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In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO2 emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle’s fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers’ behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver’s behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset.
Nikolaos Peppes; Theodoros Alexakis; Evgenia Adamopoulou; Konstantinos Demestichas. Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data. Sensors 2021, 21, 4704 .
AMA StyleNikolaos Peppes, Theodoros Alexakis, Evgenia Adamopoulou, Konstantinos Demestichas. Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data. Sensors. 2021; 21 (14):4704.
Chicago/Turabian StyleNikolaos Peppes; Theodoros Alexakis; Evgenia Adamopoulou; Konstantinos Demestichas. 2021. "Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data." Sensors 21, no. 14: 4704.
The rapid growth of demand for transportation, both for people and goods, as well as the massive accumulation of population in urban centers has augmented the need for the development of smart transport systems. One of the needs that have arisen is to efficiently monitor and evaluate driving behavior, so as to increase safety, provide alarms, and avoid accidents. Capitalizing on the evolution of Information and Communication Technologies (ICT), the development of intelligent vehicles and platforms in this domain is getting more feasible than ever. Nowadays, vehicles, as well as highways, are equipped with sensors that collect a variety of data, such as speed, acceleration, fuel consumption, direction, and more. The methodology presented in this paper combines both advanced machine learning algorithms and open-source based tools to correlate different data flows originating from vehicles. Particularly, the data gathered from different vehicles are processed and analyzed with the utilization of machine learning techniques in order to detect abnormalities in driving behavior. Results from different suitable techniques are presented and compared, using an extensive real-world dataset containing field measurements. The results feature the application of both supervised univariate anomaly detection and unsupervised multivariate anomaly detection methods in the same dataset.
Konstantinos Demestichas; Theodoros Alexakis; Nikolaos Peppes; Evgenia Adamopoulou. Comparative Analysis of Machine Learning-Based Approaches for Anomaly Detection in Vehicular Data. Vehicles 2021, 3, 171 -186.
AMA StyleKonstantinos Demestichas, Theodoros Alexakis, Nikolaos Peppes, Evgenia Adamopoulou. Comparative Analysis of Machine Learning-Based Approaches for Anomaly Detection in Vehicular Data. Vehicles. 2021; 3 (2):171-186.
Chicago/Turabian StyleKonstantinos Demestichas; Theodoros Alexakis; Nikolaos Peppes; Evgenia Adamopoulou. 2021. "Comparative Analysis of Machine Learning-Based Approaches for Anomaly Detection in Vehicular Data." Vehicles 3, no. 2: 171-186.
The ever-increasing demand for transportation of people and goods as well as the massive accumulation of population in urban centers have increased the need for appropriate infrastructure and system development in order to efficiently manage the constantly increasing and diverse traffic flows. Moreover, given the rapid growth and the evolution of Information and Communication Technologies (ICT), the development of intelligent traffic management systems that go beyond traditional approaches is now more feasible than ever. Nowadays, highways often have sensors installed across their range that collect data such as speed, density, direction and so on. In addition, the rapid evolution of vehicles with installed computer systems and sensors on board, provides a very large amount of data, ranging from very simple features such as speed, acceleration, etc. to very complex data like the driver’s situation and driving behavior. However, these data alone and without any further processing, cannot solve the congestion problem. Therefore, the development of complex computational methods and algorithms underpins the chance to process these data in a fast and reliable way. The purpose of this paper is to present a traffic control ramp metering (RM) method based on machine learning and to study its impact on a selected highway segment.
Theodoros Alexakis; Nikolaos Peppes; Evgenia Adamopoulou; Konstantinos Demestichas. An Artificial Intelligence-Based Approach for the Controlled Access Ramp Metering Problem. Vehicles 2021, 3, 63 -83.
AMA StyleTheodoros Alexakis, Nikolaos Peppes, Evgenia Adamopoulou, Konstantinos Demestichas. An Artificial Intelligence-Based Approach for the Controlled Access Ramp Metering Problem. Vehicles. 2021; 3 (1):63-83.
Chicago/Turabian StyleTheodoros Alexakis; Nikolaos Peppes; Evgenia Adamopoulou; Konstantinos Demestichas. 2021. "An Artificial Intelligence-Based Approach for the Controlled Access Ramp Metering Problem." Vehicles 3, no. 1: 63-83.
