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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.
The agriculture sector has held a major role in human societies across the planet throughout history. The rapid evolution in Information and Communication Technologies (ICT) strongly affects the structure and the procedures of modern agriculture. Despite the advantages gained from this evolution, there are several existing as well as emerging security threats that can severely impact the agricultural domain. The present paper provides an overview of the main existing and potential threats for agriculture. Initially, the paper presents an overview of the evolution of ICT solutions and how these may be utilized and affect the agriculture sector. It then conducts an extensive literature review on the use of ICT in agriculture, as well as on the associated emerging threats and vulnerabilities. The authors highlight the main ICT innovations, techniques, benefits, threats and mitigation measures by studying the literature on them and by providing a concise discussion on the possible impacts these could have on the agri-sector.
Konstantinos Demestichas; Nikolaos Peppes; Theodoros Alexakis. Survey on Security Threats in Agricultural IoT and Smart Farming. Sensors 2020, 20, 6458 .
AMA StyleKonstantinos Demestichas, Nikolaos Peppes, Theodoros Alexakis. Survey on Security Threats in Agricultural IoT and Smart Farming. Sensors. 2020; 20 (22):6458.
Chicago/Turabian StyleKonstantinos Demestichas; Nikolaos Peppes; Theodoros Alexakis. 2020. "Survey on Security Threats in Agricultural IoT and Smart Farming." Sensors 20, no. 22: 6458.
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.