5G and beyond networks aim to deliver higher speeds, lower latency, and denser device connectivity. Software-Defined Networking (SDN) and Virtual Network Functions (VNFs) are key enablers of these goals, offering flexible, software-driven solutions that decouple the networking functionalities from vendor-closed traditional hardware. In this context, many implementations of the User Plane Function (UPF), such as SPGW-U and VPP, rely on software for packet forwarding and traffic management. Additionally, the integration of AI and machine learning (AI/ML) offers predictive capabilities, allowing future networks to optimize resource allocation, anticipate traffic patterns, and enhance overall network performance dynamically. This work develops a proactive switching mechanism designed to dynamically switch between two different software implementations of the UPF, namely the SPGW-U and VPP-based implementation, based on throughput performance and energy consumption. SPGW-U offers limited throughput performance but consumes reasonable energy, whereas VPP delivers significantly higher throughput performance at the cost of much greater energy consumption. The proposed mechanism evaluates these trade-offs and employs an integrated LSTM model to proactively predict throughput values. Based on these predictions, the switching mechanism determines when to switch between the two implementations, optimizing network performance and energy efficiency dynamically.

SLICES-RI is being developed as a scientific instrument, following established methodologies and best practices from the scientific community. This approach is increasingly essential to address the accelerating pace of data-driven research, which continuously generates a deluge of publications. The core mission of a scientific instrument is to provide a standardized and trusted reference, enabling direct performance comparisons of algorithms and ensuring reproducibility — thereby simplifying and strengthening the peer review process. To meet these goals, SLICES-RI adopts an intent-based design and generates tailored Blueprints for specific scientific questions. While it does not aim to be exhaustive, this approach significantly reduces the complexity involved in designing and executing experiments. In this presentation, we share the current deployment status of SLICES-RI as it entered its pre-operational phase. We illustrate its capabilities through the Post-5G and Federated Learning Blueprints. Additionally, we highlight MRS/DMI, a key feature of SLICES-RI, which offers an advanced data management framework aligned with the FAIR principles and fully integrated into the experimental workflow.

Fifth generation (5G) and beyond cellular networks bring significant advantages over their predecessors in the RAN part of the network, as they are based on a disaggregated, cloudbased architecture, marking a major shift from the monolithic designs of previous generations. This disaggregated approach has led to the development of Virtual RAN (V-RAN) and Open RAN (O-RAN) architectures, which provide greater flexibility in deployment while optimizing costs and resource utilization. Simultaneously, the increasing demands for high-speed, lowlatency connectivity to support diverse 5G applications such as IoT, V2X, and URLLC highlight the critical need for reliable and scalable network solutions. In this paper, we address the scalability challenges of the CU-UP component, identified as a bottleneck in the 5G disaggregated RAN, particularly under high traffic loads. We propose a proactive horizontal scaling mechanism for the CU-UP, leveraging machine learning to forecast traffic demands. This involves using an xApp on the Near-RT RIC that integrates a pre-trained Long Short-Term Memory (LSTM) model using a real-world dataset which predicts traffic demands, enabling proactive scaling of CUUP instances ahead of peak periods to maintain Quality of Service (QoS). The proposed mechanism was implemented and evaluated using real-world testbeds under dynamic conditions, demonstrating its effectiveness in practical environments. Our findings emphasize the importance of integrating ML techniques to forecast network loads accurately and adapting such scaling mechanisms in 5G systems to reduce packet loss and unnecessary scaling overhead while ensuring smoother operation during highdemand periods.

Experimental research in Post-5G involves complex interactions between software, hardware, and protocols. Therefore, it is crucial to develop solutions that allow researchers to conduct their experiments in a reproducible manner. To support this need, the EU SLICES Research Infrastructure (RI) provides a scientific instrument that encompasses all the needs for Post-5G experimental research. The facility is currently being built to enable experimentation with state-of-the-art resources in various fields. SLICES-RI is intent-driven and facilitates the entire lifecycle of thought experiments. This is achieved by enabling reproducible deployment of experiments over the infrastructure using blueprints and by systematically collecting and archiving all outputs through a clear and structured methodology for experimentation. For this demonstration, we focus on the Post-5G part of the facility and will showcase how the entire lifecycle of such an experiment is orchestrated using the tools and functionalities developed. We will showcase blueprints for deploying a cloud-native 5G core and a split 7.2 radio network using open-source software in a fully reproducible manner, with the results being automatically archived and published using the SLICES metadata model. The reproducibility, deployment options, experimenter control capabilities, and access to the collected results will be highlighted.