Nowadays, (cyber)criminals demonstrate an ever-increasing resolve to exploit new technologies so as to achieve their unlawful purposes. Therefore, Law Enforcement Agencies (LEAs) should keep one step ahead by engaging tools and technology that address existing challenges and enhance policing and crime prevention practices. The framework presented in this paper combines algorithms and tools that are used to correlate different pieces of data leading to the discovery and recording of forensic evidence. The collected data are, then, combined to handle inconsistencies, whereas machine learning techniques are applied to detect trends and outliers. In this light, the authors of this paper present, in detail, an innovative Abnormal Behavior Detection Engine, which also encompasses a knowledge base visualization functionality focusing on financial transactions investigation.
Konstantinos Demestichas; Nikolaos Peppes; Theodoros Alexakis; Evgenia Adamopoulou. An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions. Information 2021, 12, 34 .
AMA StyleKonstantinos Demestichas, Nikolaos Peppes, Theodoros Alexakis, Evgenia Adamopoulou. An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions. Information. 2021; 12 (1):34.
Chicago/Turabian StyleKonstantinos Demestichas; Nikolaos Peppes; Theodoros Alexakis; Evgenia Adamopoulou. 2021. "An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions." Information 12, no. 1: 34.
Often in the area of road transport solutions and intelligent transport systems, two or more alternative solutions or methods compete in terms of energy gains, time efficiency, or other aspects. Measurements collected from field trials are used to make a comparative assessment but are usually limited because of resource constraints. The present paper describes how statistical inference techniques can be used in a systematic way, in order to validate the superior performance of one method over the other. We adopt such an approach to study the performance of two alternative routing methods in terms of achievable energy savings, although the same methodology can be widely applied to other use cases as well. We specifically employ and describe three different techniques to achieve the intended comparison, namely paired sample tests, statistical testing of mean value in a normal population, and two-sample tests in normal populations with unknown yet equal variances. We reach conclusions on whether claims of outperformance of one routing method over the other can be supported by our collected experimental data and to what extent.
Konstantinos Demestichas; Evgenia Adamopoulou. Statistical Validation of Energy Efficiency Improvements through Analysis of Experimental Field Data: A Guide to Good Practice. Vehicles 2020, 2, 542 -558.
AMA StyleKonstantinos Demestichas, Evgenia Adamopoulou. Statistical Validation of Energy Efficiency Improvements through Analysis of Experimental Field Data: A Guide to Good Practice. Vehicles. 2020; 2 (3):542-558.
Chicago/Turabian StyleKonstantinos Demestichas; Evgenia Adamopoulou. 2020. "Statistical Validation of Energy Efficiency Improvements through Analysis of Experimental Field Data: A Guide to Good Practice." Vehicles 2, no. 3: 542-558.
Food holds a major role in human beings’ lives and in human societies in general across the planet. The food and agriculture sector is considered to be a major employer at a worldwide level. The large number and heterogeneity of the stakeholders involved from different sectors, such as farmers, distributers, retailers, consumers, etc., renders the agricultural supply chain management as one of the most complex and challenging tasks. It is the same vast complexity of the agriproducts supply chain that limits the development of global and efficient transparency and traceability solutions. The present paper provides an overview of the application of blockchain technologies for enabling traceability in the agri-food domain. Initially, the paper presents definitions, levels of adoption, tools and advantages of traceability, accompanied with a brief overview of the functionality and advantages of blockchain technology. It then conducts an extensive literature review on the integration of blockchain into traceability systems. It proceeds with discussing relevant existing commercial applications, highlighting the relevant challenges and future prospects of the application of blockchain technologies in the agri-food supply chain.
Konstantinos Demestichas; Nikolaos Peppes; Theodoros Alexakis; Evgenia Adamopoulou. Blockchain in Agriculture Traceability Systems: A Review. Applied Sciences 2020, 10, 4113 .
AMA StyleKonstantinos Demestichas, Nikolaos Peppes, Theodoros Alexakis, Evgenia Adamopoulou. Blockchain in Agriculture Traceability Systems: A Review. Applied Sciences. 2020; 10 (12):4113.
