-Tsiringkaki and Panos E. Kourouthanassis},

url = {https://doi.org/10.3390/logistics10050110},

doi = {10.3390/logistics10050110},

year  = {2026},

date = {2026-01-01},

journal = {Logistics},

volume = {10},

number = {5},

pages = {110},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Background: Humanitarian campaigns are more popular with more initiatives organized than ever by public and private bodies. Simultaneously, more peculiar challenges, such as zero-waste and transparency, are required compared to traditional supply chains. This work investigates the current technological landscape for shaping the next generation of humanitarian aid management systems, focusing on algorithmic and operational aspects, matching them to a new architecture for a target-platform, called AidTech. Methods: The methodology follows a systematic literature coverage, identifying cross-disciplinary trends from operations research, computer science and sustainable logistics. We examine the convergence of optimization, artificial intelligence, and blockchain-enabled traceability toward efficient and transparent humanitarian logistics. The employed methods include adaptive scheduling, resource allocation algorithms and privacy–preserving collaboration for meeting special constraints imposed by the humanitarian scope. Results: The findings focus on the architectural perspective of humanitarian supply chains and highlight that modern humanitarian infrastructures will increasingly rely on hybrid optimization methods integrating graph theory, dynamic routing under stochastic demand, multi-criteria decision analysis and distributed ledger technologies. Conclusions: We conclude that these paradigms, when combined under a unified cyber–physical architecture, e.g., AidTech, can substantially improve responsiveness, equity and sustainability in crisis management, further shaping future humanitarian logistics.},

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@article{kallitsis2026optimal,

title = {Optimal Control of Wireless Data and Power Transfer in Energy Harvesting Consumer Networks},

author = {Georgios Kallitsis and Eleni Stai and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/tce.2026.3679014},

doi = {10.1109/tce.2026.3679014},

year  = {2026},

date = {2026-01-01},

journal = {IEEE Transactions on Consumer Electronics},

volume = {72},

number = {2},

pages = {3858–3872},

publisher = {Institute of Electrical and Electronics Engineers},

abstract = {Wireless power transfer is one of the new technologies promising to revolutionize consumer electronics in the future, especially low-power portable devices. In this paper, we propose optimal control strategies for simultaneous data and energy transfers in energy harvesting wireless networks, focusing on enhancing efficiency and performance in low-power portable devices and IoT applications. We develop an optimization framework for joint energy-data transfers in multihop wireless networks with energy harvesting capabilities in two cases, namely that of orthogonal and non-orthogonal medium access. Devices are assumed to follow half-duplex operation, thereby splitting time into mutually exclusive data and energy transfer phases, ensuring alternating, virtually simultaneous data-energy transfers within time intervals, even with low-cost consumer electronics. Distributed algorithms are proposed for congestion control, routing, power control, and time-splitting decisions. Both cases are formulated as non-convex optimization problems, which are demonstrated to have zero duality gap, therefore enabling the use of dual-based solution methods. Numerical evaluations show that the proposed algorithms outperform other schemes (e.g., those without power control or energy transfer) in terms of higher source rates (almost double for most nodes), efficient energy consumption, and extending network lifetime. Their efficacy is also depicted in realistic and frequently encountered scenarios in consumer electronics, characterized by non-uniform energy harvesting, as well as dynamically changing topologies. The results demonstrate the applicability of the proposed framework in real-world scenarios, particularly for IoT and low-power consumer electronics networks.},

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@article{kallitsis2025optimization,

title = {An optimization framework for joint wireless data-power transmission in distributed energy harvesting networks},

author = {Georgios Kallitsis and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1016/j.comnet.2025.111506},

doi = {10.1016/j.comnet.2025.111506},

year  = {2025},

date = {2025-01-01},

journal = {Computer Networks},

volume = {270},

pages = {111506},

publisher = {Elsevier BV},

abstract = {In this paper, we address the challenge of performing effective joint wireless data-energy transfers in distributed mobile energy-harvesting networks. In principle, wireless power transfer resembles energy harvesting, however, it exhibits its own special features. We develop a holistic, backpressure-inspired technique, describing the evolution of each node’s queue-battery state, and we define an optimization problem with the objective of improving the balance of energy. We implement a dual Lagrange multipliers solution and determine the key variables influencing the system’s behavior. We investigate the overall energy transfer from the periphery to the core network in cases of traffic-stressed core nodes, and through analysis and simulation, we demonstrate the theoretical and practical potentials of this framework and its potential use for greener and self-sustainable networks.},

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@article{panagiotopoulos2025commercial,

title = {Commercial Off-the-Shelf IoT-Based Infant Car Seat Application for Preventing the Forgotten Baby Syndrome},

author = {Apostolos Panagiotopoulos and Vasileios Karyotis},

url = {https://doi.org/10.3390/fi17100443},

doi = {10.3390/fi17100443},

year  = {2025},

date = {2025-01-01},

journal = {Future Internet},

volume = {17},

number = {10},

pages = {443},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {The Forgotten Baby Syndrome (FBS), the accidental abandonment of infants in vehicles, continues to result in otherwise preventable tragedies worldwide. This work presents a prototype system called SafeCuddle, designed to mitigate the risks associated with FBS. The proposed solution utilizes an Arduino UNO R4 WiFi microcontroller integrated with low-cost IoT sensors for real-time data acquisition and processing. Processed signals are visualized via a Python-based desktop application. A key feature of the system is its ability to issue immediate alerts to the driver upon detecting their departure from the vehicle while an infant remains seated in the vehicle. An extensive review of the syndrome’s etiology identifies disrupted routines and the high demands of modern life as primary contributing factors. In response, the proposed system can be easily implemented with commercial off-the-shelf components and aims to support caregivers by acting as a fail-safe mechanism. The paper is structured into two primary sections: (i) an analytical overview of FBS and its contributing factors and (ii) a detailed account of the system’s design, implementation, operational workflow, and evaluation results. The unique contribution of this work lies in the integration of a low-cost, real-time alert system within a modular and easily deployable architecture that can be integrated in existing infant car seats as an aftermarket solution, if properly commercialized, specifically tailored to prevent FBS through immediate driver feedback at the critical moment of risk.},

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@article{dimitriou2025heuristic,

title = {Heuristic Custom Similarity Index (HCSI): A novel machine learning approach for link prediction},

author = {Paraskevas Dimitriou and Vasileios Karyotis},

url = {https://doi.org/10.1016/j.jocs.2025.102719},

doi = {10.1016/j.jocs.2025.102719},

year  = {2025},

date = {2025-01-01},

journal = {Journal of Computational Science},

volume = {92},

pages = {102719},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kakkavas20245g,

title = {5G Perspective Of Connected Autonomous Vehicles: Current Landscape and Challenges Toward 6G},

author = {Grigorios Kakkavas and Maria Diamanti and Vasileios Karyotis and Kwame Nseboah Nyarko and Matthias Gabriel and Anastasios Zafeiropoulos and Symeon Papavassiliou and Klaus Moessner},

url = {https://doi.org/10.1109/mwc.014.2300277},

doi = {10.1109/mwc.014.2300277},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Wireless Communications},

volume = {31},

number = {4},

pages = {299–306},

publisher = {Institute of Electrical and Electronics Engineers},

abstract = {Connected and autonomous vehicles (CAVs) have emerged as a promising paradigm in the automotive industry, revolutionizing transportation systems with their potential to improve safety, efficiency, and overall user experience. The communication aspect of CAVs is vital to enable their advanced capabilities. In this article, we focus on analyzing the current landscape, challenges, and future vision of CAVs from a 5G perspective. Unlike previous theoretical studies, our approach is grounded in empirical evidence obtained through trials conducted over a commercial 5G deployment, considering two representative use cases: "Remote Driving" and "Quality of Service (QoS)-based Level-of-Automation Selection." Based on the quantitative findings, we identify and discuss several open challenges that need to be overcome to seamlessly integrate CAVs into the 5G ecosystem. Furthermore, we outline prospective enablers for solutions and enhancements that can address the identified shortcomings and provide a solid foundation for the future of CAVs in the context of 6G. Overall, this research contributes to the ongoing efforts to optimize communication systems for CAVs, facilitating their safe and efficient integration into the future smart transportation ecosystem.},