The transition to the 5th generation (5G) of mobile networks introduces significant advancements in telecommunications, notably in data transmission speeds, connectivity, and the accommodation of varied service requirements. Ultra-Reliable and Low-Latency Communications (URLLC) are at the cutting edge of 5G advancements, playing a critical role in enabling applications like autonomous driving, telemedicine, and the Industrial Internet of Things (IIoT). However, despite URLLC being a part of the 5G standards, achieving its goals of extremely low latency and high reliability is challenging. This paper employs the OpenairInterface (OAI) platform for a holistic end-to-end analysis of URLLC. Through the evaluation of various UPF implementations and RAN configurations, that can highly affect the perceived end-user network latency, in addition to system adjustments aimed at performance optimization, this work successfully reduces latency almost to half that of standard configurations and achieves a near-zero Block Error Rate (BLER). This study attempts to shed light on the practical difficulties of meeting URLLC standards in 5G networks and provide a basis for further research in real-world experimentation.

5G technology, the latest advancement in mobile networks, promises increased data speeds, reduced latency, and enhanced capacity. However, network performance and user experience can be critically impacted by malicious traffic, identified as anomaly traffic and intrusion methods. Addressing these challenges requires optimized resource sharing and robust network security measures. In this paper, we present an AI/ML-driven Network Intrusion Detection framework with dynamic resource allocation and user management within the O-RAN architecture. Our Anomaly Traffic Detector (ATD) enhances network security by mitigating Denial of Service (DoS) attacks through an xApp that classifies network traffic in real-time and dynamically adjusts network resources and user connections. Experimental evaluations show that our system effectively maintains low latency under attack conditions, nearly doubles the throughput for legitimate users, and reduces average CPU usage by up to 15%. We use as reference platforms the OpenAirInterface, and FlexRIC for programming the slice and user connectivity decisions at the RAN level, and evaluate our scheme under real-world settings in a testbed environment.

We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly penalize transformation irregularities but instead promote transformation regularity via an inverse consistency penalty. We use a neural network to predict a map between a source and a target image as well as the map when swapping the source and target images. Different from existing approaches, we compose these two resulting maps and regularize deviations of the Jacobian of this composition from the identity matrix. This regularizer - GradICON - results in much better convergence when training registration models compared to promoting inverse consistency of the composition of maps directly while retaining the desirable implicit regularization effects of the latter. We achieve state-of-the-art registration performance on a variety of real-world medical image datasets using a single set of hyperparameters and a single non-dataset-specific training protocol. Code is available at https://github.com/uncbiag/ICON.

Beyond 5G and future next-generation networks will have to cope with the ever-growing traffic demand for mobile traffic, as well as low-latency communications. Network densification has been long proposed as a solution for augmenting the available wireless links with more technologies, thus enhancing the available capacity for the end-users. Nevertheless, selecting the optimal split of traffic among the available links is not a trivial decision. Machine Learning (ML) approaches can assist in these decisions, by forecasting metrics collected directly from the RAN, towards predicting the near-future performance, and appropriately selecting the split of traffic. In this work, we evaluate a total of 22 different ML models in such a traffic steering use case, towards determining the solution that yields the best results in terms of accuracy of predictions, training time, and computational resources. We use a real-world testbed prototype based on OpenAirInterface to evaluate our contributions, and use realistic mobility datasets for emulating client mobility. Our results show that the different algorithms can present variations in terms of the achievable throughput, but several can substantially improve the offered wireless network capacity.

Multi-access Edge Computing (MEC) has been considered one of the most prominent enablers for low-latency access to services provided over the telecommunications network. Nevertheless, client mobility, as well as external factors which impact the communication channel can severely deteriorate the eventual user-perceived latency times. Such processes can be averted by migrating the provided services to other edges, while the end-user changes their base station association as they move within the serviced region. In this work, we start from an entirely virtualized cloud-native 5G network based on the OpenAirInterface platform and develop our architecture for providing seamless live migration of edge services. On top of this infrastructure, we employ a Deep Reinforcement Learning (DRL) approach that is able to proactively relocate services to new edges, subject to the user’s multi-cell latency measurements and the workload status of the servers. We evaluate our scheme in a testbed setup by emulating mobility using realistic mobility patterns and workloads from real-world clusters. Our results denote that our scheme is capable sustain low-latency values for the end users, based on their mobility within the serviced region.