Chicago/Turabian StyleKonstantinos Demestichas; Nikolaos Peppes; Theodoros Alexakis; Evgenia Adamopoulou. 2020. "Blockchain in Agriculture Traceability Systems: A Review." Applied Sciences 10, no. 12: 4113.
The emerging era of cloud services has already started and the concepts of Internet of Things (IoT) are ready to be introduced in the modern everyday life through the deployment of a large scope of novel applications. In this paper we present the concepts and ideas of the INLIFE project. The software produced by using the INLIFE platform can be seen as a hybrid of a typical e-health application and a typical recreational electronic game and this is one important differentiation of the INLIFE games and apps. The users play an attractive game like e.g. a strategy campaign or an MMORPG (Massively Multiplayer Online Role Playing Game) but the only way to collect the points or the resources they need in order to play, is to do something in their real life, namely "inlife". These valuable actions, that credit points to the players, are detected in an automatic way by using IoT sensors and actuators or by retrieving information from other monitoring systems like ADL (Activities of Daily Living) data.
Nikos Koutsouris; Pavlos Kosmides; Konstantinos Demestichas; Evgenia Adamopoulou; Katerina Giannakopoulou; Vanessa De Luca. InLife: a platform enabling the exploitation of IoT and gamification in healthcare. 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) 2018, 224 -230.
AMA StyleNikos Koutsouris, Pavlos Kosmides, Konstantinos Demestichas, Evgenia Adamopoulou, Katerina Giannakopoulou, Vanessa De Luca. InLife: a platform enabling the exploitation of IoT and gamification in healthcare. 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). 2018; ():224-230.
Chicago/Turabian StyleNikos Koutsouris; Pavlos Kosmides; Konstantinos Demestichas; Evgenia Adamopoulou; Katerina Giannakopoulou; Vanessa De Luca. 2018. "InLife: a platform enabling the exploitation of IoT and gamification in healthcare." 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) , no. : 224-230.
Amid the most recent years, gamification has gotten expanding consideration focusing on an assortment of individuals including children, students, youngsters and employers. Likewise, great advances have been also seen in the Internet-of-Things (IoT) triggering various researchers' interest. In this paper, the core integration architecture of the InLife ecosystem that combines IoT with Serious Games is presented, as well as the user portal that can be used from third-party developers to create their own Serious Games. Specifically, the proposed platform focuses on an innovative gamification framework targeting both typical as well as special education and social inclusion activities based on Serious Games. The core concept leverages on the potential of the IoT paradigm to link closely actions, decisions and events happening in real-life with in-game educational progress and modern gaming technologies. This bridge strengthens the infusion of gamification into non-leisure contexts, boosting at the same time the creation of new educational methodologies as well as new business opportunities.
Pavlos Kosmides; Konstantinos Demestichas; Evgenia Adamopoulou; Nikos Koutsouris; Ioannis Loumiotis; Victor Ortega; Lorenzo Mureddu. InLife Ecosystem: Creating Serious Games with IoT Features. 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) 2018, 299 -304.
AMA StylePavlos Kosmides, Konstantinos Demestichas, Evgenia Adamopoulou, Nikos Koutsouris, Ioannis Loumiotis, Victor Ortega, Lorenzo Mureddu. InLife Ecosystem: Creating Serious Games with IoT Features. 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). 2018; ():299-304.
Chicago/Turabian StylePavlos Kosmides; Konstantinos Demestichas; Evgenia Adamopoulou; Nikos Koutsouris; Ioannis Loumiotis; Victor Ortega; Lorenzo Mureddu. 2018. "InLife Ecosystem: Creating Serious Games with IoT Features." 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) , no. : 299-304.
Ioannis Loumiotis; Konstantinos Demestichas; Evgenia Adamopoulou; Pavlos Kosmides; Vasilis Asthenopoulos; Efstathios Sykas. Road Traffic Prediction Using Artificial Neural Networks. 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference (SEEDA_CECNSM) 2018, 1 .
AMA StyleIoannis Loumiotis, Konstantinos Demestichas, Evgenia Adamopoulou, Pavlos Kosmides, Vasilis Asthenopoulos, Efstathios Sykas. Road Traffic Prediction Using Artificial Neural Networks. 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference (SEEDA_CECNSM). 2018; ():1.
