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.

This paper introduces the first-ever open-source Multicast and Broadcast Services (MBS) implementation for 5G and beyond cellular networks in OpenAirInterface5G. It outlines the challenges and essential modifications required to incorporate MBS into the 5G architecture while ensuring compliance with the 3GPP specification. The study investigates the performance of the three Hybrid Automatic Repeat Request (HARQ) feedback options for MBS, including the No-ACK, ACK-NACK, and NACK-Only HARQ-ACK options. It evaluates the user plane performance of these HARQ feedback options under realistic network conditions, providing insights into their impact on reliability, throughput, spectral efficiency, and resource utilization for MBS in 5G and beyond networks. Specifically, it examines their effectiveness in scenarios where the User Equipment (UE) devices experience uniform or diverse network conditions, analyzing their overhead and the impact of the Modulation and Coding Scheme (MCS) on their overall performance. Our experiments highlight the substantial performance benefits of MBS over Unicast transmissions and the trade-off between reliability and performance introduced by the HARQ-ACK options. This work advances the practical implementation of MBS in 5G and provides valuable guidelines on optimal deployment strategies and the HARQ feedback option selection for 5G and beyond cellular networks.

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.

This paper proposes a state-of-the-art framework for adapting Active Queue Management (AQM) in 5G and beyond cellular networks with disaggregated Radio Access Network (RAN) deployments. While existing AQM algorithms effectively mitigate bufferbloat in monolithic RAN deployments, their potential in disaggregated ones remains largely unexplored. This gap particularly relates to AQM algorithms relying on communication between layers distributed across distinct network entities to operate. Our research explores the current literature on AQM, identifies the gaps regarding disaggregated deployments, and introduces a comprehensive framework that employs Artificial Intelligence (AI) and Machine Learning (ML) within the RAN Intelligent Controller (RIC) for adapting AQM in such deployments. We evaluate our novel solution on a previously proposed AQM algorithm which requires cross-layer communication, using OpenAirInterface5G (OAI5G) to deploy a disaggregated RAN and a connected User Equipment (UE) that experiences realistic network conditions, including noise and mobility. Finally, we assess its accuracy through the Quality of Service (QoS) achieved for our disaggregated deployment on the NITOS testbed.

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

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.

Numerous well-known applications use the Raft consensus algorithm to maintain consistent replicas of their data on distributed nodes. Raft is based on a dynamically elected leader which is one of the distributed nodes, and its operations are unfortunately suspended during the election of the leader. Elections can be triggered by the failure of the current leader, in which case they are unavoidable, or by a network disconnect between the leader and another node, in which case a new inefficient leader will likely replace the previous one at the expense of additional system downtime. In this paper, Raft messages are monitored at every node, and Machine Learning is used to classify the aforementioned causes of each election. This data is used to increase the system's availability by decreasing the total number of elections that could be conducted in a given time unit. Three supervised classifiers were trained with messages generated in a real Raft-operated distributed system that was deployed on a testbed and where multiple events triggering elections were applied. All classifiers are nearly 97% accurate at classifying the causes of these elections, approaching even 100% in some cases.

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.

The adoption of the Software Defined Networking (SDN) paradigm is aggressively expanding from traditional datacenter networks to 5G and IoT deployments due to its flexibility in network management and routing. Mobile Ad-Hoc Networks (MANETs), with their unique characteristics and requirements stemming from the mobility and volatile wireless medium conditions, have not yet realized the benefits of the SDN approach. Adoption has been hampered by technical challenges and the contradiction between the centralized concept of SDN and the distributed one of traditional MANET routing schemes. In this paper, we present our implementation of a fully-fledged SDN framework, which attempts to bridge the gap by combining the benefits of both worlds. Our developed prototype integrates with 802.11 Ad-Hoc networks and offers a) fault tolerance for the SDN controller by recovering from failures with the (re)-election of a new controller, b) dynamic network topology discovery, and c) a cross-layer approach in routing based on 802.11 MAC layer statistics that more accurately characterize link quality and capacity. We validated the applicability and performance benefits of our method by evaluating our proposed SDN architecture in a proof-of-concept scenario.

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.

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.