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@article{stai2024scalable,

title = {A Scalable and Efficient Backpressure-Based Scheduling Framework for Supply Chain Networks},

author = {Eleni Stai and Vasileios Karyotis and Panos E. Kourouthanassis},

url = {https://doi.org/10.1109/access.2024.3452276},

doi = {10.1109/access.2024.3452276},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Access},

volume = {12},

pages = {122047–122060},

publisher = {Institute of Electrical and Electronics Engineers},

abstract = {Supply chain networks have proven to be rather important and delicate to seemingly small perturbations of their operations. Although scheduling has been extensively studied in logistics systems, there are several remaining open challenges regarding scalability, stability, and other quality indices. In this work, we present a novel framework denoted as BackPressure-style Packet transfer algorithm for Logistics Systems (BPLS) for making jointly optimal routing and scheduling decisions in freight networks. The proposed approach is based on the backpressure algorithm and maximum weight matching, which have been extensively applied for optimal routing and scheduling of data packet transmissions in communications networks. Our goal is to develop a broader optimal transfer process for freight networks consisting of multiple entities, such as last-mile companies, freight subcontractors, etc., addressing all the previously mentioned challenges and in addition, allow for setting different optimization goals regarding the offered quality of service. Special features of freight networks such as the limited capacities of both storage places and transportation means along with the time-varying availability of the latter are considered by means of integrating pressure functions in the original backpressure approach. We provide extensive simulations with evaluation and comparison results on the performance of the approach, demonstrating its potential to improve up to more than$100times $on the traditional BP algorithm. In addition, we have incorporated an implementation in an operational information system to assess the potentials of BPLS with multiple interested stakeholders. Through simulations and the actual evaluation, we were able to show how the framework can be used to provide long and short term decisions for optimizing holistically freight networks.},

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@article{alexandridis2024streetlines,

title = {StreetLines: A Smart and Scalable Tourism Platform Based on Efficient Knowledge-Mining},

author = {Georgios Alexandridis and Georgios Siolas and Tasos Papagiannis and George Ioannou and Konstantinos Michalakis and George Caridakis and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.3390/digital4030034},

doi = {10.3390/digital4030034},

year  = {2024},

date = {2024-01-01},

journal = {Digital},

volume = {4},

number = {3},

pages = {676–697},

publisher = {MDPI AG},

abstract = {Identifying and understanding visitor needs and expectations is of the utmost importance for a number of stakeholders and policymakers involved in the touristic domain. Apart from traditional forms of feedback, an abundance of related information exists online, scattered across various data sources like online social media, tourism-related platforms, traveling blogs, forums, etc. Retrieving and analyzing the aforementioned content is not a straightforward task and in order to address this challenge, we have developed the StreetLines platform, a novel information system that is able to collect, analyze and produce insights from the available tourism-related data. Its highly modular architecture allows for the continuous monitoring of varying pools of heterogeneous data sources whose contents are subsequently stored, after preprocessing, in a data repository. Following that, the aforementioned data feed a number of independent and parallel processing modules that extract useful information for all individuals involved in the tourism domain, like place recommendation for visitors and sentiment analysis and keyword extraction reports for professionals in the tourism industry. The presented platform is an outcome of the StreetLines project and apart from the contributions of its individual components, its novelty lies in the holistic approach to knowledge extraction and tourism data mining.},

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@article{Stavropoulou2024,

title = {Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes},

author = {G. Stavropoulou and K. Tsitseklis and L. Mavraidi and K. -I. Chang and A. Zafeiropoulos and V. Karyotis and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191412428&doi=10.3390%2fs24082618&partnerID=40&md5=cf5b51c8b7dcd4b785279973b67a7847},

doi = {10.3390/s24082618},

issn = {14248220},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Sensors},

volume = {24},

number = {8},

publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},

abstract = {In the highly competitive field of material manufacturing, stakeholders strive for the increased quality of the end products, reduced cost of operation, and the timely completion of their business processes. Digital twin (DT) technologies are considered major enablers that can be deployed to assist the development and effective provision of manufacturing processes. Additionally, knowledge graphs (KG) have emerged as efficient tools in the industrial domain and are able to efficiently represent data from various disciplines in a structured manner while also supporting advanced analytics. This paper proposes a solution that integrates a KG and DTs. Through this synergy, we aimed to develop highly autonomous and flexible DTs that utilize the semantic knowledge stored in the KG to better support advanced functionalities. The developed KG stores information about materials and their properties and details about the processes in which they are involved, following a flexible schema that is not domain specific. The DT comprises smaller Virtual Objects (VOs), each one acting as an abstraction of a single step of the Industrial Business Process (IBP), providing the necessary functionalities that simulate the corresponding real-world process. By executing appropriate queries to the KG, the DT can orchestrate the operation of the VOs and their physical counterparts and configure their parameters accordingly, in this way increasing its self-awareness. In this article, the architecture of such a solution is presented and its application in a real laser glass bending process is showcased. © 2024 by the authors.},

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@article{Dimitriou202445044,

title = {Empowering Random Walk Link Prediction Algorithms in Complex Networks by Adapted Structural Information},

author = {P. Dimitriou and V. Karyotis},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189142829&doi=10.1109%2fACCESS.2024.3381510&partnerID=40&md5=87890e22714752dd52338e7a791b1f94},

doi = {10.1109/ACCESS.2024.3381510},

issn = {21693536},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Access},

volume = {12},

pages = {45044-45059},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In the link prediction problem a relevant algorithm running over a network attempts to determine whether a link between two nodes will exist in the future, given that it is not present at the moment. Most link prediction algorithms take into account the structure of the network on which they are applied and based on this, they attempt to predict the existence or not of future new edges in the network. However, many of them are quite standardized, applying the same concept and parametrization to all networks, thus not always achieving good results in every different network structure. Algorithms based on Graph Neural Networks (GNNs) are more adaptive to any network structure but they do not give appreciable results when the only information available is the network structure. In this paper, we propose a new approach to this problem that approximates the structure of a complex network by allowing adjusted weight to this network structure to create additional information, which we can embed into effective algorithms such as local and superposed random walk link prediction. To achieve this goal, we use well-known kernel functions such as Sigmoids, in which we fit their parameters appropriately by a genetic algorithm to achieve the best possible approximation. To demonstrate the effectiveness of our proposed method we have compared our prediction method results based on precision, AUC and AUPR on eleven selected networks of different structures and properties with seven well-known link prediction algorithms and one more utilizing GNNs. In every case, we have improved the results of random walk algorithms and in most cases we achieved better results from all employed benchmark algorithms. © 2013 IEEE.},

note = {cited By 0},

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@article{Kakkavas20241029,

title = {Generative Deep Learning Techniques for Traffic Matrix Estimation from Link Load Measurements},

author = {G. Kakkavas and N. Fryganiotis and V. Karyotis and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183941616&doi=10.1109%2fOJCOMS.2024.3358740&partnerID=40&md5=bbc1e767cb9b5d8bf30cadd9973dd81a},

doi = {10.1109/OJCOMS.2024.3358740},

issn = {2644125X},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Open Journal of the Communications Society},

volume = {5},

pages = {1029-1046},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Traffic matrices (TMs) contain crucial information for managing networks, optimizing traffic flow, and detecting anomalies. However, directly measuring traffic to construct a TM is resource-intensive and computationally expensive. A more practical approach involves estimating the TM from readily available link load measurements, which falls under the category of inferential network monitoring based on indirect measurements known as network tomography. This paper focuses on solving the problem of estimating the traffic matrix from link loads by utilizing deep generative models. The proposed models are trained using historical data - specifically, previously observed TMs - and are then leveraged to transform traffic matrix estimation (TME) into a simpler minimization problem in a lower-dimensional latent space. This transformed problem can be efficiently solved using a gradient-based optimizer. Our work aims to examine and test different model architectures and optimization approaches. The performance of the proposed methods is comparatively evaluated over a comprehensive set of suitable metrics on two publicly available datasets comprising actual traffic matrices obtained from real backbone networks. In addition, we compare our approach with a state-of-the-art method previously published in the literature. © 2020 IEEE.},

note = {cited By 0},

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@article{dimitriou2023combinatory,

title = {A Combinatory Framework for Link Prediction in Complex Networks},

author = {Paraskevas Dimitriou and Vasileios Karyotis},

url = {https://doi.org/10.3390/app13179685},

doi = {10.3390/app13179685},

year  = {2023},

date = {2023-01-01},

journal = {Applied Sciences},

volume = {13},

number = {17},

pages = {9685},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Link prediction is a very important field in network science with various emerging algorithms, the goal of which is to estimate the presence or absence of an edge in the network. Depending on the type of network, different link prediction algorithms can be applied, being less or more effective in the relevant scenarios. In this work, we develop a novel framework that attempts to compose the best features of link prediction algorithms when applied to a network, in order to have even more reliable predictions, especially in topologies emerging in Industrial Internet of Things (IIoT) environments. According to the proposed framework, we first apply appropriate link prediction algorithms that we have chosen for an analyzed network (basic algorithms). Each basic algorithm gives us a numerical estimate for each missing edge in the network. We store the results of each basic algorithm in appropriate structures. Then we provide them as input to a developed genetic algorithm. The genetic algorithm evaluates the results of the basic algorithms for each missing edge of the network. At each missing edge of the network and from generation to generation, it composes the estimates of the basic algorithms regarding each edge and produces a new optimized estimate. This optimization results in a vector of weights where each weight corresponds to the effectiveness of the prediction for each of the basic algorithms we have employed. With these weights, we build a new enhanced predictor tool, which can obtain new optimized estimates for each missing edge in the network. The enhanced predictor tool applies to each missing edge the basic algorithms, normalizes the basic algorithms’ estimates, and, using the weights of the estimates derived from the genetic algorithm, returns a new estimate of whether or not an edge will be added in the future. According to the results of our experiments on several types of networks with five well-known link prediction algorithms, we show that the new enhanced predictor tool yields in every case better predictions than each individual algorithm, therefore providing an accuracy-targeting alternative in the existing state of the art.},