Chicago/Turabian StyleIoannis Loumiotis; Konstantinos Demestichas; Evgenia Adamopoulou; Pavlos Kosmides; Vasilis Asthenopoulos; Efstathios Sykas. 2018. "Road Traffic Prediction Using Artificial Neural Networks." 2018 South-Eastern European Design Automation, Computer Engineering, Computer Networks and Society Media Conference (SEEDA_CECNSM) , no. : 1.
During the last 10 years, gamification has received increasing attention targeting a variety of people including children, students, youngsters and employers. In addition, great progress has been also observed in the Internet-of-Things (IoT) triggering various researchers' interest. In this paper, we present the core integration architecture and a serious game use case that are both implemented by the InLife project to drive new learning scenarios. InLife is European funded project that focuses on an innovative gamification framework targeting both typical as well as special education and social inclusion activities based on Serious Games. The core concept leverages on the potential of the IoT paradigm to link closely actions, decisions and events happening in real-life with in-game educational progress and modern gaming technologies. This bridge strengthens the infusion of gamification into non-leisure contexts, boosting at the same time the creation of new educational methodologies as well as new business opportunities.
Pavlos Kosmides; Konstantinos Demestichas; Evgenia Adamopoulou; Nikos Koutsouris; Yannis Oikonomidis; Vanessa De Luca. InLife: Combining Real Life with Serious Games using IoT. 2018 IEEE Conference on Computational Intelligence and Games (CIG) 2018, 1 -7.
AMA StylePavlos Kosmides, Konstantinos Demestichas, Evgenia Adamopoulou, Nikos Koutsouris, Yannis Oikonomidis, Vanessa De Luca. InLife: Combining Real Life with Serious Games using IoT. 2018 IEEE Conference on Computational Intelligence and Games (CIG). 2018; ():1-7.
Chicago/Turabian StylePavlos Kosmides; Konstantinos Demestichas; Evgenia Adamopoulou; Nikos Koutsouris; Yannis Oikonomidis; Vanessa De Luca. 2018. "InLife: Combining Real Life with Serious Games using IoT." 2018 IEEE Conference on Computational Intelligence and Games (CIG) , no. : 1-7.
Nowadays, an ever-increasing number of information and communication technology solutions (hardware or software based) are finding their way to the automotive sector. Vehicles are being transformed into electronic hubs of information, communication, entertainment and other applications. Prior to commercial deployment, every single of these solutions must undergo a scrutiny of technical tests, often in the field (i.e. on-road as opposed to simulation), in order to ensure safe operation and robust performance. ‘Robustness’ is here perceived as operating as close to the target specifications as possible and with minimum variance, under varying conditions (factors). Meeting this requirement given a limited amount of resources (human, financial, equipment etc.) available for on-road technical tests is often a serious challenge for both researchers and product developers. This study proposes an experimental design process, based on suitable statistical means, for minimising the number of technical tests required to optimise the performance robustness of an automotive service or product under development. The process is substantiated and exemplified for the case study of an electric vehicle consumption estimation product, but could also be used in a variety of other applications (such as navigation, infotainment, safety solutions and others).
Konstantinos Demestichas; Evgenia Adamopoulou; Vasilis Asthenopoulos; Pavlos Kosmides. Robust and cost‐efficient experimental design for technical tests of information and communication technology‐based solutions in the automotive sector. IET Intelligent Transport Systems 2017, 11, 368 -378.
AMA StyleKonstantinos Demestichas, Evgenia Adamopoulou, Vasilis Asthenopoulos, Pavlos Kosmides. Robust and cost‐efficient experimental design for technical tests of information and communication technology‐based solutions in the automotive sector. IET Intelligent Transport Systems. 2017; 11 (7):368-378.
Chicago/Turabian StyleKonstantinos Demestichas; Evgenia Adamopoulou; Vasilis Asthenopoulos; Pavlos Kosmides. 2017. "Robust and cost‐efficient experimental design for technical tests of information and communication technology‐based solutions in the automotive sector." IET Intelligent Transport Systems 11, no. 7: 368-378.
