Publications

@article{nokey,

title = {Game Theory Algorithms for Resource Allocation in 5G MIMO},

author = {Konstantinos Tsachrelias and Chrysostomos-Athanasios Katsigiannis and Vasileios Kokkinos and Apostolos Gkamas and Christos Bouras and Philippos Pouyioutas },

url = { https://telematics.upatras.gr/ijim_56051/},

year  = {2025},

date = {2025-07-08},

journal = {International Journal of Interactive Mobile Technologies},

volume = {19},

number = {13},

abstract = {Efficient resource allocation is essential in 5G MIMO networks due to increasing demands for high-quality communications. This paper compares four game theory algorithms: Stackelberg, Nash Bargaining, Mean Field Game, and Potential Game, evaluating their effectiveness in allocating resources dynamically. A simulation environment is developed to represent realistic user mobility by continuously updating user equipment (UE) positions. Each algorithm is assessed based on UE distribution, fairness, bandwidth consumption, and energy efficiency. The simulation results show clear differences among the algorithms, highlighting specific advantages and limitations that help inform resource allocation strategies in practical 5G network scenarios.},

note = {to appear},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Performance Evaluation of Downlink – Uplink Decoupling in 5G Multiple Input Multiple Output Networks},

author = {Konstantinos Tsachrelias and Chrysostomos-Athanasios Katsigiannis and Vasileios Kokkinos and Apostolos Gkamas and Christos Bouras and Philippos Pouyioutas },

url = {https://telematics.upatras.gr/d-ijim_journal_final/},

year  = {2025},

date = {2025-07-08},

urldate = {2025-07-08},

journal = {International Journal of Interactive Mobile Technologies},

abstract = {In the landscape of 5G networks, efficient resource allocation stands as a critical factor in meeting the diverse demands of applications and users. This paper delves into optimizing resource allocation within 5G Multiple Input Multiple Output (MIMO) networks by leveraging Downlink/Uplink Decoupling (DUDe) techniques. MIMO technology, enabling the simultaneous transmission of multiple data streams, holds promise for boosting spectral efficiency. However, accommodating the dynamic and diverse user requirements poses a significant challenge in resource allocation. By employing advanced DUDe techniques, this research dynamically allocates resources in 5G MIMO Heterogeneous 	Networks (HetNets), seeking to enhance throughput, minimize latency, and optimize user satisfaction. The paper includes scenarios involving varying User Equipment (UE) densities and mobility to evaluate system performance under different load conditions. Through simulation-based analysis, this paper highlights the efficacy of the proposed approach in significantly improving network performance, energy efficiency, and resource utilization.},

note = {to appear},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Framework for User Traffic Prediction and Resource Allocation in 5G Networks},

author = {Ioannis Konstantoulas and Iliana Loi and Dimosthenis Tsimas and Kyriakos Sgarbas and Apostolos Gkamas and Christos Bouras },

url = {https://telematics.upatras.gr/applsci-3728840/},

year  = {2025},

date = {2025-07-07},

urldate = {2025-07-07},

journal = {Applied Sciences Jouranl, MDPI},

abstract = {Fifth-Generation (5G) Networks deal with dynamic fluctuations in user traffic and the demands of each connected user and application. This creates a need for optimizing 2

resource allocation to reduce network congestion in densely populated urban centers, and further ensure Quality of Service (QoS) in (5G) environments. To address this issue, we present a framework for both predicting user traffic and allocating users to base stations in 5G networks using neural network architectures. This framework consists of a hybrid approach utilizing a Long Short-Term Memory(LSTM) network or a Transformer architec-ture for user traffic prediction in base stations, as well as a Convolutional Neural Network (CNN) to allocate users to base stations in a realistic scenario. The models show high accuracy in the tasks performed; especially, in the user traffic prediction task, where the 1models show an accuracy of over 99%. Overall, our framework is capable of capturing long-term temporal features and spatial features from 5G user data, taking a significant step towards a holistic approach in data-driven resource allocation and traffic prediction in 5G networks.},

note = {to appear},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokeye,

title = {Analyzing mmWave Bands from a tecno – economic perspective in 5G networks},

author = {Christos Bouras and Anastasia Kollia},

url = {https://bouras.upatras.gr/analyzing-mmwave-bands-from-a-techno-economic-pers/},

year  = {2024},

date = {2024-12-01},

urldate = {2024-12-01},

journal = {International Journal of Business Data Communicatrions and Networking (IJBDCN), IGI Global},

volume = {19},

issue = {1},

pages = {1-20},

abstract = {5G has almost arrived, and the demands imposed by mobile subscribers gradually augment in terms of network performance, coverage, and efficiency. Thus, the frequencies that are already available 

do not adequately cover the users’ needs. In this context, Milimeter Wave technology (mmWave) appears as one of the 5G key enablers, as the unused and available spectrum of 30-300GHz (mmWave) 

could be redefined in order to cover several issues that have appeared concerning bandwidth. The mmWave band is almost unused, and as a result, it could cover future demands and network users. 