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@article{Dimitriou2023,

title = {On the computation of Delaunay triangulations via genetic algorithms},

author = {P. Dimitriou and V. Karyotis},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180719695&doi=10.1007%2fs12065-023-00893-5&partnerID=40&md5=ce28c67dcb7bd7bbfa3417161b1fa975},

doi = {10.1007/s12065-023-00893-5},

issn = {18645909},

year  = {2023},

date = {2023-01-01},

journal = {Evolutionary Intelligence},

volume = {13},

number = {17},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {In this work, we introduce a new approach for computing Delaunay triangulations. Delaunay triangulations have numerous applications in geolocation, communications and other ICT systems or practical applications. Having available various types of approaches for computing such structures is rather desired from an implementation and computational point of view. We adopt Genetic Algorithms for computing Delaunay triangulations and present the design and evaluation of our novel approach. We consider a set of points in the plane as vertices and connect them with edges, creating the point graph. We have developed in C++ an application framework based on genetic algorithms, called Delaunay_Genetic, which produces the Delaunay triangulation structure of a given set of points in the plane. Delaunay_Genetic considers a novel graph-based chromosome representation of desired solutions, creates an initial population of individuals (chromosomes), an initial generation, and produces from the original population (generation) new generations of individuals in each repetition of the genetic process of Reproduction. Each new generation emerges more robust than the previous one. Our evaluations have revealed that the Delaunay triangulation yielded by Delaunay_Genetic, achieves an accuracy of 98–100% of the optimal Delaunay triangulation, while maintaining good convergence speed. Despite its limitations in computational time and space, the proposed novel approach exhibits several complementary benefits to computational geometry based approaches, such as allowing the insertion of new points in the triangulation dynamically, leading to seamless adaptation to new conditions, parallelization of the computational process and tolerance to noise regarding the coordinates of the points. Therefore, this work provides a useful alternative approach for computing Delaunay triangulations. © 2023, The Author(s).},

note = {cited By 0},

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@article{kakkavas2022design,

title = {Design, Development, and Evaluation of 5G-Enabled Vehicular Services: The 5G-HEART Perspective},

author = {Grigorios Kakkavas and Maria Diamanti and Adamantia Stamou and Vasileios Karyotis and Faouzi Bouali and Jarno Pinola and Olli Apilo and Symeon Papavassiliou and Klaus Moessner},

url = {https://doi.org/10.3390/s22020426},

doi = {10.3390/s22020426},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Sensors},

volume = {22},

number = {2},

pages = {426},

publisher = {MDPI},

abstract = {The ongoing transition towards 5G technology expedites the emergence of a variety of mobile applications that pertain to different vertical industries. Delivering on the key commitment of 5G, these diverse service streams, along with their distinct requirements, should be facilitated under the same unified network infrastructure. Consequently, in order to unleash the benefits brought by 5G technology, a holistic approach towards the requirement analysis and the design, development, and evaluation of multiple concurrent vertical services should be followed. In this paper, we focus on the Transport vertical industry, and we study four novel vehicular service categories, each one consisting of one or more related specific scenarios, within the framework of the “5G Health, Aquaculture and Transport (5G-HEART)” 5G PPP ICT-19 (Phase 3) project. In contrast to the majority of the literature, we provide a holistic overview of the overall life-cycle management required for the realization of the examined vehicular use cases. This comprises the definition and analysis of the network Key Performance Indicators (KPIs) resulting from high-level user requirements and their interpretation in terms of the underlying network infrastructure tasked with meeting their conflicting or converging needs. Our approach is complemented by the experimental investigation of the real unified 5G pilot’s characteristics that enable the delivery of the considered vehicular services and the initial trialling results that verify the effectiveness and feasibility of the presented theoretical analysis. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},

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@article{avgeris2022enerdge,

title = {ENERDGE: Distributed energy-aware resource allocation at the edge},

author = {Marios Avgeris and Dimitrios Spatharakis and Dimitrios Dechouniotis and Aris Leivadeas and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.3390/s22020660},

doi = {10.3390/s22020660},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Sensors},

volume = {22},

number = {2},

pages = {660},

publisher = {MDPI},

abstract = {Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},

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@article{kakkavas2022topology,

title = {Topology Inference and Link Parameter Estimation Based on End-to-End Measurements},

author = {Grigorios Kakkavas and Vasileios Karyotis and Symeon Papavassiliou},

year  = {2022},

date = {2022-01-01},

journal = {Future Internet},

volume = {14},

number = {2},

pages = {45},

publisher = {MDPI},

keywords = {},

pubstate = {published},

tppubtype = {article}

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@article{kallitsis2022potential,

title = {On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information},

author = {Georgios Kallitsis and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.3390/fi14080242},

doi = {10.3390/fi14080242},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Future Internet},

volume = {14},

number = {8},

pages = {242},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {In this paper, we study the potential of using the metric of Age of Information (AoI) for enhancing delay-tolerant routing protocols. The latter have been proposed for alleviating the impact of long roundtrip time in networks operating in harsh environments, e.g., in distributed applications deployed in a desert/sparsely populated area without infrastructure, a space network, etc. Delay-tolerant routing protocols can prevent excessive packet timer expiration, but do not provide any packet delivery time guarantee. Thus, they are unsuitable for time-sensitive applications that are more intensely desired nowadays in the next generation networking applications. By incorporating AoI into the operation of delay-tolerant routing protocols, we aim at devising routing protocols that can cope with both long propagation times and challenges related to time-sensitivity in packet delivery. More specifically, in this work, we modify the operation of a well-known delay-tolerant routing protocol, namely FRESH, to make AoI-based packet forwarding decisions, aiming at achieving specific delay guarantees regarding the end-to-end delivery time. We investigate the advantages and disadvantages of such an approach compared to the traditional FRESH protocol. This work serves as a cornerstone for successfully demonstrating the potential of exploiting AoI in delay-tolerant routing and its applications. © 2022 by the authors.},

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@article{karageorgiou2022markov,

title = {Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks},

author = {Stavros Karageorgiou and Vasileios Karyotis},

url = {https://doi.org/10.3390/network2030028},

doi = {10.3390/network2030028},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Network},

volume = {2},

number = {3},

pages = {456--478},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {In this paper, we focus on the dynamics of the spread of malicious software (malware) in multi-layer networks of various types, e.g., cyber-physical systems. Recurring malware has been one of the major challenges in modern networks, and significant research and development has been dedicated to mitigating it. The majority of relevant works has focused on networks characterized by “flat” topologies, namely topologies in which all nodes consist of a single layer, studying the dynamics of propagation of a specific threat or various types of malware over a homogeneous topology. As cyber-physical systems and multi-layer networks in general are gaining in popularity and penetration, more targeted studies are needed. In this work, we focus on the propagation dynamics of recurring malware, namely Susceptible–Infected–Susceptible (SIS type) in multi-layer topologies consisting of combinations of two different types of networks, e.g., a small-world overlaying a random geometric, or other such combinations. We utilize a stochastic modeling framework based on Markov Random Fields for analyzing the propagation dynamics of malware over such networks. Through analysis and simulation, we discover the most vulnerable and the most robust topology among the six considered combinations, as well as a result of rather practical use, namely that the denser the network, the more flexibility it provides for malware mitigation eventually. © 2022 by the authors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tsitseklis2020scalable,

title = {Scalable Community Detection for Complex Data Graphs via Hyperbolic Network Embedding and Graph Databases},

author = {Konstantinos Tsitseklis and Maria Krommyda and Vasileios Karyotis and Verena Kantere and Symeon Papavassiliou},