Konstantinos Demestichas; Evgenia Adamopoulou; Michał Choraś. 5G Communications: Energy Efficiency. Mobile Information Systems 2017, 2017, 1 -3.
AMA StyleKonstantinos Demestichas, Evgenia Adamopoulou, Michał Choraś. 5G Communications: Energy Efficiency. Mobile Information Systems. 2017; 2017 ():1-3.
Chicago/Turabian StyleKonstantinos Demestichas; Evgenia Adamopoulou; Michał Choraś. 2017. "5G Communications: Energy Efficiency." Mobile Information Systems 2017, no. : 1-3.
The market uptake of the 4th Generation networks is expected to support the increasing demand for wireless broadband services and ensure an enhanced mobile user experience. In this direction, the convergence of a wireless access network with an optical backhauling has been proposed. However, in such a converged architecture, the traditional fixed commitment of the backhaul resources does not prove to be as efficient, and novel dynamic schemes are required that consider both the needs of the base stations and the limitations of the passive optical network. This paper is concerned with the topic of resource allocation in two competing base stations that belong to different operators and share a common optical backhaul network infrastructure. An approach based on evolutionary game theory is proposed and employed, with a view to examining the interactions among the base stations and the passive optical network. Using the model of replicator dynamics, the proposed system design is proved to be asymptotically stable. In addition, this paper studies and reveals the extent to which time delay can have an impact on the proposed system design.
Ioannis V. Loumiotis; Pavlos Kosmides; Evgenia Adamopoulou; Konstantinos Demestichas; Michael E. Theologou. Dynamic Allocation of Backhaul Resources in Converged Wireless-Optical Networks. IEEE Journal on Selected Areas in Communications 2017, 35, 280 -287.
AMA StyleIoannis V. Loumiotis, Pavlos Kosmides, Evgenia Adamopoulou, Konstantinos Demestichas, Michael E. Theologou. Dynamic Allocation of Backhaul Resources in Converged Wireless-Optical Networks. IEEE Journal on Selected Areas in Communications. 2017; 35 (2):280-287.
Chicago/Turabian StyleIoannis V. Loumiotis; Pavlos Kosmides; Evgenia Adamopoulou; Konstantinos Demestichas; Michael E. Theologou. 2017. "Dynamic Allocation of Backhaul Resources in Converged Wireless-Optical Networks." IEEE Journal on Selected Areas in Communications 35, no. 2: 280-287.
One of the most significant issues the research community has focused on during the last decades, is the reduction of the energy consumed in every aspect of everyday life. A standout amongst the most important factors of energy consumption is transportation. To this end, a lot of work in the field of Intelligent Transport Systems concentrates on enhancing energy efficiency. This trend was reinforced by the appearance of Fully Electric Vehicles (FEVs), where it is more crucial to increase their energy efficiency in any manner. Eco-routing refers to the choice of the most energy efficient route towards a destination and seems very promising for reducing everyday energy consumption. In this paper, we present a novel method for predicting energy consumption levels, based on machine learning techniques. In addition, addressing the problem of ever increasing amounts of tracking data acquired from vehicles, we introduce a clustering based prediction method and apply it on real world measurements in order to evaluate its performance.
Pavlos Kosmides; Lambros Lambrinos; Vasilis Asthenopoulos; Konstantinos Demestichas; Evgenia Adamopoulou. A clustering based approach for energy efficient routing. 2016 IEEE Symposium on Computers and Communication (ISCC) 2016, 232 -237.
AMA StylePavlos Kosmides, Lambros Lambrinos, Vasilis Asthenopoulos, Konstantinos Demestichas, Evgenia Adamopoulou. A clustering based approach for energy efficient routing. 2016 IEEE Symposium on Computers and Communication (ISCC). 2016; ():232-237.
Chicago/Turabian StylePavlos Kosmides; Lambros Lambrinos; Vasilis Asthenopoulos; Konstantinos Demestichas; Evgenia Adamopoulou. 2016. "A clustering based approach for energy efficient routing." 2016 IEEE Symposium on Computers and Communication (ISCC) , no. : 232-237.