In this paper, the mmWave is analyzed in a techno-economic way. What is more, mathematical models for the costing of mmWave are developed. Parameters for the models are opted. Sensitivity 

Analysis (SA) experiments contribute to indicating the most fundamental network parameters and costs concerning the mmWave and the bandwidth in general. Conclusions are summarized and future 

research is suggested.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokeyf,

title = {Simulation Based Energy Efficiency Analysis and Evaluation of DUDe 5G Networks},

author = {Chrysostomos-Athanasios Katsigiannis and Konstantinos Tsachrelias and Christos Bouras and Vasileios Kokkinos and Apostolos Gkamas and Philippos Pouyioutas},

url = {https://bouras.upatras.gr/ijoainas2024_bouras/},

year  = {2024},

date = {2024-12-01},

urldate = {2024-12-01},

journal = {International Journal on Advances in Networks and Services (to appear)},

volume = {17},

number = {3&4},

abstract = {Meeting the escalating demands for data traffic in fifthgeneration networks and beyond requires efficient solutions like Heterogeneous Networks, which enhance spectral and energy efficiency by eploying small cells close to users. Traditional Downlink and Uplink Coupling often limits uplink efficiency due to power imbalances across base stations. Downlink and Uplink Decoupling addresses this by allowing separate access points for uplink and downlink, optimizing user association and energy use. This research expands upon previous conference work by introducing a new scenario that evaluates Downlink and Uplink Decoupling’s performance at a 25 decibel milliwatts user equipment power setting, along with an additional experiment for 1500 user devices in the 20 and 30 decibel milliwatts scenarios. The 

extended analysis offers deeper insights into the energy efficiency and resource allocation of Downlink and Uplink Decoupling under various network conditions, confirming its suitability for scalable, 

efficient fifth-generation networks},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4672,

title = {The Nexus between Wind Energy, Biodiversity Protection and Social Acceptance: Evidence of Good Practices from Greece, Latvia and Poland},

author = {Christos Bouras and Eirini Stergiou and Charitini Karakostaki and Vasileios Tzanos and Vasileios Kokkinos},

url = {https://telematics.upatras.gr/wp-content/uploads/2024/06/IJEEE2023_Bouras.pdf},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {International Journal of Environmental and Ecological Engineering , World Academy of Science, Engineering and Technology},

volume = {18},

pages = {85-94},

abstract = {Wind power represents a major pathway to curtailing greenhouse gas emissions and thus reducing the rate of climate change. A wind turbine runs practically emission-free for 20 years, representing one of the most environmentally sustainable sources of energy. Nevertheless, environmental and biodiversity concerns can often slow down or halt the deployment of wind farms due to local public opposition. This opposition is often fuelled by poor relationships between wind energy stakeholders and civil society, which in many cases led to conflictual protests and property damage. In this context, addressing these concerns is essential in order to facilitate the proliferation of wind farms in Europe and the phase-out of fossil fuels from the energy mix. The aim of this study is to identify a number of good practices and cases to avoid increasing biodiversity protection at all stages of wind farms’ lifecycle in three participating countries, namely Greece, Latvia, and Poland. The results indicate that although available technological solutions are already being exploited worldwide, in these countries, there is still room for improvement. To address this gap, a set of policy recommendations is proposed to accomplish the wind energy targets in the near future while simultaneously mitigating the pertinent biodiversity risks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4670,

title = {Long Range Based IoT Search and Rescue System, a Human- Computer Interaction Preliminary Study and Implementation},

author = {Christos Bouras and Apostolos Gkamas and Spyridon Aniceto Katsampiris Salgado},

url = {https://telematics.upatras.gr/wp-content/uploads/2024/06/CNC2023_Bouras1.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Computer Networks and Communications, Universal Wiser},