url = {https://doi.org/10.1109/TNSE.2020.3022248},

doi = {10.1109/TNSE.2020.3022248},

year  = {2021},

date = {2021-01-01},

urldate = {2020-01-01},

journal = {IEEE Transactions on Network Science and Engineering},

volume = {8},

number = {2},

pages = {1269--1282},

publisher = {IEEE},

abstract = {Community detection and its variations is one of the typically employed approaches for analyzing graph data originating from various diverse fields. In this paper, we focus on a particular approach for community detection capitalizing on hyperbolic network embedding, which is aimed at analyzing large data graphs. In order to enable its scaling to arbitrary sized data sets and respective data graphs, we extend it by incorporating a graph database approach. This allows for handling a larger number of nodes and edges in the data graph. Also, we turn our focus on the discovery and visualization of communities in Resource Description Framework (RDF) data, namely over linked datasets from diverse areas, explaining how our approach can accommodate relevant analysis. We demonstrate the applicability of the new approach over both real-world and artificially generated datasets showing its feasibility in producing correct results, while being able to scale seamlessly in large datasets. The approach can be used for multi-lateral analysis of feature-rich graph data, originating from diverse sources, enabling the discovery of hidden correlations through the hyperbolic network embedding. © 2013 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tsitseklis2021socio,

title = {Socio-Aware Recommendations Under Complex User Constraints},

author = {Konstantinos Tsitseklis and Margarita Vitoropoulou and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/TCSS.2020.3046686},

doi = {10.1109/TCSS.2020.3046686},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Transactions on Computational Social Systems},

volume = {8},

number = {2},

pages = {377--387},

publisher = {IEEE},

abstract = {In this work, we consider the joint behavior of an information diffusion process integrated with a recommender system (RecSys) over an online social network (OSN), where the typical users' resilience to information varies, leading to potential information overloads. We assume that each user has a threshold over the information that she can process in a meaningful way, and exceeding it could lead to user dissatisfaction or the user remaining idle. In order to efficiently tackle this issue, while considering complex user constraints, we consider two types of users' capacity to information, that is, capacity for distinct items and capacity for duplicate items. In this setting, we aim to allocate the items in the OSN in order to maximize users' total relevance to the former while ensuring that no user exceeds any type of capacity. We show that this problem is NP-complete and present various heuristic methods to address it. A novel framework, called socially constrained recommendations (SCoRe), is developed for the final assignment of items to users, consisting of a two-step procedure. We present and evaluate two different approaches for each step and discuss the usability of SCoRe for the efficient diffusion of items in the network while respecting the users' constraints. © 2014 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{vitoropoulou2021cover,

title = {CoveR: An Information Diffusion Aware Approach for Efficient Recommendations Under User Coverage Constraints},

author = {Margarita Vitoropoulou and Konstantinos Tsitseklis and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/TCSS.2021.3067711},

doi = {10.1109/TCSS.2021.3067711},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Transactions on Computational Social Systems},

volume = {8},

number = {4},

pages = {894--905},

publisher = {IEEE},

abstract = {In this article, we consider the problem of recommendations to the users of an online social network (OSN), through an information diffusion aware recommender system (IDARS). We map the assignment of recommendations in influence networks to a problem of selecting an $ell $ -cover of the minimum total cost, which is defined to be a set of assignments such that each user in the OSN is recommended of at least $ell $ different items at the minimum defined cost. This corresponds to a special case of the minimum weighted partition set cover problem, which is a generalization of the minimum weighted set cover problem, both of which are proven to be NP-hard. We formulate a corresponding integer programming problem and we apply a linear programming (LP)-based branch and bound (BnB) methodology for its solution. We also propose a greedy algorithm, denoted as CoveR, which we show to be an $O((Delta /delta )cdot H(Delta))$ -approximation for the $ell $ -coverage problem, where $Delta $ and $delta $ are the maximum and minimum degree of an influence network, respectively, and $H(Delta)$ is the $Delta text th$ harmonic number. We investigate CoveR's performance through extensive simulations on both synthetic and real networks, which indicate that the quality of its solution is comparable to the one obtained by the BnB method, while at the same time outperforms other information diffusion-aware recommendation heuristics. © 2014 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bouali20215g,

title = {5G for Vehicular Use Cases: Analysis of Technical Requirements, Value Propositions and Outlook},

author = {Faouzi Bouali and Jarno Pinola and Vasileios Karyotis and Bastiaan Wissingh and Michalis Mitrou and Prageeth Krishnan and Klaus Moessner},

url = {https://doi.org/10.1109/OJITS.2021.3072220},

doi = {10.1109/OJITS.2021.3072220},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Open Journal of Intelligent Transportation Systems},

volume = {2},

pages = {73--96},

publisher = {IEEE},

abstract = {The fifth generation (5G) of wireless networks promises to meet the stringent requirements of vehicular use cases that cannot be supported by previous technologies. However, the stakeholders of the automotive industry (e.g., car manufacturers and road operators) are still skeptical about the capability of the telecom industry to take the lead in a market that has been dominated by dedicated intelligent transport systems (ITS) deployments. In this context, this paper constructs a framework where the potential of 5G to support different vehicular use cases is thoroughly examined under a common format from both the technical and business perspectives. From the technical standpoint, a storyboard description is developed to explain when and how different use case scenarios may come into play (i.e., pre-conditions, service flows and post-conditions). Then, a methodology to trial each scenario is developed including a functional architecture, an analysis of the technical requirements and a set of target test cases. From the business viewpoint, an initial analysis of the qualitative value perspectives is conducted considering the stakeholders, identifying the pain points of the existing solutions, and highlighting the added value of 5G in overcoming them. The future evolution of the considered use cases is finally discussed. © 2020 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kakkavas2021network,

title = {Network tomography for efficient monitoring in SDN-enabled 5G networks and beyond: Challenges and opportunities},

author = {Grigorios Kakkavas and Adamantia Stamou and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/MCOM.001.2000458},

doi = {10.1109/MCOM.001.2000458},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Communications Magazine},

volume = {59},

number = {3},

pages = {70--76},

publisher = {IEEE},

abstract = {Efficient monitoring plays a vital role in software-defined networking (SDN)-enabled 5G networks, involving the monitoring of performance metrics for both physical and virtual networks and considering the requirements of a variety of traffic types originating from different applications. However, conventional network monitoring tools based on traditional IP-based mechanisms may not be able to keep up with the dynamic nature of 5G networks. Network tomography (NT) is an emerging monitoring approach that estimates network performance based on measurements realized at a limited subset of network elements, presenting benefits over traditional monitoring techniques, but susceptible to identifiability issues. In this article, we demonstrate how NT can respond to current monitoring challenges, complementing and working together with SDN, yielding accurate estimations with low overhead, while exploiting SDN capabilities such as the centralized view of the entire network, direct flow-level measurements, and controllable routing. Furthermore, we present the spectrum of applications of NT-based solutions in 5G networks and beyond, ranging from virtual to vehicular networks. © 1979-2012 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{vlachakis2021improving,

title = {Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer’s Disease},

author = {Dimitrios Vlachakis and Eleni Papakonstantinou and Ram Sagar and Flora Bacopoulou and Themis Exarchos and Panos Kourouthanassis and Vasileios Karyotis and Panayiotis Vlamos and Constantine Lyketsos and Dimitrios Avramopoulos and Vasiliki Mahairaki},

url = {https://doi.org/10.3390/cells10071627},

doi = {10.3390/cells10071627},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Cells},

volume = {10},

number = {7},

pages = {1627},

publisher = {MDPI},

abstract = {The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person’s SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer’s Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kakkavas2020review,

title = {A review of advanced algebraic approaches enabling network tomography for future network infrastructures},

author = {Grigorios Kakkavas and Despoina Gkatzioura and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.3390/fi12020020},

doi = {10.3390/fi12020020},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Future Internet},

volume = {12},

number = {2},

pages = {20},

publisher = {MDPI},

abstract = {Network tomography has emerged as one of the lean approaches for efficient network monitoring, especially aiming at addressing the ever-increasing requirements for scaling and efficiency in modern network architectures and infrastructures. In this paper, we explore network coding and compressed sensing as enabling technologies in the context of network tomography. Both approaches capitalize on algebraic tools for achieving accuracy while allowing scaling of operation as the size of the monitored network increases. Initially, a brief overview of the tomographic problems and the related classification of methods is provided to better comprehend the problems encountered and solutions provided to date. Subsequently, we present representative approaches that employ either one of the aforementioned technologies and we comparatively describe their fundamental operation. Eventually, we provide a qualitative comparison of features and approaches that can be used for further research and technology development for network monitoring in future Internet infrastructures. © 2020 by the authors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kakkavas2020software,