As networks become denser and more heterogeneous different paths can be considered in order to reach each multi-homed UE, offering optimal performance. 5G and beyond networks feature contributions related to the dynamic programming of the network, from the operator side, in order to optimally allocate resources in the network. In this work, we consider such a case, where network access is provided to the end-users via heterogeneous (3GPP and non-3GPP) Distributed Units (DUs), converging to a single Central Unit (CU), and programmable on the fly with external interfaces. We employ Machine Learning (ML) methods in order to forecast the Quality of Service (QoS) that a wireless client will get from the network in the near future based on the Channel State Information (CSI) metric. Subsequently, we appropriately steer the traffic over the different heterogeneous DUs for ensuring that the network meets the needs of the UEs. We design, develop, deploy and evaluate our method in a real testbed environment, using emulated mobility. Our results show that the overall throughput of each UE can be drastically improved compared to existing allocation mechanisms.

As the containerization of network services is expanding towards the Radio Access Network (RAN), the operators seek to benefit from the paradigm of cloud-native services through a wide ecosystem of practices that are applicable to such resources. Such practices include the dynamic scaling of the services in response to the demand, with the network service being assigned more/less resources, or replicated, for accommodating the incoming demand. In such cloud-native environments, proactive decisions can be accomplished through Machine Learning models, which are efficiently trained for specific metrics that reflect the network demand. In this work, we use a real cloud-native telecommunications network and real traffic patterns, and evaluate four different Machine Learning methods for predicting the incoming demand. The decisions made based on the predictions regard the scaling of the base station (gNB/eNB) and the core network entities that deal with the User-Plane traffic (UPF/SPGW-U). Our results show that higher accuracy for such predictions can be accomplished using the tree-based methods over the Neural Network-based solutions, when each method is used for making accurate pro-active decisions for scaling of the under-study network functions.

The evolution of the cloud-computing technology has allowed the instantiation of resources almost anywhere. Handheld devices, edge/fog resources, and core cloud datacenters comprise a resource continuum that can be used for hosting almost any service. The rise of micro-services has allowed any application to be hosted over any type of compute resource, regardless of the underlying hardware architecture. In this work, we focus on the far-edge devices that participate in the resource continuum, located at the network access or the fog, and are usually resource constrained. We evaluate two lightweight frameworks which can be used for orchestrating micro-services on top of them. Our evaluation presents experimental evidence in terms of their capabilities for instantiating/tear-down of network services, and their dynamic adaptation to external workloads by using the respective horizontal scaling solutions, when tested under the same experimental environment.

A science is defined by a set of encyclopedic knowledge related to facts or phenomena following rules or evidenced by experimentally-driven observations. Computer Science and in particular computer networks is a relatively new scientific domain maturing over years and adopting the best practices inherited from more fundamental disciplines. The design of past, present and future networking components and architectures have been assisted, among other methods, by experimentally-driven research and in particular by the deployment of test platforms, usually named as testbeds . However, often experimentally-driven networking research used scattered methodologies, based on ad-hoc, small-sized testbeds , producing hardly repeatable results. We believe that computer networks needs to adopt a more structured methodology, supported by appropriate instruments, to produce credible experimental results supporting radical and incremental innovations. This paper reports lessons learned from the design and operation of test platforms for the scientific community dealing with digital infrastructures. We introduce the SLICES initiative as the outcome of several years of evolution of the concept of a networking test platform transformed into a scientific instrument. We address the challenges, requirements and opportunities that our community is facing to manage the full research-life cycle necessary to support a scientific methodology.