pages = {2 - 16},

abstract = {Nowadays, IoT has been introduced in different aspects of our lives giving solutions to several problems, one of which is the Search and Rescue (SAR) operations. People that suffer from dementia or people with disease in the autism spectrum tend to elope from their caretaker’s attention, a fact that can lead to serious problems. Thus, a system that takes into consideration of the multiple requirements is of paramount importance. In this paper, a LoRa based SAR system is proposed in order to help in the localization of people with high risk of going missing. In contrast to most of the related systems, in this research article a different approach has been followed. Instead of focusing on the technology, in our approach a human-centered design has been used, leveraging the knowledge of Human-Computer Interaction (HCI) domain. Thus, the technologies used, are an outcome of the initial study. Finally, the system is evaluated using heuristic evaluation and questionnaires},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4671,

title = {Analyzing mmWave Bands from a techno – economic perspective in 5G networks},

author = {Christos Bouras and Anastasia Kollia},

url = {https://telematics.upatras.gr/wp-content/uploads/2024/06/IJBDCN2023_Bouras.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {International Journal of Business Data Communications and Networking (IJBDCN), IGI Global},

pages = {1 - 26},

abstract = {The data rates used by mobile network subscribers have been augmented the last few years either via excessive social media usage or video-streams. Thus, most traffic is exchanged in video form online. The next decade almost 7-10 devices per person will be connected via the Internet. The Internet of Things (IoT) is a technology contributing in this direction, as simplistic domestic devices such as cookers, fridges etc. are going to be connected in the Internet and will offer the chance of remote managing of these appliances via applications in mobile phones. 

Nowadays, mobile subscribers require augmented demands in terms of mobile service experience, which require large network resources. Users are not extremely content by network’s efficiency and ask for better services in lower prices. They also require coverage on the most remote places, such as high mountains, beaches etc. as social media are a huge part of the everyday life and thus, users want to stay connected as much as possible},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4547,

title = {Prediction Mechanisms to Improve 5G Network User allocation and Resource management},

author = {Christos Bouras and Rafail Kalogeropoulos},

url = {https://telematics.upatras.gr/bouras-kalogeropoulos2022_article_predictionmechanismstoimprove5/},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Wireless Personal Communications, Springer Verlang},

pages = {1 - 25},

abstract = {As technology rapidly advances, the number of devices constantly communicating, transmitting and receiving data through the cellular networks keeps rising, posing an unprecedented load on them. Such an increase calls for establishing new methods to manage these devices as well as utilize the data produced by them to establish network architectures that can prevent cellular networks from overloading. To achieve the desired results, we need to optimally allocate network resources to existing users. Resource allocation has traditionally been considered an optimization problem where proposed solutions are hard to implement in real time, resulting in the use of inferior solutions with reduced produced performance. With the introduction of Machine Learning, we propose three mechanisms, intending to utilize network data to improve real time network performance. The first mechanism, a Decision Trees based mechanism aims to improve real time decision making by predicting the optimal matching of users and Base Stations. The second mechanism, a K-means based mechanism intends to tackle network congestion and ensure uninterrupted Quality of Service by predicting the optimal coordinates for placing Base Stations along the network based on traffic data. Finally, a Regression based mechanism manages to predict user movement along the network, resulting in improved resource management and reduced energy waste. These mechanisms can work cooperatively, essentially establishing a network architecture that utilizes prediction to efficiently allocate users and manage available resources.},

keywords = {Internet of Things, Machine Learning},

pubstate = {published},

tppubtype = {article}

}

@article{4568,

title = {Honey discrimination based on the bee feeding by Laser Induced Breakdown Spectroscopy},

author = {Dimitris Stefas and Nikolaos Gyftokostas and Panagiotis Kourelias and Eleni Nanou and Chrysoula Tananaki and Dimitrios Kanelis and Vasileios Liolios and Vasileios Kokkinos and Christos Bouras and Stelios Couris},

url = {https://telematics.upatras.gr/honey-discrimination-based-on-the-bee-feeding-by-libs/},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Food Control, Elsevier Science},

pages = {1 - 9 },

abstract = {In the present work, the effects of artificial feeding of bees on the honey are investigated by employing for the first time, Laser Induced Breakdown Spectroscopy (LIBS) by analyzing the emission spectral characteristics of the plasma created on the surface of honey samples. Correlation plots indicating the importance of spectral lines of elements as e.g., Calcium (Ca), Magnesium (Mg), Sodium (Na) and Potassium (K) are constructed, while machine learning algorithms based on Linear Discriminant Analysis (LDA) and Random Forest Classifiers (RFC) are employed to classify the honey samples in terms of the bee food used. The constructed machine learning 