Raft is a state-of-the-art consensus algorithm for state replication over a distributed system of nodes. According to Raft, all state updates occurring anywhere in the system are forwarded to the leader, which is elected among the system nodes to collect and replicate these updates to all other nodes. Thus, the time required for the state replication, named as system response time, depends on the delays between the leader and all other nodes. After multiple node failures and leadership transitions, each node can be leader with a probability that affects the expected response time. The leadership probabilities, in turn, are affected by the random intervals that nodes are waiting, after detecting a leader failure and before competing for the successive leadership. The Raft designers suggest the ranges of these intervals to be equal for all nodes. However, this may result in increased expected response time. In this paper, mathematical models are presented for estimating the ranges resulting in the desired leadership probabilities. The presented theoretical results are also confirmed by testbed experimentation with an open-source and widely used Raft implementation.

In this work we propose a new scheme for semi-active Wake-Up Receiver circuits that exhibits remarkable sensitivity beyond -70 dBm, while state-of-the-art receivers illustrate sensitivity of up to -55 dBm. The receiver employs the typical principle of an envelope detector that harvests RF energy from its antenna, while it employs a nano-power operation amplifier to intensify the obtained signal prior to the final decoding that is realized with the aid of a comparator circuit. It operates at the 868 MHz ISM band using OOK signals propagated through LoRa transceivers, while also supporting addressing capabilities in order to awake only the specified network's nodes. The power expenditure of the developed receiver is as low as 580 nA, remaining at the same power consumption levels as the state-of-the-art implementations.

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.

This paper focuses on COSMOS - Cloud enhanced Open Software defined MObile wireless testbed for city-Scale deployment. The COSMOS testbed is being deployed in West Harlem (New York City) as part of the NSF Platforms for Advanced Wireless Research (PAWR) program. It will enable researchers to explore the technology "sweet spot" of ultra-high bandwidth and ultra-low latency in the most demanding real-world environment. We describe the testbed's architecture, the design and deployment challenges, and the experience gained during the design and pilot deployment. Specifically, we describe COSMOS' computing and network architectures, the critical building blocks, and its programmability at different layers. The building blocks include software-defined radios, 28 GHz millimeter-wave phased array modules, optical transport network, core and edge cloud, and control and management software. We describe COSMOS' deployment phases in a dense urban environment, the research areas that could be studied in the testbed, and specific example experiments. Finally, we discuss our experience with using COSMOS as an educational tool.

Despite recent progress in orchestration of Virtual Network Functions (VNFs) and in multi-technology SDN connectivity, the automated provisioning of end-to-end network services composed of virtual functions deployed across distributed compute locations remains an open challenge. This problem is especially relevant to support the deployment of future 5G networks, comprising virtual access and core network functions connected through a potentially multi-domain transport network. In this paper we present and demonstrate the 5GOS, a lightweight end-to-end orchestration framework that enables the automated provisioning of virtual radio access network services. Using an experimental multi-domain testbed we demonstrate that the 5GOS can provision multi-domain virtual Wi-Fi and LTE services in less than three minutes.

This paper discusses the benefits in the operation of a Raft based SDN controller cluster, when the election of the cluster leader becomes more or less “fair. Raft is a leader based consensus algorithm, which is used by the most popular open-source SDN controllers for replicating the network state. It requires all state changes to be confirmed by the leader, thus the leader election is very crucial for the Raft performance. In case that the inter-controller communication delay is the same for all controller pairs, the election process is absolute fair, meaning that the leadership is shared equally among the controllers. In all other cases, some controllers become leaders more frequently in benefit or at a cost of the average time required for a network state update. In this paper, we model this time as a function of the leadership probabilities of the cluster controllers. We also model these probabilities as a function of the time that each controller is waiting after detecting the current leader failure and before starting its campaign. We configure different ranges for the controller waiting times, adjusting the leadership probabilities and decreasing the average response time. Our model is confirmed by testbed experimentation.

Ethernet Virtual Private Network is an emerging Data Center Interconnect technology, which relies on Multiprotocol BGP and an encapsulation protocol over IP, such as Virtual Extensible LAN. It enables the creation of Layer 2 overlays on top of provider IP networks, which can be interconnected through the symmetric or asymmetric Integrated Routing and Bridging extension. In this work, we present our testbed implementation of these technologies using OpenFlow and we compare the advantages and disadvantages of the symmetric and asymmetric solutions.