The evolution of the different network functions to a cloud-native configuration creates fertile ground for the efficient management and reconfiguration of the network. Through the wide application of softwarization and virtualization, cloud-native approaches can extend even to the RAN, that has been dominated by monolithic non-configurable hardware equipment in the past generations of mobile network access. As such, a cloud-native deployment can cover the end-to-end 5G network architecture, from the Core Network to the base stations, with the respective services benefiting from several advanced features, such as automatic scaling of the deployed functions based on monitored metrics. Through the application of Machine Learning, the evolution of the metrics can be predicted and thus the respective functions can be pro-actively scaled. In this work, we use an end-to-end real-world cloud-native deployment of a 5G network, and deal with two different types of scaling, applied at three different parts of the network: vertical scaling for the base station, and horizontal scaling for control and user plane functions of the core network. We use a real-world dataset for replicating traffic over our setup and closely monitor the evolution of metrics from different parts of the network. By applying Machine Learning methods, we accurately predict the future network load and use it to decide on the pro-active allocation of resources for the RAN and the Core Network.

5G redefines the network architecture, through the introduction of base station disaggregation, adding up to the overall flexibility for deployments of network elements based on the existing demand. Moreover, through the wide application of Multi-access Edge Computing (MEC), the latency for accessing services offered on top of the infrastructure can be drastically minimized, thus supporting several applications that exchange critical data over the network. Vehicular communications are a type of applications that can highly benefit from the MEC approach, as they need to exchange critical data with roadside infrastructure (V2I). Nevertheless, the technology through which the vehicles exchange data with the infrastructure may cause fluctuations to the network performance observed by the end-users. In this work, we blend the concept of disaggregated 5G base stations with integrated access for non-3GPP technologies, and the MEC concept, by realizing a novel placement for services that is not yet suggested in existing literature. The setup is applied to a vehicular environment, providing the ability to dynamically steer the traffic over multiple technologies serving each user (vehicle) of the network. We implement, deploy and evaluate our solution in a testbed environment, providing proof on reduced latency for accessing the MEC services as well as the role that the technology selection plays for the overall performance observed by the end-users.

The adoption of Network Functions Virtualization (NFV) is considered as one of the enablers for a fully softwarized 5G architecture, that allows significantly higher flexibility for network service providers to instantiate services, conFigure and update them, as well as to truly realize multi-tenancy. The key enablers of the NFV technology lie in the evolution of virtualization technologies, such as the long established Virtual Machines, and the more recent paradigms of containers for micro-services, like docker and LXC. Such technologies allow the virtualization to span even to the wireless RAN, with fully softwarized base station architectures. Nevertheless, with the plethora of different virtualization technologies, several Virtual Infrastructure Managers (VIMs) and service orchestrators have emerged, each of them addressing different aspects for the provided services e.g. possible nomadic behaviour of the hosting computers, resource constrained devices hosting the services and services deployed for time critical services to name a few. In this work, we use a reference setup and experiment with some of the most widely adopted infrastructure managers, trying to experimentally derive and validate their differences under varying loads of traffic. Our results reveal the time needed for instantiating the same service function, for dockers, containers and Virtual Machines, for a different number of VNFs.

Network Functions Virtualization Management and Orchestration (NFV-MANO) provides a standardized approach for the management and effortless deployment of (virtual) services. Although NFV-MANO initially focused on the deployment of services over datacenters, the introduction of fully softwarized network architectures even for the wireless network creates fertile ground for the re-conception of the manner through which the underlying hardware is managed. In this article, we consider the case of an open experimentation testbed, with focus on wireless networking, and adopt the Open Source MANO framework for provisioning virtual services on top of the experimentation equipment. We extend the Virtual Infrastructure Managers (VIMs) of the NFV-MANO architecture in two well-known frameworks (Openstack and OpenVIM) for the testbed in order to create and handle virtualized wireless network interfaces, hosted on the generic networking nodes of the testbed. Through our contributions, existing VNFs can be deployed over the testbed interconnected over wireless links, specified during the on-boarding phase. The extensions are introduced transparently to the existing operation of the platform, in order to allow the portability of network services and network functions to other instances as well. We focus on providing virtual functions inter-networked over wireless links, which traditionally are not handled by the framework, and allow easier interaction of the end-users with the testbed. We benchmark the framework in terms of performance and analyze the deployment case of a softwarized 5G Radio Access Network in a real testbed deployment.