models were validated by both cross-validation and external validation, while the obtained accuracies exceeded 90% of correct classification, indicating the potential of LIBS technique for honey discrimination. The obtained results by LIBS were also validated by HPLC-RID, which is the standard technique used for the analysis of the main honey sugars.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4510,

title = {Energy efficient mechanism for LoRa networks},

author = {Christos Bouras and Apostolos Gkamas and Spyridon Katsampiris},

url = {https://telematics.upatras.gr/1-s2-0-s2542660521000044-main/},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Internet of Things: Engineering Cyber Physical Human Systems, Elsevier Science, 2021},

volume = {13},

pages = {1-13},

abstract = {Internet of Things (IoT) is the ability of communication between objects and refers to a wide range of applications, such as the Search and Rescue (SAR) operations. SAR applications need long distance connectivity, thus can be benefited by Low Power Wide Area Networks (LPWAN). One LPWAN technology is called LoRa (Long Range). In this context, the WeSAR project has been created that provides a system for locating and rescuing people, especially those who belong to population groups with a very high probability of getting lost. The energy consumption of the wearable devices is important factor in the SAR operations, as the battery should last more than 45 50 hours. Therefore, the proposed system is based on LoRa technology, the user localization is based on LoRa using trilateration and Time Difference of Arrival (TDoA) instead of Geolocation Positioning System (GPS), as GPS increases the energy consumption, and we created an energy-efficient mechanism to tackle the problem of energy consumption. In this paper, an energy efficient mechanism for LoRa networks is presented, that is based on the user’s state and the battery level of the wearable device. Realistic simulations have been conducted to evaluate the system for both one wearable device, and multiple wearable devices, using different mobility models. The results from the simulations have shown a decrease in the energy consumption in various node mobility models that were tested, without compromising the delivery ratio of the network, something important as the LoRa packets are used for the localization of the lost person.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4524,

title = {MAES_GR: A Web-Based, Spatially Enabled Field Survey Platform for the MAES Implementation in Greece},

author = {Ioannis Kokoris and Vasileios Kokkinos and Evangelos Michos and Rafail Kalogeropoulos and Marios Charalambides and Agisilaos Kounelis and Eleni Iliadou and Christos K. Damianidis and Giorgos Mallinis and Christos Bouras and Panayotis Dimopoulos},

url = {https://telematics.upatras.gr/land-10-00381/},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Land, MDPI 2021},

volume = {10},

pages = {1 - 18},

abstract = {This study presents a standardized approach to collecting, registering, and reporting field-survey data for baseline MAES (Mapping and Assessment of Ecosystems and their Services) information in Greece. This is accomplished through a web-based platform (MAES_GR) exclusively developed under the relevant, nation-wide LIFE-IP 4 NATURA project. Based on the European Commission’s guidance for ecosystem condition (EC) and ecosystem services (ES) MAES studies, we conceptualized and structured an online platform to support EC and ES assessments, integrating all relevant fields of information needed for registering EC and ES parameters. A novel algorithm calculating EC was also developed and it is available as an integral part of the platform. The use of the MAES_GR platform was evaluated during nationwide field surveys efforts, increasing time efficiency and reducing costs. Field recording of EC and ES pinpoint spatial priorities for ecosystem restoration, conservation and sustainable development. This work highlights that MAES implementation can be favored by the use of technology tools such as mobile survey platforms, developed according to scientific needs and policy guidelines. Such tools, apart from the data inventory phase, can be used for data analysis, synthesis and extraction, providing timely, standardized information suitable for reporting at the local, regional, national and European Union scale.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4521,

title = {Classification of Greek olive from different regions by machine learning - aided Laser - Induced Breakdown Spectroscopy and Absorption Spectroscopy},

author = {Nikolaos Gyftokostas and Eleni Nanou and Dimitrios Stefas and Vasileios Kokkinos and Christos Bouras and Stelios Couris},

url = {https://telematics.upatras.gr/s41598-021-84941-z_0/},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Open Access Journal, MDPI, Special Issue Characterization of Olive Products from Greece},