During the last decade, in parallel with the rapid growth of mobile communications and devices, location-based social networks have met a tremendous growth with the acceptance of the public being constantly increasing. Users have access to a plethora of venues and points of interest, while they are able to share their visits to various locations along with comments and ratings about their experience (a process which is often referred to as “check-ins”). Location recommendations based on users’ needs have been a subject of interest for many researchers, while location prediction schemes have been developed in order to provide user’s possible future locations. In this paper, we present a novel method for predicting a user’s location based on machine learning techniques. In addition, following the incremental trend towards data accumulation in social networks, we introduce a clustering based prediction method in order to enhance the recommender system. For the prediction process we propose a probabilistic neural network and confirm its superior performance against two other types of neural networks, while for the clustering process we use a K-means clustering algorithm. The dataset we used was based on input from a well-known location-based social network. Prediction results can be used in order to make appropriate suggestions for venues or points of interests to users, based on their interests and social connections.
Pavlos Kosmides; Konstantinos Demestichas; Evgenia Adamopoulou; Chara Remoundou; Ioannis Loumiotis; Michael Theologou; Miltiades Anagnostou. Providing recommendations on location-based social networks. Journal of Ambient Intelligence and Humanized Computing 2016, 7, 567 -578.
AMA StylePavlos Kosmides, Konstantinos Demestichas, Evgenia Adamopoulou, Chara Remoundou, Ioannis Loumiotis, Michael Theologou, Miltiades Anagnostou. Providing recommendations on location-based social networks. Journal of Ambient Intelligence and Humanized Computing. 2016; 7 (4):567-578.
Chicago/Turabian StylePavlos Kosmides; Konstantinos Demestichas; Evgenia Adamopoulou; Chara Remoundou; Ioannis Loumiotis; Michael Theologou; Miltiades Anagnostou. 2016. "Providing recommendations on location-based social networks." Journal of Ambient Intelligence and Humanized Computing 7, no. 4: 567-578.
One of the major challenges that mobile operators (MOs) are faced with nowadays is the transition to 4th Generation (4G) mobile communication technologies. The main reason for this lies on the reluctance of MOs to invest in a new technology without being sure about its success. The current paper investigates the decision-making procedures of a MO that wishes to migrate from its current technology type to 4G. Traditionally, the decision of deploying a new technology has been based on the analysis of similar implementations in other countries. However, such approaches can be inefficient and time consuming, as there are discrepancies concerning the technological progress among different countries. To this end, the authors employ evolutionary game theory to model the interactions of the MO’s decisions and the subscribers’ needs, and propose a practical and efficient qualitative model that identifies the circumstances under which the transition towards 4G networking can be facilitated. Specifically, the mathematical foundation of the decision making process is provided and the key role of the charging price and the quality of experience by the subscribers for using 4G connectivity is proven. With the process of 4G deployment still ongoing, this paper aims to present an analysis that can be used supplementary to the decision process of a MO that aims to evolve his network.
Ioannis Loumiotis; Evgenia Adamopoulou; Konstantinos Demestichas; Chara Remoundou; Pavlos Kosmides; Vasileios Asthenopoulos; Michael Theologou. Road to Next Generation Mobile Networks: An Evolutionary Dynamics Approach. Mobile Networks and Applications 2015, 21, 237 -246.
AMA StyleIoannis Loumiotis, Evgenia Adamopoulou, Konstantinos Demestichas, Chara Remoundou, Pavlos Kosmides, Vasileios Asthenopoulos, Michael Theologou. Road to Next Generation Mobile Networks: An Evolutionary Dynamics Approach. Mobile Networks and Applications. 2015; 21 (2):237-246.
Chicago/Turabian StyleIoannis Loumiotis; Evgenia Adamopoulou; Konstantinos Demestichas; Chara Remoundou; Pavlos Kosmides; Vasileios Asthenopoulos; Michael Theologou. 2015. "Road to Next Generation Mobile Networks: An Evolutionary Dynamics Approach." Mobile Networks and Applications 21, no. 2: 237-246.
During the last years, Social Networks have been in the spotlight of many researchers, trying to enhance them with pervasive features that will simplify and facilitate users’ experience. One of the most innovative additions to social networks has been the introduction of communities in users’ lifecycle. However, there are still a lot of issues regarding the automation of this feature in order to minimize user’s effort to discover new communities and as a result, to improve his experience. In this paper, we introduce the use of communities in location-based social networks. We also present the proposed systems architecture including Processes and Services.