In this paper, we demonstrate the implementation of a matching theory based algorithm that extends the well known Top-Trading-Cycles (TTC) in the resource allocation mechanism of the NITOS networking testbed. We first provide an overview of prominent networking testbeds and the common approach that is used for the allocation of testbed resources to experimenters. We briefly describe our extended TTC algorithm and how it is applied in the enhanced architecture of NITOS resource allocation framework, by illustrating the interactions that take place between the testbed users and the testbed management services.

In this paper, we examine the problem of the efficient allocation of resources in networking testbeds, which cannot be shared among the experimenters. We highlight the similarities with the housing market where indivisible network resources play the role of houses, while experimenters the role of owners. We adopt the Top-Trading-Cycles (TTC) algorithm for providing Pareto efficient allocations and we compare this approach with the current mechanism of the simple First-Come-First-Served (FCFS) approach used in most networking testbeds. A formulation of the problem is provided where we describe the average utility of the system as a function of the desired testbed resources of the experimenters and the final allocation of the resources to them. In the performance evaluation we observe that TTC outperforms FCFS in all the examined scenarios and achieves almost 95% better average utility in certain cases.

Software Defined Networking (SDN) decouples the control and data planes and moves the control logic to the SDN controllers. The traffic in the control plane is either related to the interconnection of the controllers or to the connectivity between the SDN switches and the controllers. The controller placement affects the total control traffic. Multiple distributed controllers increase the inter-controller traffic, while few concentrated controllers increase the controller to switch traffic. We model the problem of determining the optimal controller placement for minimum control traffic. We further discuss the complexity of the optimization problem, and devise a heuristic algorithm for its solution. Our simulations and test bed experimentation reveal close to optimal performance of the presented heuristic algorithm.

In this paper, we present a UE-driven light-weight mechanism for fast handover decision and efficient WLAN selection in the context of 5G networks. As the network deployments are expected to be denser and the mobile user will be offered a multitude of alternative short coverage range options to have her mobile traffic served, her roaming decision will be performance critical. While the current 3GPP standardization considers the use of network performance statistics of nearby WLANs for the UE-driven roaming selection to address the uncertainty of the shared wireless medium, their collection and processing inevitably affects the mobile user performance and inserts an accuracy-performance tradeoff. We introduce a spectrum assessment framework, that is based on commercial hardware and open-source software, to evaluate the conditions on the nearby WLANs and let the UE to infer their performance with minimum overhead relying on Duty Cycle evaluation and the RSSI metrics. Our ready-to-be deployed solution leverages the use of off-the-shelf equipment and commercial devices and enables fast decision procedures for the WLAN selection with low collection and processing overhead. We evaluate our mechanism by conducting testbed experiments. The results reveal performance gains in terms of UE's achieved throughput when enabling the proposed framework to infer the spectral WLAN conditions and decide for the AP to roam.

The rapidly increasing popularity of 802.11 WLANs along with the co-existence of multiple heterogeneous devices in the unlicensed frequency bands have created unprecedented levels of congestion, especially in densely populated urban areas. Under such complex setups, WLAN under-performance issues experienced by end-users are hard to interpret even by experts. In this paper, we develop an intelligent, easy to deploy mechanism that takes advantage of MAC-layer exported data and employs machine learning techniques to accurately diagnose the five most common WiFi pathologies (contention, low-SNR, non-802.11 Interference, etc.). The collected data are fed to four different classification algorithms, which we fine-tune, in order to optimize their hyper-parameters in regards to their precision and accuracy. The resulting solution provides two different mechanisms, with the first targeting low-overhead passive detection and the second offering more accurate performance relying on active probing. Detection performance is evaluated through extensive testbed experiments and exhibits that the K-Nearest Neighbors classifier achieves almost 100% accuracy and precision for the active probing and 95% accuracy and precision for the passive detection across the five considered pathologies.

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.

Software-defined networking is at the root of future 5G network design. Among others, it allows for automated network reconfiguration and network slicing support. In this paper we present an implementation of a hierarchical control plane in the transport network architecture envisioned by the 5G-PICTURE project. We analyze the implementation of the slicing mechanism at the network edge and the end-to-end path establishment procedure, which involves interactions within a hierarchy of controllers. We also evaluate the latency performance of our control plane solution, by means of testbed experimentation.

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.