title = {A software defined radio cross-layer resource allocation approach for cognitive radio networks: From theory to practice},

author = {Grigorios Kakkavas and Konstantinos Tsitseklis and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/TCCN.2019.2963869},

doi = {10.1109/TCCN.2019.2963869},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IEEE Transactions on Cognitive Communications and Networking},

volume = {6},

number = {2},

pages = {740--755},

publisher = {IEEE},

abstract = {Software Defined Radio (SDR)-enabled cognitive radio network architectures are expected to play an important role in the future 5G networks. Despite the increased research interest, the current implementations are of small-scale and provide limited functionality. In this paper, we contribute towards the alleviation of the limitations in SDR deployments by developing and evaluating a resource allocation approach for cognitive radios implemented with SDR technology over two testbeds of the ORCA federation. Resource allocation is based on a Markov Random Field (MRF) framework realizing a distributed cross-layer computation for the secondary nodes of the cognitive radio network. The proposed framework implementation consists of self-contained modules developed in GNU Radio realizing cognitive functionalities, such as spectrum sensing, collision detection, etc. We demonstrate the feasibility of the MRF based resource allocation approach and provide extensive results and performance analysis that highlight its key features. The latter provide useful insights about the advantages of our framework, while allowing to pinpoint current technological barriers of broader interest. © 2015 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stai2020optimal,

title = {Optimal resource allocation in multihop wireless networks relying on energy harvesting},

author = {Eleni Stai and Vasileios Karyotis},

year  = {2020},

date = {2020-01-01},

journal = {IEEE Communications Letters},

volume = {25},

number = {1},

pages = {224--228},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karyotis2017markov,

title = {A Markov random field framework for modeling malware propagation in complex communications networks},

author = {Vasileios Karyotis},

url = {https://doi.org/10.1109/TDSC.2017.2703622},

doi = {10.1109/TDSC.2017.2703622},

year  = {2019},

date = {2019-01-01},

urldate = {2017-01-01},

journal = {IEEE Transactions on Dependable and Secure Computing},

volume = {16},

number = {4},

pages = {551--564},

publisher = {IEEE},

abstract = {The proliferation of complex communication networks (CCNs) and their importance for maintaining social coherency nowadays have urgently elevated the need for protecting networking infrastructures from malicious software attacks. In this paper, we propose a Markov Random Field (MRF) based spatio-stochastic framework for modeling the macroscopic behavior of a CCN under random attack, where malicious threats propagate through direct interactions and follow the Susceptible-Infected-Susceptible infection paradigm. We exploit the MRF framework for analytically studying the propagation dynamics in various types of CCNs, i.e., lattice, random, scale-free, small-world and multihop graphs, in a holistic manner. By combining Gibbs sampling with simulated annealing, we study the behavior of the above systems for various topological and malware related parameters with respect to the general random attacks considered. We demonstrate the effectiveness of the MRF framework in capturing the evolution of SIS malware propagation and use it to assess the robustness of synthetic and real CCNs with respect to the involved parameters. It is found that random networks are more robust, followed by scale-free, regular and small-world, while multihop emerge as the most vulnerable of all. © 2004-2012 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{vitoropoulou2019sensing,

title = {Sensing and monitoring of information diffusion in complex online social networks},

author = {Margarita Vitoropoulou and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1007/s12083-018-0684-7},

doi = {10.1007/s12083-018-0684-7},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Peer-to-Peer Networking and Applications},

volume = {12},

number = {3},

pages = {604--619},

publisher = {Springer US},

abstract = {Sensing and monitoring information diffusion in online social networks is a complex problem of prominent importance, typically requiring significant sensing resources to address it properly. In this paper, we propose an inference approach for an information diffusion process where information is considered to belong to different classes, characterized by different spreading dynamics and possibly different topical content. Our framework utilizes social network analysis metrics in order to reduce the sensing resources that would be required in an otherwise exhaustive approach, while employing statistical learning and probabilistic inference for maintaining the accuracy of information tracking, whenever needed. The proposed framework defines an edge coloring scheme, based on which it is possible to keep track of information diffusion. We assume that the latter spreads according to various biased random walks that represent the dynamics of the considered classes of information. We have employed learning for the inference of those cases where backtracking leads to multiple potential choices for information paths. We demonstrate the operation and efficacy of our approach in characteristic online social networks, such as distributed wireless (spatial) and scale-free (relational) topologies, and draw conclusions on the impact of topology on information spreading. Finally, we discuss the emerging trends applicable for each topology and provide broader guidelines on the suitability of the proposed information diffusion inference scheme for each network. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rey2019mathematical,

title = {Mathematical Models for Malware Propagation},

author = {Ángel Mart'in del Rey and Lu-Xing Yang and Vasileios A Karyotis},

url = {https://doi.org/10.1155/2019/6046353},

doi = {10.1155/2019/6046353},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Security and Communication Networks},

volume = {2019},

publisher = {Hindawi},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stamou2019autonomic,

title = {Autonomic network management and cross-layer optimization in software defined radio environments},

author = {Adamantia Stamou and Grigorios Kakkavas and Konstantinos Tsitseklis and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.3390/fi11020037},

doi = {10.3390/fi11020037},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Future Internet},

volume = {11},

number = {2},

pages = {37},

publisher = {MDPI},

abstract = {The demand for Autonomic Network Management (ANM) and optimization is as intense as ever, even though significant research has been devoted towards this direction. This paper addresses such need in Software Defined (SDR) based Cognitive Radio Networks (CRNs). We propose a new framework forANMand network reconfiguration combining Software Defined Networks (SDN) with SDR via Network Function Virtualization (NFV) enabled Virtual Utility Functions (VUFs). This is the first approach combining ANM with SDR and SDN via NFV, demonstrating how these state-of-the-art technologies can be effectively combined to achieve reconfiguration flexibility, improved performance and efficient use of available resources. In order to show the feasibility of the proposed framework, we implemented its main functionalities in a cross-layer resource allocation mechanism for CRNs over real SDR testbeds provided by the Orchestration and Reconfiguration Control Architecture (ORCA) EU project. We demonstrate the efficacy of our framework, and based on the obtained results, we identify aspects that can be further investigated for improving the applicability and increasing performance of our broader framework. © 2019 by the authors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stai2018temporal,

title = {Temporal dynamics of information diffusion in twitter: Modeling and experimentation},

author = {Eleni Stai and Eirini Milaiou and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/TCSS.2017.2784184},

doi = {10.1109/TCSS.2017.2784184},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {IEEE Transactions on Computational Social Systems},

volume = {5},

number = {1},

pages = {256--264},

publisher = {IEEE},

abstract = {Twitter constitutes an accessible platform for studying and experimenting with the dynamics of information dissemination. By exploiting this and using real data, in this paper, we study the temporal dynamics of topic-specific information spread in Twitter, where we assume that each topic corresponds to a hashtag. We develop an epidemic model for information spread in Twitter and we validate it using real data for several hashtags chosen so as to cover a variety of characteristics. Contrary to the existing works in literature, which define the informed Twitter users as those who have produced/reproduced tweets with a specific hashtag, our model considers as informed a superset of Twitter users who have seen/produced/reproduced tweets with a specific hashtag. Thus, it does not underestimate the extent of information propagation in the network. The evaluation results indicate a satisfactory performance of the proposed epidemic model for all hashtag types examined; while more importantly, they allow studying the impact of several factors, such as the need of time-varying infection rates depending on the hashtag type. © 2014 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karyotis2018big,

title = {Big data clustering via community detection and hyperbolic network embedding in IoT applications},

author = {Vasileios Karyotis and Konstantinos Tsitseklis and Konstantinos Sotiropoulos and Symeon Papavassiliou},

url = {https://doi.org/10.3390/s18041205},

doi = {10.3390/s18041205},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Sensors},

volume = {18},

number = {4},

pages = {1205},

publisher = {MDPI},

abstract = {In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stai2017strategy,

title = {Strategy evolution of information diffusion under time-varying user behavior in generalized networks},

author = {Eleni Stai and Vasileios Karyotis and Antonia-Chrysanthi Bitsaki and Symeon Papavassiliou},

url = {https://doi.org/10.1016/j.comcom.2016.12.013},

doi = {10.1016/j.comcom.2016.12.013},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Computer Communications},

