@article {4547,
	title = {Prediction Mechanisms to Improve 5G Network User allocation and Resource management},
	journal = {Wireless Personal Communications, Springer Verlang},
	year = {2022},
	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.},
	author = {Christos Bouras and Rafail Kalogeropoulos}
}
@conference {4525,
	title = {Clustering Based User Allocation in 5G Networks},
	booktitle = {The 2021 IEEE International Symposium on Networks, Computers and Communications (ISNCC 2021), October 31 {\textendash} November 2, 2021, Dubai - UAE },
	year = {2021},
	abstract = {Machine Learning is an extremely efficient technique for solving complex problems without the use of traditional programming but rather enabling machines to learn from an input of data and train them to cope with various problems. The rapid growth in the number of active mobile devices, mobile applications and services dictate an efficient utilization of mobile and wireless networking infrastructure. Communication networks need to evolve and valorize Machine Learning methods in order to process large volumes of data without introducing excessive time delay in these computations. Upcoming 5G systems are expected to be the first network infrastructure to support exploding mobile traffic volumes and Machine Learning techniques can be used in order to help manage the rise in data volumes. We present a mechanism for resource allocation in mobile and wireless networks, that effectively utilizes Machine Learning techniques.},
	author = {Christos Bouras and Rafail Kalogeropoulos and Ioannis Sina}
}
@article {4524,
	title = {MAES_GR: A Web-Based, Spatially Enabled Field Survey Platform for the MAES Implementation in Greece},
	journal = {Land, MDPI 2021},
	volume = {10},
	year = {2021},
	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{\textquoteright}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.},
	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}
}
@conference {4518,
	title = {User Allocation in 5G Networks Using Machine Learning Methods for Clustering},
	booktitle = {The 35th International Conference on Advanced Information Networking and Applications (AINA - 2021), May 12 - 14, 2021, Toronto, Canada },
	volume = {1},
	year = {2021},
	month = { May 12 - 14, },
	pages = {13-24},
	abstract = {The rapid increase in the volume of devices connected to mobile networks poses unprecedented demands on existing networking infrastructures. Machine Learning is a promising technique, already applied in various sectors of our everyday lives. It enables decision making not with the use of traditional programming but rather by using data to train models to cope with various problems without explicit programming on how to do so. The integration of Machine Learning techniques is deemed necessary in as many processes as possible to help the network face congestion and enable efficient real time decision making. In this paper we present two Machine Learning based mechanisms for improving real time user allocation on the network as well as predicting the best positioning scheme for Smallcell Base Stations to provide effective utilization of the network{\textquoteright}s resources.},
	author = {Christos Bouras and Rafail Kalogeropoulos}
}
@article {4466,
	title = {A QoS driven adaptive mechanism for Downlink and Uplink Decoupling in 5G},
	journal = {Internet of Things: Engineering Cyber Physical Human Systems, Elsevier Science},
	volume = {11},
	year = {2020},
	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{\textquoteright}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.},
	author = {Christos Bouras and Rafail Kalogeropoulos}
}
@conference {4415,
	title = {An Energy Efficient Mechanism for Downlink and Uplink Decoupling in 5G Networks},
	booktitle = {The 14th International Conference on Broad - Band Wireless Computing, Communication and Applications (BWCCA - 2019), Antwerp, Belgium},
	year = {2019},
	month = {November 7 - 9},
	pages = {241-252},
	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 in Downlink and Uplink. 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 rate is heavily dependent on the load, which can significantly vary from Base Station to Base Station. Due to increased demands for usage over several devices in heterogenous networks, large disparities in the Downlink pose a threat to the quality of services rendered by the network and this tecnhique seems obsolete. Uplink and Downlink decoupling is the proposed solution, where the Downlink cell association is not necessarily based on the same criteria as Uplink . We propose using SINR and Path Loss with Range Extension as factors for choosing the appropriate Base Station for connection in Downlink and Uplink respectively, taking into consideration the Base Stations{\textquoteright} Resource Block availability, to avoid overloading Base Stations and we will use simulations to test our theory.},
	author = {Christos Bouras and Georgios Diles and Rafail Kalogeropoulos}
}