In recent years, the rise of Network Function Virtualization (NFV) makes the Network Service (NS) deployment much agile and flexible. The proprietary and custom-made hardware is replaced by a virtual and software-based infrastructure, that is easily exploited in a common way for the NS deployment. One of the most challenging problems in this environment is deploying and organizing in a large-scale and multi-domain infrastructure, which contains geographically distributed but interconnected data-centers. The proposed solution focuses on the extra networking operations required in a NFV infrastructure, managed by Open Source MANO, OpenStack and OpenDaylight. We develop software proxies that collaborate with the aforementioned tools and enhance their functionality. Finally, we implement and evaluate the proposed architecture, using the NITOS experimentation testbed.

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.

In recent years, the rise of Network Function Virtualization (NFV) makes the Network Service (NS) deployment much agile and flexible. The proprietary and custom-made hardware is replaced by a virtual and software-based infrastructure, that is easily exploited in a common way for the NS deployment. One of the most challenging problems in this environment is deploying and organizing in a large-scale and multi-domain infrastructure, which contains geographically distributed but interconnected data-centers. The proposed solution focuses on the extra networking operations required in a NFV infrastructure, managed by Open Source MANO, OpenStack and OpenDaylight. We develop software proxies that collaborate with the aforementioned tools and enhance their functionality. Finally, we implement and evaluate the proposed architecture, using the NITOS experimentation testbed.

The need for specialized hardware acceleration in today's computing platforms is well established, due to power and efficiency reasons. Broadening an accelerator's scope of application is highly desirable, but requires a finer-grained architecture with basic primitives, which inevitably exhibits increased communication and synchronization requirements. In disaggregated-computing environ-ments, where data transfers between remote nodes are realized via datacenter-wide packet exchanges, reducing communication and synchronization is a prerequisite for the effective employment of remote acceleration. To this end, we present ReFiRe (Remote Fine-grained Reconfigurable acceleration), a generic deployment framework with native support for partial reconfiguration that allows to considerably reduce communication needs between a processor and remote accelerators. This is achieved by shifting control flow, partial reconfiguration, and execution decisions to the remote side through arbitrarily long instructions that encapsulate complex sequences of operations and their re-spective synchronization requirements. ReFiRe outperforms an SDSoC-generated accelerator system that employs the same accelerator cores to boost performance of a genomics application that detects positive selection.

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.

Performance experienced by end-users supporting the popular 802.11 protocol is significantly degraded in densely populated urban areas, mainly due to the extensive spectrum sharing and the resulting 802.11 impairments such as ”hidden-terminals”, overlapping channel interference, etc. Moreover, as the unlicensed spectrum is also home for other wireless technologies and a large range of RF devices, the experienced channel conditions further deteriorate due to cross-technology interference. While the various resulting phenomena can be efficiently mitigated by isolating affected links from interference sources over spectrum, 802.11 networks currently lack unified mechanisms for characterizing the impact of different interference sources across the available channel configurations. In this work, we take advantage of spectral measurements available at the PHY-layer of commercial 802.11 equipment, in order to develop a highly accurate spectral analysis mechanism that is able to quantify the impact of interference on WLAN performance. The developed distributed mechanism concurrently operates on all network nodes and characterizes the band of interest with minimal overhead. Through the implementation of our approach on commercial 802.11n chipsets and its detailed experimental evaluation, we showcase its applicability in characterizing the impact of spectrum congestion and interference in a unified way, towards driving efficient spectrum adaptation decisions.

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.

This paper proposes distributed load shedding policies for regulating excessive network load. Data packets are inserted into the network to be delivered to intended destinations. The intermediate network nodes may decide to forward or shed some packets depending on temporally available resources. It is possible for some packets to traverse several nodes in the network until they are finally dropped before reaching the destination, which exacerbates energy consumption. We define a multi-objective optimization problem where we aim to minimize the used energy subject to providing maximum sum throughput. For the case of single-path unicast sessions, we show that Energy-efficient Distributed Load Shedding (E-DLS), a simple shedding mechanism combined with pushback routing, solves this load di shedding optimization. We implement E-DLS in a testbed and use the experiments to select policy parameter values that strike a good balance between energy and delay performance. We then propose a heuristic extension of E-DLS for multirate multicast routing, and showcase via testbed experiments its optimal performance.