volume = {100},

pages = {91--103},

publisher = {Elsevier},

abstract = {Modern generalized networks, where physical devices and social players are combined to support seamless and more flexible services, are strongly driven by user behavior and its evolution. A typical example of behavior-affected processes is the information diffusion by users in generalized networks. In this paper, we focus on user strategy selection with respect to information diffusion in generalized networks, and study its evolutionary dynamics and their benefits, as user behavior/interests change over the course of time. We model information diffusion in generalized networks through graphical evolutionary game theory (graphical EGT) and analyze the evolution of the system and its Evolutionary Stable States (ESS) for time-varying fitness values due to time-varying user interests. We perform extensive evaluations based on numerical and simulation results, and study the impact of several parameters on the derived ESS curves (i.e., ESS points over time). Such parameters involve user interests and their fitness values, and the degree distributions and network type of the layers of the generalized network. We thoroughly evaluate via numerical analysis and appropriate examples the existence of ESS loci for the system dynamics depending on the associated parameters. Also, we discuss the relation between (graphical) EGT-based information diffusion analysis and the epidemics-based information diffusion modeling and analysis that is frequently applied in the literature. Our modeling and evaluations can be used to predict the evolution of the cumulative behavior, allowing for efficient design and control of information campaigns. © 2016},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stai2017hyperbolic,

title = {Hyperbolic embedding for efficient computation of path centralities and adaptive routing in large-scale complex commodity networks},

author = {Eleni Stai and Konstantinos Sotiropoulos and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/TNSE.2017.2690258},

doi = {10.1109/TNSE.2017.2690258},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {IEEE Transactions on Network Science and Engineering},

volume = {4},

number = {3},

pages = {140--153},

publisher = {IEEE},

abstract = {Computing the most central nodes in large-scale commodity networks is rather important for improving routing and associated applications. In this paper, we introduce a novel framework for the analysis and efficient computation of routing path-based centrality measures, focusing on betweenness and traffic load centrality. The proposed framework enables efficient approximation and in special cases accurate computation of the aforementioned measures in large-scale complex networks, as well as improving/adapting commodity (traffic) routing by identifying and alleviating key congestion points. It capitalizes on network embedding in hyperbolic space and exploits properties of greedy routing over hyperbolic coordinates. We show the computational benefits and approximation precision of our approach by comparing it with state-of-the-art path centrality computation techniques. We demonstrate its applicability on real topologies, characteristic of actual large-scale commodity networks, e.g., data, utility networks. Focusing on two graph embedding types, Rigel and greedy, we compare their impact on the performance of our framework. Then, we exemplify and statistically analyze the dynamic routing adaptation, via the variation of the minimum-depth spanning tree employed for greedy embedding in hyperbolic space. Notably, this allows for efficient routing adaptation according to a simple, distributed computation that can be applied during network operation to alleviate arising bottlenecks. © 2013 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stai2016hyperbolic,

title = {A hyperbolic space analytics framework for big network data and their applications},

author = {Eleni Stai and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/MNET.2016.7389825},

doi = {10.1109/MNET.2016.7389825},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {IEEE Network},

volume = {30},

number = {1},

pages = {11--17},

publisher = {IEEE},

abstract = {Big data analytics have generated a paradigm shift in modern data analysis and decision making in almost every aspect of human society. Nowadays, massive amounts of generated network and correlated (networked) data pose critical computational and storage challenges, requiring the development of radical techniques to manage, process, and analyze them more efficiently. We propose embedding such data and their correlations in hyperbolic metric spaces as one approach aspiring to radically change current practices. In this article, we explore the potential that such data embedding and the corresponding hyperbolic space based data analytics can offer to networks, their applications, and their services. We demonstrate how this approach may lead to more efficient and scalable problem solving within diverse application domains, such as network design/analysis, network resource allocation optimization, and network economics/marketing, paving the way for more diverse and effective solutions in the future. © 2016 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karyotis2015macroscopic,

title = {Macroscopic malware propagation dynamics for complex networks with churn},

author = {Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/LCOMM.2015.2399925},

doi = {10.1109/LCOMM.2015.2399925},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {IEEE Communications Letters},

volume = {19},

number = {4},

pages = {577--580},

publisher = {IEEE},

abstract = {In this letter, a queueing-based approach for modeling malware propagation in complex networks with churn (dynamic node variation) is introduced. The proposed methodology captures the dynamics of SIS-type malware, especially in time-varying networks, where nodes enter and leave due to their limited energy reserves, or as the outcome of malware attacks. We demonstrate the developed model in various complex networks, showing how it can be exploited for analytically quantifying network reliability and further used for increasing the robustness of the network against generic malware attacks. © 2015 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stai2015analysis,

title = {Analysis and control of information diffusion dictated by user interest in generalized networks},

author = {Eleni Stai and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1186/s40649-015-0025-4},

doi = {10.1186/s40649-015-0025-4},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Computational Social Networks},

volume = {2},

number = {1},

pages = {1--31},

publisher = {SpringerOpen},

abstract = {The diffusion of useful information in generalized networks, such as those consisting of wireless physical substrates and social network overlays is very important for theoretical and practical applications. Contrary to previous works, we focus on the impact of user interest and its features (e.g., interest periodicity) on the dynamics and control of diffusion of useful information within such complex wireless-social systems. By considering the impact of temporal and topical variations of users interests, e.g., seasonal periodicity of interest in summer vacation advertisements which spread more effectively during Spring–Summer months, we develop an epidemic-based mathematical framework for modeling and analyzing such information dissemination processes and use three indicative operational scenarios to demonstrate the solutions and results that can be obtained by the corresponding differential equation-based formalism. We then develop an optimal control framework subject to the above information diffusion modeling that allows controlling the trade-off between information propagation efficiency and the associated cost, by considering and leveraging on the impact that user interests have on the diffusion processes. By analysis and extensive simulations, significant outcomes are obtained on the impact of each network layer and the associated interest parameters on the dynamics of useful information diffusion. Furthermore, several behavioral properties of the optimal control of the useful information diffusion with respect to the number of infected/informed nodes and the evolving user interest are shown through analysis and verified via simulations. Specifically, a key finding is that low interest-related diffusion can be aided by utilizing proper optimal controls. Our work in this paper paves the way towards this user-centered information diffusion framework. © 2015, Stai et al.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{anifantis2014spatio,

title = {A spatio-stochastic framework for cross-layer design in cognitive radio networks},

author = {Evangelos Anifantis and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/TPDS.2013.2297108},

doi = {10.1109/TPDS.2013.2297108},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {IEEE Transactions on Parallel and Distributed Systems},

volume = {25},

number = {11},

pages = {2762--2771},

publisher = {IEEE},

abstract = {In this paper, we address the problem of distributed resource management for secondary users in Cognitive Radio Networks (CRNs), through a topology aware and frequency agile cross-layer approach. We exploit the theory of spatial processes and propose a Markov Random Field (MRF) based framework, which enables secondary CRN users to achieve efficient and viable mechanisms in the lower protocol stack layers by exchanging local only information. Specifically, through Gibbs sampling secondary users can optimize in a distributed and parallel manner their channel allocation, medium access and routing without resolving to otherwise computationally demanding optimization approaches. Through analysis and simulation we exhibit the efficacy of the proposed framework and show that a semi-parallel implementation can significantly reduce the required overhead cost compared to the sequential Gibbs sampling approach, while retaining a very close performance to the latter. We also study the emerging trade-offs by demonstrating the performance benefits in terms of channel assignment, medium access and data flow. © 2014 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stai2014exploiting,

title = {Exploiting socio-physical network interactions via a utility-based framework for resource management in mobile social networks},

author = {Eleni Stai and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/MWC.2014.6757892},

doi = {10.1109/MWC.2014.6757892},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {IEEE Wireless Communications},

volume = {21},

number = {1},

pages = {10--17},

publisher = {IEEE},

abstract = {Mobile social networks have the lion's share in modern mobile telecommunications, and their interaction with the underlying infrastructure networks has attracted significant attention due to its impact on resource management. In this article, we present and demonstrate a framework for addressing such interplay between online social networks and wireless communications by exploiting principles from the theory of utility-based engineering and elements from social network analysis. We aim at a holistic design framework that allows the joint development of improved resource management mechanisms for future mobile wireless infrastructures and their social counterparts. We demonstrate the proposed methodology and reveal the key aspects of designing and exploiting convenient utility functions within the framework of network science in order to better manage the available resources, improve infrastructures, and eventually obtain from them the maximum possible benefit. We establish the above principles and emerging design potentials in future complex networks by presenting two tangible examples where personalized advertising and topology control in MSNs are used to exploit and validate different network and individual socio-utility maximization features, respectively. © 2014 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{arkoulis2014discovering,

title = {Discovering and exploiting spectrum power correlations in cognitive radio networks: an experimentally driven approach},

author = {Stamatios Arkoulis and Evangelos Anifantis and Vasileios Karyotis and Symeon Papavassiliou and Nikolaos Mitrou},

url = {https://doi.org/10.1186/1687-1499-2014-17},

doi = {10.1186/1687-1499-2014-17},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