Pavlos Kosmides; Chara Remoundou; Ioannis Loumiotis; Evgenia Adamopoulou; Konstantinos Demestichas. Introducing Community Awareness to Location-Based Social Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015, 125 -130.
AMA StylePavlos Kosmides, Chara Remoundou, Ioannis Loumiotis, Evgenia Adamopoulou, Konstantinos Demestichas. Introducing Community Awareness to Location-Based Social Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2015; ():125-130.
Chicago/Turabian StylePavlos Kosmides; Chara Remoundou; Ioannis Loumiotis; Evgenia Adamopoulou; Konstantinos Demestichas. 2015. "Introducing Community Awareness to Location-Based Social Networks." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 125-130.
The introduction of the new 4G technologies promises to satisfy the increasing demands of the end-users for bandwidth consuming applications. However, the high data rates provided by 4G networks at the air interface raise the need for more efficient management of the backhaul resources. In the current work, the authors study the problem of the efficient management of the backhaul resources at the side of the base station. Specifically, a novel scheme is proposed that, initially, predicts the forthcoming demand using artificial neural networks and, then, based on the prediction results, it proactively requests the commitment of the appropriate resources using linear optimisation techniques. The experimental results show that the proposed scheme can efficiently and cost-effectively manage the backhaul resources, outperforming the traditional flat commitment approaches.
Ioannis Loumiotis; Evgenia Adamopoulou; Konstantinos Demestichas; Michael Theologou. Optimal Backhaul Resource Management in Wireless-Optical Converged Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015, 254 -261.
AMA StyleIoannis Loumiotis, Evgenia Adamopoulou, Konstantinos Demestichas, Michael Theologou. Optimal Backhaul Resource Management in Wireless-Optical Converged Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2015; ():254-261.
Chicago/Turabian StyleIoannis Loumiotis; Evgenia Adamopoulou; Konstantinos Demestichas; Michael Theologou. 2015. "Optimal Backhaul Resource Management in Wireless-Optical Converged Networks." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 254-261.
The development of smart cities has been the epicentre of many researchers’ efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities. One common solution proposed in the recent literature is the use of software defined networks (SDNs), in order to enhance interoperability among the various heterogeneous wireless networks. In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure. In this paper, we propose an architecture combining heterogeneous wireless networks with social networks using SDNs. Specifically, we exploit the information retrieved from location based social networks regarding users’ locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques. By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation.
Pavlos Kosmides; Evgenia Adamopoulou; Konstantinos Demestichas; Michael Theologou; Miltiades Anagnostou; Angelos Rouskas. Socially Aware Heterogeneous Wireless Networks. Sensors 2015, 15, 13705 -13724.
AMA StylePavlos Kosmides, Evgenia Adamopoulou, Konstantinos Demestichas, Michael Theologou, Miltiades Anagnostou, Angelos Rouskas. Socially Aware Heterogeneous Wireless Networks. Sensors. 2015; 15 (6):13705-13724.
Chicago/Turabian StylePavlos Kosmides; Evgenia Adamopoulou; Konstantinos Demestichas; Michael Theologou; Miltiades Anagnostou; Angelos Rouskas. 2015. "Socially Aware Heterogeneous Wireless Networks." Sensors 15, no. 6: 13705-13724.
Ioannis Loumiotis; V. Asthenopoulos; Evgenia Adamopoulou; Konstantinos Demestichas; E. Sykas. Intelligent and Efficient Car Management Application for Advanced Green Routing. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015, 135 -140.
AMA StyleIoannis Loumiotis, V. Asthenopoulos, Evgenia Adamopoulou, Konstantinos Demestichas, E. Sykas. Intelligent and Efficient Car Management Application for Advanced Green Routing. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2015; ():135-140.
Chicago/Turabian StyleIoannis Loumiotis; V. Asthenopoulos; Evgenia Adamopoulou; Konstantinos Demestichas; E. Sykas. 2015. "Intelligent and Efficient Car Management Application for Advanced Green Routing." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 135-140.