volume = {26},

pages = {1 - 19},

abstract = {In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All 

data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4519,

title = {A Comparative Study of Machine Learning Models for Spreading Factor Selection in LoRa Networks},

author = {Christos Bouras and Apostolos Gkamas and Spyridon Katsampiris and Nikolaos Papachristos},

url = {https://telematics.upatras.gr/a-comparative-study-of-machine-learning-models-for-spreading-factor-selection-in-lora-networks/},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Journal of Wireless Networks and Broadband Technologies (IJWNBT), IGI Global},

volume = {10},

pages = {100-121},

abstract = {Low Power Wide Area Networks (LPWAN) technologies offer reasonably priced connectivity to a large number of low-power devices spread over great geographical ranges. Long Range (LoRa) is a LPWAN technology that empowers energy-efficient communication. In LoRaWAN networks collisions are strongly correlated with Spreading Factor (SF) assignment of end-nodes which affects network performance. In this work, SF assignment using Machine Learning models in simulation environment is presented. This work examines three approaches for the selection of the SF during LoRa transmissions a) random SF assignment b) Adaptive Data Rate (ADR) and c) SF selection through Machine Learning (ML). The main target is to study and determine the most efficient approach as well as to investigate the benefits of using ML techniques in the context of LoRa networks. In this research a library that enables the communication between ML libraries and OMNeT++ simulator was created. The performance of the approaches is evaluated for different scenarios, using the delivery ratio and energy consumption metrics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4576,

title = {Performance Evaluation of Monitoring IoT Systems using LoRaWan},

author = {Christos Bouras and Apostolos Gkamas and Vasileios Kokkinos and Nikolaos Papachristos},

url = {https://telematics.upatras.gr/telecommunicationssystems2021/},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Telecommunication Systems, Springer Verlang},

pages = {295 - 308 },

abstract = {The proliferation of smart devices, or even better, IoT devices, has led to the widespread development of applications that take advantage of these devices. Of particular interest is the precise localization of such a device. However, these use cases become extremely difficult when connectivity to end-devices is required even in areas where the signal is too low or different technologies co-exist for the transmission of the data. In this research work, we study LoRaWan and Wi-Fi as two possible candidates for data transmission. We are particularly focused on the study of the above technologies in terms of performance as well as application development that can be used as rescue monitoring systems. For this reason, we start by describing LoRa as an ideal low power and long-distance communication protocol on the IoT devices compared to the Wi-Fi network. We perform various simulations in terms of time on air transmission, bit error rate by changing important metrics to study the behavior of the whole mechanism. Based on our simulations, the main findings highlight that the contribution of a Spreading Factor (SF) and bandwidth (BW) optimizations can be applied to real hardware for real Search and Rescue (SAR) cases giving improved results in case of coverage and battery extension applications. As a continuation of our research, we developed a monitor application that collects and visualizes data from end-nodes (wearables). These data are processed gateway and network server to The Things Network (TTN) for further analysis. The proposed solution can be used in different rescue monitor scenarios such as identifying and find individuals of vulnerable groups or those belonging to group of people with a high probability of being lost. The purpose of the above solution is to overcome monitor problems on SAR cases, compare with WiFi and suggest a module supporting both technologies in order to be used in real experiments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4546,

title = {A Laser-Based Method for the Detection of Honey Adulteration},

author = {Dimitrios Stefas and Nikolaos Gyftokostas and Panagiotis Kourelias and Eleni Nanou and Vasileios Kokkinos and Christos Bouras and Stelios Couris},

url = {https://telematics.upatras.gr/applsci-11-06435/},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Applied Sciences, MPDI, Special Issue Chemical Composition, Properties and Applications of Honey},

pages = {1 - 14 },

abstract = {In the present work, laser-induced breakdown spectroscopy, aided by some machine learning algorithms (i.e., linear discriminant analysis (LDA) and extremely randomized trees (ERT)), is used for the detection of honey adulteration with glucose syrup. In addition, it is shown that instead of the entire LIBS spectrum, the spectral lines of inorganic ingredients of honey (i.e., calcium, sodium, and potassium) can be also used for the detection of adulteration providing efficient discrimination. The constructed predictive models attained high classification accuracies exceeding 90% correct classification.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4538,

title = {Discrimination of olive oils based on the olive cultivar origin by machine learning employing the fusion of emission and absorption spectroscopic data},

author = {Christos Bouras and Dimitrios Stefas and Nikolaos Gyftokostas and Panagiotis Kourelias and Eleni Nanou and Vasileios Kokkinos and Stelios Couris},