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

volume = {2014},

number = {1},

pages = {1--14},

publisher = {SpringerOpen},

abstract = {In this paper, we focus on increasing the spectrum awareness of cognitive radio users through statistical processing of spectrum sensing data, obtained via wideband energy-detection-based sensing techniques. Based on observations over real spectrum power measurements, we advocate the existence of correlation properties in the sensed power of measured neighboring channels and propose an inference methodology for exploiting them towards acquiring a more accurate view of the underlying wireless environment. To highlight the benefits of the proposed correlation-based inference methodology on cognitive radio systems, we emphasize on enhancements of white space discovery and channel selection processes, while we thoroughly discuss the impact of our findings on existing relevant approaches. Based on a systematic wireless spectrum survey in the metropolitan area of Athens, Greece, we validate our proposed methodology and assess the achieved performance improvements through the obtained measurement data, confirming its potential value in future cognitive radio networks. © 2014 Arkoulis et al.; licensee Springer.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{anifantis2014exploiting,

title = {Exploiting social features for improving cognitive radio infrastructures and social services via combined MRF and back pressure cross-layer resource allocation},

author = {Evangelos Anifantis and Eleni Stai and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1186/s40649-014-0004-1},

doi = {10.1186/s40649-014-0004-1},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Computational Social Networks},

volume = {1},

number = {1},

pages = {1--27},

publisher = {SpringerOpen},

abstract = {The observed unbalance of spectrum utilization combined with the proliferation of wireless devices and the growth of online social networks call for a joint study of cognitive radios (CRs) and social networking towards improving the operation of both the underlying CR-capable communications infrastructures and of the corresponding dynamic networks. In this work, we address such issue and focus on the twofold direction of resource allocation in CRs and social feature exploitation with the aim of providing a holistic cross-layer optimization framework that jointly considers information from the social layer as well as the traditional network protocol layers. We combine Markov random field (MRF) cross-layer decisions with back pressure (BP) features for the allocation of resources, in order to improve the operation of CRs that act as social network substrate. Espousing the recent advances in the field of dynamic spectrum allocation, the proposed BP-enhanced MRF (BPeMRF) network optimization serves the purpose of improving in a low complexity manner the capacity of agile infrastructures, which are required to adapt fast according to the demands of the volatile CR environment and the overlaying dynamic online social networks. In addition, within the proposed socially enhanced BPeMRF (SeBPeMRF) approach, we leverage emerging social information to further enhance performance. The obtained results exhibit the efficacy of BPeMRF/SeBPeMRF regarding the above objectives and demonstrate significant promise for further improving the corresponding infrastructures and services. © 2014, Anifantis et al.; licensee Springer.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{arkoulis2013optimal,

title = {On the optimal, fair and channel-aware cognitive radio network reconfiguration},

author = {Stamatios Arkoulis and Evangelos Anifantis and Vasileios Karyotis and Symeon Papavassiliou and Nikolaos Mitrou},

url = {https://doi.org/10.1016/j.comnet.2013.03.004},

doi = {10.1016/j.comnet.2013.03.004},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

journal = {Computer Networks},

volume = {57},

number = {8},

pages = {1739--1757},

publisher = {Elsevier},

abstract = {In this work, we focus on the Joint Channel Assignment and Routing (JCAR) problem in Cognitive Radio Networks (CRNs) and especially on the optimal reconfiguration of secondary networks under the presence of primary users. Secondary CRN users need to adapt their transmission channels promptly, while effectively limit additional or escalating system modifications triggered by the intertweaved primary user activity. Our approach takes into consideration the underlying spectrum switching dynamics and concurrently aims at a fair resource allocation among the active network flows. We take an optimization perspective and formulate the JCAR and network reconfiguration problems as mixed integer linear programs, addressing fairness concerns as well. We propose a heuristic approach which is based on a sequential reduced search space methodology, in order to obtain efficiently solutions of otherwise tough and demanding reconfiguration problems. The operation, effectiveness and performance of the proposed mechanisms are evaluated through analysis and simulations under various working conditions. The obtained numerical results indicate the benefits of the proposed schemes in terms of overhead performance and their scaling properties with respect to more realistic and thus demanding topologies. © 2013 Elsevier B.V. All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cheng2013diffusion,

title = {Diffusion models for information dissemination dynamics in wireless complex communication networks},

author = {Shin-Ming Cheng and Vasileios Karyotis and Pin-Yu Chen and Kwang-Cheng Chen and Symeon Papavassiliou},

year  = {2013},

date = {2013-01-01},

journal = {Journal of Complex Systems},

volume = {2013},

publisher = {Hindawi},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stai2011topology,

title = {Topology enhancements in wireless multihop networks: A top-down approach},

author = {Eleni Stai and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/TPDS.2011.250},

doi = {10.1109/TPDS.2011.250},

year  = {2012},

date = {2012-01-01},

urldate = {2011-01-01},

journal = {IEEE Transactions on Parallel and Distributed Systems},

volume = {23},

number = {7},

pages = {1344--1357},

publisher = {IEEE},

abstract = {Contemporary traffic demands call for efficient infrastructures capable of sustaining increasing volumes of social communications. In this work, we focus on improving the properties of wireless multihop networks with social features through network evolution. Specifically, we introduce a framework, based on inverse Topology Control (iTC), for distributively modifying the transmission radius of selected nodes, according to social paradigms. Distributed iTC mechanisms are proposed for exploiting evolutionary network churn in the form of edge/node modifications, without significantly impacting available resources. We employ continuum theory for analytically describing the proposed top-down approach of infusing social features in physical topologies. Through simulations, we demonstrate how these mechanisms achieve their goal of reducing the average path length, so as to make a wireless multihop network scale like a social one, while retaining its original multihop character. We study the impact of the proposed topology modifications on the operation and performance of the network with respect to the average throughput, delay, and energy consumption of the induced network. © 2012 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{manolakos2012cross,

title = {A cross-layer-based topology control framework for wireless multihop networks},

author = {Alexandros Manolakos and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1109/TVT.2012.2195340},

doi = {10.1109/TVT.2012.2195340},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

journal = {IEEE transactions on vehicular technology},

volume = {61},

number = {6},

pages = {2858--2864},

publisher = {IEEE},

abstract = {In this paper, we propose a distributed and asynchronous topology control framework for wireless multihop networks, aiming at improving the quality-of-service capabilities of such systems. The proposed mechanisms exploit cross-layer information such as physical-layer signal-to-interference-plus- noise ratio (SINR) values and medium-access-control (MAC)/network-layer connectivity to effectively allocate resources. Based on properly defined utility functions, we cast the transmission power allocation problem as a noncooperative game and then show that it belongs to the class of potential games. We obtain structural properties of the defined game and its variations and demonstrate their effectiveness. Through analysis and simulation, we explore the behavior of the proposed framework and compare it with the most relevant approach in the literature. It is shown that the proposed unifying game-theoretic framework achieves three key goals for multihop ad hoc networks, i.e., reduced transmission power, high-quality links, and connectivity maintenance. © 1967-2012 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karyotis2011comparison,

title = {Comparison of efficient random walk strategies for wireless multi-hop networks},

author = {Vasileios Karyotis and Maria Fazio and Symeon Papavassiliou and Antonio Puliafito},

url = {https://doi.org/10.1016/j.comcom.2011.01.001},

doi = {10.1016/j.comcom.2011.01.001},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

journal = {Computer Communications},

volume = {34},

number = {10},

pages = {1258--1267},

publisher = {Elsevier},

abstract = {Wireless multi-hop networks have drawn great attention from research and business communities, since they suit well diverse application scenarios, such as environmental monitoring, military support in hostile environments and emergency applications. However, this challenging communication paradigm requires solutions able to fit specific requirements in terms of resource constrains, node mobility and quality of service. Random Walks (RWs) are probabilistic approaches to perform distributed operations, such as data search and retrieval. They are effective and have relatively small overhead compared to classic schemes, such as flooding. To further improve performance of RWs, hybrid solutions may be employed. Such strategies increase system performance at the cost of additional energy consumption. In this work, we propose two novel schemes that exploit local topological information in order to increase the hybrid RW protocols performance. Through simulations, we compare hybrid protocols with a traditional RW solution, studying their performance in static and mobile scenarios. An analysis of the trade-off between the number of node revisits and energy consumption allows to identify the more fitting protocols for different application scenarios in wireless multi-hop networks. Advantages and drawbacks of different RW strategies are highlighted, along with research challenges that need to be investigated in the future. © 2011 Elsevier B.V. All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stai2011socially,