Mobile telecommunication infrastructure has been considered as the deployment of predefined network components at the end-user access. The advent of novel 5G technologies allows the re-conception of the traditional model through which network services are offered to network User Equipment (UE) devices, through the introduction of new network architectures. Cloud-based Radio Access Networks (Cloud-RANs) add to the network flexibility, allowing on-demand instantiation of new base stations. The introduction of heterogeneous technologies in the cells, and the fact that mobile devices are equipped with diverse wireless interfaces create fertile ground for the operators to select the technologies through which each UE in a cell is served, towards maximizing the perceived QoE. In this direction, we apply the Portfolio Theory, stemming from the finance field, to reveal that the appropriate combinations of networking technologies and traffic allocation can achieve the demanded throughput. In this work we provide a detailed description of this architecture and apply the Portfolio Theory for selecting the appropriate combinations of radio access technologies, towards achieving minimum volatility in throughput performance of a heterogeneous Cloud-RAN. We investigate our approach with proof-of-concept testbed experiments that showcase the applicability and performance of Portfolio Theory in real wireless environment conditions.

Multi-access Edge Computing (MEC) is expected to play an important role in next generation networks, as it is able to provide resources accessible through multiple wireless technologies located close to the network edge. MEC is therefore an enabler for low-latency applications, allowing novel time critical services to be offered through mobile networks. Nevertheless, hosting multiple service providers over the same physical infrastructure shall carefully consider the needs of the MEC enabled applications. Moreover, different suggested placements for the MEC service provide fertile ground for the differentiation of the hosted service providers. In the meantime, the integration of multiple technologies in the wireless access of the MEC concept is increasing the complexity for efficient allocation of network and computational resources among the involved stakeholders. In this work, we model the MEC resource allocation problem for different service providers by using a pricing scheme. We consider two different deployments for the MEC services when considering a multi-technology Cloud-RAN base station: either at the fronthaul interface or located at the Core Network. We integrate intelligence to the MEC enabled framework, by considering the available links through which each client is served. We employ testbed experimentation in order to illustrate the efficiency of our scheme and demonstrate how we achieve efficient allocation of the MEC resources and wireless technologies used for the system under consideration.

In this paper, we focus on 5G heterogeneous networks, considering the existence of multiple Distributed Units (DUs) that can provide access to end users implementing several access technologies, managed by a Central Unit (CU) responsible for the allocation of network resources. Based on a distributed dynamic pricing scheme, that gives to User Equipment (UE) the ability to select the appropriate Radio Access Technology (RAT) depending on its sensitivity to congestion, we investigate a scheme of greater granularity, where UEs are able to allocate each of their traffic classes to the appropriate RAT, exploiting their multi-homing features. As UEs are sequentially polled to request for network resources, we develop a centrally controlled proportionally fair ranking as a benchmark policy. We then propose a dynamic polling policy that presents close performance to the benchmark policy, while maintaining a distributed nature. We evaluate our framework for a variety of traffic classes in terms of Quality of Service (QoS) requirements, and we provide results on capacity utilization and load distribution over available RATs, as well as access price variations.

Multiple-access Edge Computing (MEC) has been proposed as a means to minimize the user to service path latency, by deploying and operating datacenter resources close to the network edge. The introduction of 5G mobile network services, and their provisioning through disaggregated base stations complying with the Cloud-RAN paradigm, allows the redefinition of the traditional Edge Computing by offering deployment of services even closer to the network access edge. In this work, we leverage a disaggregated heterogeneous 5G infrastructure, compliant with the 5G New Radio (NR) specifications, and present a scheme for placing the services even closer to the Edge, close to the concept of fog computing. We develop a scheme for the OpenAirInterface platform that allows services to be executed close or over the machines hosting the radio services for the network access. By exploiting features for integrating heterogeneous radio resources in the cell, we are able to create a controller interface for selecting the optimal radio access technology used to serve each user of the network from the MEC service perspective. We evaluate our solution in a real testbed setup, and measure performance related indicators for our solution by using adaptive video streaming. Our results illustrate up to 80% better video qualities delivered to the end user when appropriately selecting the access technology.