Together with recent advancements in Radio Access Network (RAN) technologies, Wi-Fi is expected to be at the center of research on the subject of ubiquitous wireless connectivity, towards building the new 5G ecosystem. Nevertheless, the necessary testbed infrastructure to support large scale experimentally driven research seems to be missing. In this work we present the design and implementation of a novel Virtual Wi-Fi Testbed. We present how a traditional Wireless Testbed can support hundreds of virtual Wi-Fi nodes that are open to experimenters. We discuss the design and implementation of the virtualization tools. We demonstrate the accuracy and the overhead analysis of the approach in the face of actual testbed conditions. Implementation experience is also reported on the benefits of using the proposed virtualization approach for a simple association algorithm.

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.

A common simplifying assumption made in wireless simulation and modeling is that the world is flat, i.e. to ignore the effect of the terrain in which the wireless signal propagates. In this paper, we show with empirical measurements from an urban wireless network testbed how the terrain affects the spatial and temporal correlation of the wireless signal, and in turn, the distance or duration over which the wireless signal remains consistent. Furthermore, we suggest that this effect has practical implications for systems that make assumptions about the duration over which wireless signal quality stays roughly the same, such as adaptive transmission schemes or applications that buffer data to smooth over variations in signal quality.

We present a novel control scheme that dynamically optimizes multirate multicast. By computing the differential backlog at every node, our scheme adaptively allocates transmission rates per session/user pair in order to maximize throughput. An important feature of the proposed scheme is that it does not require source cooperation or centralized calculations. This methodology leads to efficient and distributed algorithms that scale gracefully and can be embraced by low-cost wireless devices. Additionally, it is shown that maximization of sum utility is possible by the addition of a virtual queue at each destination node of the multicast groups. The virtual queue captures the desire of the individual user and helps in making the correct resource allocation to optimize total utility. Under the operation of the proposed schemes backlog sizes are deterministically bounded, which provides delay guarantees on delivered packets. To illustrate its practicality, we present a prototype implementation in the NITOS wireless testbed. The experimental results verify that the proposed schemes achieve maximum performance while maintaining low complexity.

As the growth of mobile data traffic places significant strain on cellular networks, plans for exploiting under-utilized network resources become increasingly attractive. In this paper, we propose, design, and evaluate a data offloading architecture, where mobile users are offloaded to mesh networks, which are built and managed by residential users. Such networks are often developed in the context of community networks or, recently, as commercial services. Mobile network operators can lease capacity from these networks and offload traffic to reduce their servicing costs. We introduce an analytical framework that determines the offloading policy, i.e., which mobile users should be offloaded, based on the energy cost induced to the cellular base stations. Accordingly, we design a minimum-cost servicing policy for the mesh networks. Clearly, such architectures are realizable only if the mesh nodes agree with each other to jointly serve the offloaded traffic. To achieve this, we employ the Shapley value rule for dispensing the leasing payment among the mesh nodes. We evaluate this paper by simulating the operation of the LTE-A network, and conducting test bed experiments for the mesh network. The results reveal significant savings for eNBs power consumption and reimbursements for mesh users.

Millimeter wave (mmWave) bands have attracted considerable recent interest for next-generation cellular systems due to the massive available spectrum at these frequencies. However, a key challenge in designing mmWave cellular systems is initial access-the procedure by which a mobile device establishes an initial link-layer connection to a cell. MmWave communication relies on highly directional transmissions and the initial access procedure must thus provide a mechanism by which initial transmission directions can be searched in a potentially large angular space. Design options are compared considering different scanning and signaling procedures to evaluate access delay and system overhead. The channel structure and multiple access issues are also considered. The results of our analysis demonstrate significant benefits of low-resolution fully digital architectures in comparison with single stream analog beamforming.

The recent mobile data explosion has increased the interest for mobile user-provided networks (MUPNs), where users share their Internet access by exploiting the diversity in their needs and resource availability. Although promising, MUPNs raise unique challenges. Namely, the success of such services relies on user participation which in turn can be achieved on the basis of a fair and efficient resource (i.e., Internet access and battery energy) exchange policy. The latter should be devised and imposed in a very fast time scale, based on near real-time feedback from mobile users regarding their needs, resources, and network conditions that are rapidly changing. To address these challenges we design and implement a novel cloud-controlled MUPN system, that employs software defined networking support on mobile terminals, to dynamically apply data forwarding policies with adaptive flow-control. We devise these policies by solving a coalitional game that is played among the users. We prove that the game has a non-empty core and hence the solution, which determines the servicing policy, incentivizes the users to participate. Finally, we evaluate the performance of the service in a prototype, where we investigate its performance limits, quantify the implementation overheads, and justify our architecture design choices.