title = {A socially-driven topology improvement framework with applications in content distribution and trust management},

author = {Eleni Stai and Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1007/s13174-011-0024-0},

doi = {10.1007/s13174-011-0024-0},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

journal = {Journal of Internet Services and Applications},

volume = {2},

number = {2},

pages = {113--127},

publisher = {SpringerOpen},

abstract = {Contemporary networking infrastructures are required to be capable of adapting to the increasing trends of user demands, as well as the impairments of their operational environment. In this work, by exploiting the power varying capabilities of multihop wireless networks and inspired by social structures of the higher protocol layers, we develop a distributed and dynamic physical topology modification framework for weighted and directed multihop networks. The operational robustness and effectiveness of the proposed framework is demonstrated in two highly popular application areas, namely QoS-oriented content distribution and trust management in wireless multihop networks. We focus on the emerging trade-offs of topology modification, and through analysis and simulation, we demonstrate how social features can be used in improving the physical network topology and corresponding performance. © 2011 The Brazilian Computer Society.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karyotis2010analytic,

title = {Analytic stochastic propagation model for urban streets},

author = {VA Karyotis and CA Valagiannopoulos},

url = {https://doi.org/10.1049/iet-map.2008.0131},

doi = {10.1049/iet-map.2008.0131},

year  = {2010},

date = {2010-01-01},

urldate = {2010-01-01},

journal = {IET microwaves, antennas & propagation},

volume = {4},

number = {1},

pages = {91--98},

publisher = {IET Digital Library},

abstract = {Stochastic ray propagation is a method to retain analytic description in topologies where application of Maxwell's equations would be ineffective or impossible. In this paper, a probabilistic model describing ray propagation along an urban street is analysed and discussed. The proposed model bears the simplicity of an analytic approach, and even though not fully descriptive of all propagation mechanisms, it provides informative results of wireless propagation in the event of a first-stage analysis. Based on the presented probabilistic formulation, explicit expressions of the power across and at the end of the street are obtained and used to evaluate the behaviour of the considered structure. We confirm our analysis through simulation, by computing the distribution of ray paths along the street. We identify the probabilistic propagation mechanism as the dominant factor for the received power at the end of the street for a travelling wave antenna. By considering multiple distributed point sources across the beginning of the street, the coincidence between the numerical average of the received power and the expected value of the corresponding random variable is exhibited. The proposed formulation can be further used as a basis for analysing similar problems, yielding useful and indicative results. © 2010 The Institution of Engineering and Technology.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karyotis2010markov,

title = {Markov random fields for malware propagation: The case of chain networks},

author = {Vasileios Karyotis},

url = {https://doi.org/10.1109/LCOMM.2010.072910.100866},

doi = {10.1109/LCOMM.2010.072910.100866},

year  = {2010},

date = {2010-01-01},

urldate = {2010-01-01},

journal = {IEEE communications letters},

volume = {14},

number = {9},

pages = {875--877},

publisher = {IEEE},

abstract = {Epidemic and stochastic models have been employed for describing the dynamic behavior of malware outbreaks. However, most of them lack a holistic treatment of the problem. In this work, we model malware propagation as a Markov Random Field and employ Gibbs sampling for the analysis of the system. We demonstrate the proposed framework for the case of a chain network, a model often emerging in both wired and wireless multi-hop networks. © 2010 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karyotis2008malware,

title = {Malware-propagative mobile ad hoc networks: asymptotic behavior analysis},

author = {Vasileios Karyotis and Anastasios Kakalis and Symeon Papavassiliou},

url = {https://doi.org/10.1007/s11390-008-9141-z},

doi = {10.1007/s11390-008-9141-z},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

journal = {Journal of Computer Science and Technology},

volume = {23},

number = {3},

pages = {389--399},

publisher = {Springer US},

abstract = {In this paper, the spreading of malicious software over ad hoc networks, where legitimate nodes are prone to propagate the infections they receive from either an attacker or their already infected neighbors, is analyzed. Considering the Susceptible-Infected-Susceptible (SIS) node infection paradigm we propose a probabilistic model, on the basis of the theory of closed queuing networks, that aims at describing the aggregated behavior of the system when attacked by malicious nodes. Because of its nature, the model is also able to deal more effectively with the stochastic behavior of attackers and the inherent probabilistic nature of the wireless environment. The proposed model is able to describe accurately the asymptotic behavior of malware-propagative large scale ad hoc networking environments. Using the Norton equivalent of the closed queuing network, we obtain analytical results for its steady state behavior, which in turn is used for identifying the critical parameters affecting the operation of the network. Finally, through modeling and simulation, some additional numerical results are obtained with respect to the behavior of the system when multiple attackers are present, and regarding the time-dependent evolution and impact of an attack. © 2008 Science Press, Beijing, China and Springer Science + Business Media, LLC, USA.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{valagiannopoulos2008stochastic,

title = {Stochastic ray propagation for two parallel urban streets: Exact and approximate results},

author = {Constantine A Valagiannopoulos and Vasileios A Karyotis},

url = {https://doi.org/10.1109/LAWP.2008.2000856},

doi = {10.1109/LAWP.2008.2000856},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

journal = {IEEE Antennas and Wireless Propagation Letters},

volume = {7},

pages = {381--384},

publisher = {IEEE},

abstract = {In this letter, an analytic stochastic model for the propagation of plane waves in two parallel urban streets is developed. The received power at the end of the topology is obtained and three solution cases of significant and practical interest are identified and studied in detail. In addition, a linear approximation method is proposed for the same problem. Numerical results are provided to analyze the nature of the propagation with respect to the parameters, symmetry of the topology and the applicability of the approximation method. The proposed analytic model is simple, yet it can capture the basic mechanism of propagation and can be used as an early indication before the application of large-scale methods in practical scenarios. © 2008 IEEE.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karyotis2007risk,

title = {Risk-based attack strategies for mobile ad hoc networks under probabilistic attack modeling framework},

author = {Vasileios Karyotis and Symeon Papavassiliou},

url = {https://doi.org/10.1016/j.comnet.2007.01.028},

doi = {10.1016/j.comnet.2007.01.028},

year  = {2007},

date = {2007-01-01},

urldate = {2007-01-01},

journal = {Computer Networks},

volume = {51},

number = {9},

pages = {2397--2410},

publisher = {Elsevier},

abstract = {In this paper, we introduce and design a modeling framework that allows for the study and analysis of attack propagation in mobile ad hoc networks. The choice of a statistical approach for the problem is motivated by the dynamic characteristics of the ad hoc topology and the stochastic nature of threat propagation. Based on this probabilistic modeling framework, we study the impact of topology and mobility in the propagation of software threats over ad hoc networks. We design topology control algorithms that indicate how to properly adjust an attacker's transmission radius, according to the measured topological characteristics and availability of its resources, in the process of infecting a network more effectively. Then based on these topology control algorithms we develop different attack strategies that may range from independent attacks to cooperative scenarios in order to increase the negative impact of an attack on the network. Our performance evaluation results demonstrate that the proposed topology control algorithms and respective attack strategies effectively balance the tradeoffs between the potential network damage and the attackers' lifetime, and as a result significantly outperform any other flat and threshold-based approaches. © 2007 Elsevier B.V. All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{karyotis2006novel,

title = {A novel framework for mobile attack strategy modelling and vulnerability analysis in wireless ad hoc networks},

author = {Vasileios Karyotis and Symeon Papavassiliou and Mary Grammatikou and Vasilis Maglaris},

url = {https://doi.org/10.1504/ijsn.2006.011785},

doi = {10.1504/ijsn.2006.011785},

year  = {2006},

date = {2006-01-01},

urldate = {2006-01-01},

journal = {International Journal of Security and Networks},

volume = {1},

number = {3-4},

pages = {255--265},

publisher = {Inderscience Publishers},

abstract = {Global dissemination of information and tools for computer networks, has allowed for major system attacks affecting critical network points and resulting in significant network performance degradation. In this paper, we present a probabilistic modelling framework for the propagation of an energy-constrained mobile threat in a wireless ad hoc network. The motivation behind this approach can be found in the topology-constrained character of the ad hoc setting, its dynamic nature and the stochastic characteristics of the interactions among the involved events. The introduced formulation is used to identify and evaluate different attack strategies and approaches. Through modelling and simulation, we evaluate the impact of various parameters associated with the operational characteristics of the mobile attacker on an outbreak spreading and the network evolution. Furthermore, a new metric, which indicates the overall infection-capability of each attack strategy is proposed and used to characterise the potential of each strategy to harm the network. © 2006 Inderscience Enterprises Ltd.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}