5G networks bring increased flexibility for the operator at different levels. On one side, RAN disaggregation based on the Cloud-RAN concept allows the instantiation of base stations in an area based on demand, whereas on the other side NFV-MANO orchestration brings effortless delivery of services deployed over distributed infrastructure. In this paper, we demonstrate a disaggregated heterogeneous CloudRAN, consisting of cellular and WiFi infrastructure, deployed through the Open Source MANO orchestrator in the distributed infrastructure of a testbed. Through a pair of mmWave and WiFi redundant links, we backhaul the RAN and in case of a broken link, seamlessly switch technologies without affecting the traffic delivered to the end-user.

Radio Access Network (RAN) disaggregation is transforming mobile networks, as it allows the single click instantiation of base stations in the Cloud, and can potentially ease the integration of heterogeneous technologies for wireless access. At the same time, Multi-access Edge Computing is proven to minimize the latency for accessing services located at the network edge. In this demo, we showcase a disaggregated heterogeneous Cloud-RAN, consisting of cellular and WiFi infrastructure, providing access to edge resources placed on the fronthaul of the network to multi-homed UEs. We experiment with different wireless technologies and placements for the MEC service, and illustrate our experimental results.

Cloud-RAN based architectures are widely considered a fundamental part of 5G networks. As a consequence, in the upcoming standards for 5G RAN, disaggregating the RAN functionality between a Central Unit (CU) and multiple Distributed Units (DUs) is considered, addressing the splitting of the 5G protocol stack at the PDCP/RLC point. This split is expected to bring numerous advantages to mobile network operators, as through the isolation of the stack from the PDCP layer and upwards, the CU will be able to act as the Cloud-based convergence point among multiple heterogeneous technologies in the provisioned networks and hence able to serve multiple heterogeneous DUs. Moreover, data rate requirements for this type of split are not very demanding, thus allowing the IP-based transferring of data from the DU to CU and vice-versa. In this work, we propose, implement and evaluate a protocol for a Cloud-RAN based architecture allowing the selection and dynamic switching of different heterogeneous networks in the RAN. We rely on the open source OpenAirInterface platform and extend it to support data plane splitting of the LTE functionality, and the subsequent data injection to WiFi networks. We evaluate the platform using a real network setup, under several scenarios of network selection and different delay settings.

Ultra-Dense Heterogeneous Network deployments are expected to boost the offered network capacity and enhance the user-perceived Quality of Experience, through the simultaneous offering of multiple technologies using distinct or shared wireless spectrum. In such environments with a plethora of available Radio Access Technologies (RATs), the network UEs shall decide either independently or assisted through operator based services on which network they shall use to better serve their needs. In this work, we model the network selection problem in a Multi- RAT system, based on the Paris Metro Pricing (PMP) scheme, enhanced with dynamic pricing formed by the congestion of each available technology. We assume that the network UEs are equipped with multi-homing features and thus are able to use concurrently more than one technologies, based on the requirements of the applications requesting network connectivity (e.g. UHD video streaming). We port and experimentally evaluate the proposed system model over a testbed setup, using distributed components running at the UEs and at a Core Network controller. We provide evaluation results on the average cost per UE for the selected RATs, the average data rate distribution of each UE per RAT and how these performance metrics are affected under different client ordering policies at the network controller.

Centralized RAN processing has been identified as one of the major enablers for 5G mobile network access. By moving the baseband units (BBU) to the Cloud, multiple instances can be instantiated on the fly, serving several Remote Radio Head (RRH) units. The goal is to satisfy the existing demand of particular geographical areas, whereas drastically reducing the overall CAPEX and OPEX costs of the mobile operators. In this work, we present an experimental study of real Cloud-RAN deployments, with respect to different functional splits. We use as a reference architecture the 3GPP LTE stack, and argue about the functional split applicability in contemporary networks. We evaluate Layer 2 functional splits, that can be used for the convergence of multiple heterogeneous wireless technologies in an all-in-one unit. By deploying our approach in a real testbed setup, we extract the backhaul network transfer requirements for the different splits and present our experimental findings, compared with the respective simulation results.

Heterogeneous network access has been proposed as a solution for the continuously deteriorating congestion problem of cellular infrastructures. In this paper we focus on a multi- Radio Access Technology (RAT) environment and we provide a solution based on the Paris Metro Pricing (PMP) scheme, which was first applied in Paris metro. The concept of this pricing scheme was to provide differentiated service classes for customers who desired to avoid being congested, while maintaining the same wagons, characterized only by a different ticket price. The proposed solution extends the classic PMP policy by inducing dynamic prices formed by the congestion of each available technology. We investigate the performance of our dynamic PMP scheme taking into consideration a mobility model of the interested users. Through simulations and testbed experimentation we provide evaluation results on the average throughput, the acceptance capability of the incoming users and how these performance metrics are affected under different mobility conditions.