In this work, we present how programmable data-plane technology (software routers) offers an easy-to-apply mechanism to create virtual wireless networks and support buffering and scheduling decisions. Furthermore, we present a feedback-based buffering mechanism that is able to provide throughput ratio guarantees per virtual network, without requiring any modifications in the 802.11 driver and without relying on statistical knowledge of the workload per virtual network or knowledge regarding the channel conditions. We implement the proposed mechanism in a software router in a 802.11 Access Point and we evaluate its performance in a wireless testbed environment. The methodology and the mechanics developed are generic and with some modifications can be applied to differentiating services for other types of guarantees like delay.

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 this work, we demonstrate and validate a novel architecture for next generation cellular networks that enables collaborative forwarding at Layer 2 among adjacent eNBs with the aid of enhanced user equipment (UE) devices, that act voluntarily as packet forwarders. We introduce an evolved-UE (eUE) which is capable of operating simultaneously over multiples eNBs in order to enable reliable multi-hop operation through relaying and to achieve low-latency communication through efficient L2/MAC forwarding. For the demonstration and the evaluation of this architecture, we used the OpenAirInterface emulation platform to implement it, and also to evaluate its performance. The obtained results show that, the proposed architecture achieves significant reduction in latency (up to 16.94%) and improvement on packet loss rate (up to 59.25%), as the number of the employed eUEs increases with increasing BLER up to 20%. Moreover, the proposed architecture enables eUEs to increase the aggregated data rate in downlink by exploiting data connection to multiple eNBs.

In this paper, we propose a novel architecture for next generation cellular networks that enables collaborative forwarding at Layer 2 among adjacent eNBs with the aid of enhanced user equipment (UE) devices, that act voluntarily as packet forwarders. Therefore, legacy UEs are leveraged as active network elements being capable of operating simultaneously over multiple base stations (eNBs). To this end, we introduce an evolved-UE (eUE) in order to enable reliable multi-hop operation through relaying and to achieve low-latency communication through efficient L2/MAC forwarding. Through extensive experimentation with OpenAirInterface emulation platform, we evaluated the performance and also validated the feasibility of the proposed architecture. Our results show that, in certain use cases corresponding to public safety and moving/small cell scenarios, the proposed architecture achieves significant reduction in latency (up to 16.94%) and improvement on packet loss rate (up to 59.25%), as the number of the employed eUEs increases with increasing BLER up to 20%. Moreover, the proposed architecture enables eUEs to increase the aggregated data rate in downlink by exploiting data connection to multiple eNBs at the expense of extra power consumption, which calls for the appropriate incentives to enable such a cooperation.

Experimental platforms (testbeds) play a significant role in the evaluation of new and existing technologies. Their popularity has been raised lately as more and more researchers prefer experimentation over simulation as a way for acquiring more accurate results. This imposes significant challenges in testbed operators since an efficient mechanism is needed to manage the testbed's resources and provision them according to the users' needs. In this paper we describe such a framework which was implemented for the management of networking testbeds. We present the design requirements and the implementation details, along with the challenges we encountered during its operation in the NITOS testbed. Significant results were extracted through the experiences of the every day operation of the testbed's management.

The acute disparity between increasing bandwidth demand and available spectrum has brought millimeter wave (mmWave) bands to the forefront of candidate solutions for the next-generation cellular networks. Highly directional transmissions are essential for cellular communication in these frequencies to compensate for higher isotropic path loss. This reliance on directional beamforming, however, complicates initial cell search since mobiles and base stations must jointly search over a potentially large angular directional space to locate a suitable path to initiate communication. To address this problem, this paper proposes a directional cell discovery procedure where base stations periodically transmit synchronization signals, potentially in time-varying random directions, to scan the angular space. Detectors for these signals are derived based on a Generalized Likelihood Ratio Test (GLRT) under various signal and receiver assumptions. The detectors are then simulated under realistic design parameters and channels based on actual experimental measurements at 28 GHz in New York City. The study reveals two key findings: 1) digital beamforming can significantly outperform analog beamforming even when digital beamforming uses very low quantization to compensate for the additional power requirements and 2) omnidirectional transmissions of the synchronization signals from the base station generally outperform random directional scanning.