url = {https://telematics.upatras.gr/1-s2-0-s0956713521004564-main-1/},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Food Control, Elsevier Science},

volume = {130},

pages = {1-8},

abstract = {In this work Laser-Induced Breakdown Spectroscopy (LIBS) and absorption spectroscopy aided by machine learning are employed for discriminating some extra virgin Greek olive oils of different olive cultivars for the first time. LIBS and absorption spectra of extra virgin olive oils belonging to Kolovi and Koroneiki cultivars, as well as mixtures of them, were collected, analyzed, and used to develop classification schemes employing Linear Discriminant Analysis and Gradient Boosting, the latter allowing the determination of the most important spectral features. Both algorithms were found to provide efficient classification of the olive oil spectra with accuracies exceeding 90%. Furthermore, for the first time, the emission spectra of LIBS were fused with the absorption spectra to create predictive models and their accuracies were found to be significantly improved. This work demonstrates the enhanced capabilities of LIBS and absorption spectroscopy and the potential of their combination for olive oil quality monitoring and control.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4520,

title = {Laser-Induced Breakdown Spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination},

author = {Nikolaos Gyftokostas and Dimitrios Stefas and Vasileios Kokkinos and Christos Bouras and Stelios Couris},

url = {https://telematics.upatras.gr/s41598-021-84941-z/},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Scientific Reports, Nature Research Journals},

volume = {11},

pages = {1 - 11},

abstract = {Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4502,

title = {An online tool on sustainable water management},

author = {Christos Bouras and Vasileios Kokkinos and Evangelos Michos},

url = {https://telematics.upatras.gr/450-465-paper-06-tourism-2020-04eng/},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Tourism: An International Interdisciplinary Journal, Institute for Tourism, Zagreb, Croatia, 2020},

volume = {68, Νο4},

pages = {450-465},

abstract = {Water demand strains supply capacity in the Mediterranean coastal areas, affecting negatively the maintenance of natural and cultural heritage. Tackling this subject, the “Coastal areas sustainable tourism water management in the Mediterranean - CASTWATER” project aims to support sustainable tourism water management in Mediterranean (MED) coastal areas, by improving the monitoring and assessment of the water sustainability performance of tourism. To this end, the elaboration of an online tool to monitor and assess sustainable tourism water management was envisaged and developed, primarily addressed to tourism sector small and medium-sized enterprises (SMEs). The CASTWATER online tool was designed to so as to provide two different functionalities. On the one hand, SMEs can self-evaluate their performance in sustainable water management and efficiently acknowledge possible actions that promote water efficiency in their tourism establishments. On the other hand, the tool measures, at both regional and local level, the levels of good governance and the effectiveness of water-tourism policies in order to improve sustainable water management. The approach presented in this work is heavily based on the initial technical specifications and end-user feedback, aiding SMEs in understanding, comparing, assessing and rating their performance regarding water efficiency and sustainable water manag},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{4466,

title = {A QoS driven adaptive mechanism for Downlink and Uplink Decoupling in 5G},

author = {Christos Bouras and Rafail Kalogeropoulos},

url = {https://telematics.upatras.gr/1-s2-0-s2542660520300524-main/},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Internet of Things: Engineering Cyber Physical Human Systems, Elsevier Science},

volume = {11},

pages = {1-11},

abstract = {In current cellular networks, cell association is heavily based on the Downlink signal power and all devices are associated with the same Base Station (BS) in Downlink (DL) and Uplink (UL). While as of now this technique has been proved adequate in homogeneous networks where all BSs have similar transmission levels, in increasingly dense heterogeneous networks (HetNets) rate is heavily dependent on the load, which can significantly vary from BS to BS. Due to increased demands for usage over several devices in HetNets, large disparities in the DL pose a threat to the quality of services (QoS) rendered by the network and this technique seems obsolete. Decoupling Uplink and Downlink is the proposed solution, where the UL cell association is not necessarily based on the same criteria as the DL association. We propose using Signal to Noise Ratio (SINR) and Path Loss with Range Extension (PLRE) as factors for choosing the appropriate BS for connection in DL and UL respectively for every user, taking into consideration the BS’s Resource Block availability, to avoid overloading BSs. We will use simulations to test our approach, with different Path Loss offsets for PLRE, to note the importance of adaptive mechanisms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}