The wide proliferation of IEEE 802.11 compatible devices and the provisioning of costless Internet connectivity in most cases, have created fertile ground for investigating seamless client mobility and handoff management from a cellular technology to any wireless access point available. Although handoffs and client mobility are currently addressed by the IEEE 802.21 standard along with mobility management protocols such as Mobile IPv6, yet no remarkable efforts exist for the wide deployment of such solutions. Moreover, the adoption of such architectures requires considerable changes in the mobile node's networking stack. In this work, we propose a Software Defined Networking technology inspired scheme for managing client mobility among heterogeneous wireless networks, by adopting changes only on the network edges. Our solution is compatible with the existing IPv4 and IPv6 addressing solutions. By employing the OpenFlow technology on the border of our network with the Internet, we manage to keep both ends of the network aware of any topology changes, and thus preserve any already established connections, resulting in a seamless handoff process. We evaluate our technique in a real network setup, by employing WiFi and LTE technologies and benchmark it using higher layer protocols with multi-homing features, namely Stream Control Transmission Protocol and Multipath TCP.

LTE, commonly known as 4G, stands for Long-Term Evolution and is a wireless communication technology standardized by 3GPP. LTE adoption has increased drastically over the last years, and today is integrated widely in a vast number of wireless devices. However, significant restrictions, such as the increased equipment cost and the acquisition of licensed spectrum, renders experimentation with real LTE equipment very difficult. Recently, iMinds w-iLab.t testbed has been extended with commercial LTE equipment and an EPC (Evolved Packet Core) software platform, offering the opportunity to orchestrate and execute real LTE experiments, hereby providing full access to the most relevant configurable LTE parameters of the network. In this paper, we describe an LTE demonstration scenario that showcases various use cases and the respective experimental settings that can be conducted on the LTE testbed.

The lessons already learned from the existing protocols operation are taken into deep consideration during the standardization activities of the potential technologies opted for the future 5th Generation mobile networks. Prior research on wireless technologies in general has clearly shown the need for open programmable experimental facilities which can be used for the implementation and evaluation of novel algorithms and ideas under real world settings, even directly comparable to existing technologies and methodologies. Nevertheless, provisioning of such testbed platforms mandates the respective tools which will enable access to the testbed resources and will expose the maximum possible flexibility in configuring them. In this work, we present our efforts in building such a facility, along with the tools and services that cope with such requirements. The facility upon which we build is the long-established NITOS wireless testbed, which is offering commercial as well as open source LTE components in a 24/7 basis.

In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, which allows us to characterize the order of development of large-scale and finer folding patterns independently. Given a limited amount of training data, we use a regularization framework to estimate the parameters of the Gompertz model to improve the prediction performance on new data. We develop an efficient method to estimate this regularized Gompertz model based on the Broyden-Fletcher-Goldfarb-Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomic information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurologic deficits in newborns.

This paper demonstrates a time-saving, automated method that helps to segment the lateral ventricles and caudate nucleus in T1-weighted coronal magnetic resonance (MR) brain images of normal control subjects. The method involves choosing intensity thresholds by using anatomical information and by locating peaks in histograms. To validate the method, the lateral ventricles and caudate nucleus were segmented in three brain scans by four experts, first using an established method involving isointensity contours and manual editing, and second using automatically generated intensity thresholds as an aid to the established method. The results demonstrate both time savings and increased reliability.

This paper offers a definition of precise, comprehensive, robust and practical neuroanatomical segmentation in magnetic resonance brain images with the goal of performing quantitative morphometric analyses. The main types of difficulties experienced with such problems are described, including those relating to the classification of MR signal intensities and the fact that there is insufficient information in the 2D image. To illustrate the details of obtaining a morphometric description, a case study of semi-automated methods is presented for segmenting the lateral ventricles and caudate nucleus in T1 coronal MR image data. The most significant remaining difficulties are summarized and are offered as objectives for further research.