As the unprecedented growth of mobile data traffic places significant strain on cellular networks, alternative plans for exploiting already existing and under-utilized wireless infrastructure, become quite attractive. In this paper, we study cellular-to-mesh (C2M) data offloading for LTE-A cellular mobile users to WiFi mesh networks, which are built and managed collaboratively by users. Such networks are developed in the context of community networks or, recently, as commercial services among residential users. Mobile network operators can lease these mesh networks to offload their traffic and reduce their servicing cost. In this context, we introduce an analytical framework that determines which mobile users should be offloaded, based on the energy cost incurred to the cellular base stations (eNB) for serving their demands. Accordingly, we design a routing policy that the mesh network can employ so as to serve the offloaded traffic with the minimum possible cost. Moreover, the reimbursement offered by the operator should be dispensed to the different mesh users, according to their contribution and added-value significance. We address this issue by employing the Shapley value profit sharing rule, which ensures the participation of the mesh nodes in this joint task. We evaluate our work by simulating the operation of the LTE-A network, and conducting testbed experimentation for the mesh network. The results reveal significant savings for eNBs power consumption and compensation profits for mesh users.

Cloud service provisioning on top of virtual infrastructures is of major importance in modern ICT, since it is directly correlated to the way business models are designed and revenue is generated from the cloud service providers. In this work we examine an end-to-end content replication problem over cloud-based multi-technology infrastructures. We extend the classical model where every network node is a potential replica carrier and the link weights represent hops/delay and we examine replication schemes for content that a) is requested by customers belonging in different virtual networks and b) depending on the requester there is different impact on the system operational cost. We examine both centralized and distributed content replication management policies and we evaluate their performance through extended simulations, by means of total cost, the number of object replacements and the number of iterations required.

Multirate multicast improves user quality but complicates network optimization. This paper introduces a novel control scheme to dynamically optimize multirate multicast. We present MMT, an adaptive policy which combines differential backlog scheduling and intelligent packet dropping, both based on local information. MMT is shown to maximize network throughput by adapting to changing conditions such as channel quality, network congestion, and device capabilities. Then, we study the problem of per-receiver network utility maximization. To maximize sum utility we propose the MMU policy, an extension of MMT with receiver-end flow control. Under the operation of both policies backlog sizes are deterministically bounded, which provides delay guarantees on delivered packets. An important feature of the proposed scheme is that it does not require source cooperation or centralized calculations. To illustrate its practicality, we present a prototype implementation in the NITOS wireless testbed. Experimental results verify the optimality of the scheme and its low complexity.

In this paper we present NITOS Bikes Net, a framework for mobile sensing in a city-wide environment offering experimentation capabilities. More Specifically, we present a custom-made and modular prototype device that can be easily mounted on volunteers' bicycles dedicated to collecting environmental measurements and available WiFi networks. In addition, we present our enhancements in OMF framework through which we remotely control the operation of the developed devices, whenever they experience back-end connection. Finally, we analyze an indicative demonstration experiment which illustrates the capabilities of the developed framework.

In this paper we present the NITOS Bikes Net platform, a city-scale mobile sensing infrastructure that relies on bicycles of volunteer users. NITOS Bikes Net employs a custom-built embedded node that can be equipped with different types of sensors, and which can be easily mounted on a bicycle in order to opportunistically collect environmental and WiFi measurements in different parts of the city. Experimenters can remotely reserve and control the sensor nodes on bicycles as well as collect/visualize their measurements via the OMF/OML framework, which was extended in order to handle the intermittent connectivity and disconnected operation of the mobile nodes. We also provide a performance analysis of our node prototype in terms of sensing latency, end-to-end data transmission capability and power consumption, and report on a first experiment that was performed using NITOS Bikes Net in the city of Volos, Greece.

In this work, we present the AGILE Spectrum Adaptation system that is able to dynamically tune the channel central frequency and bandwidth of wireless links in an adaptive to the interference and traffic conditions way. The developed system is able to detect under-utilised spectrum fragments and optimally adjust the occupied spectrum. Through the online execution of 3 specifically designed experimental scenarios, we demonstrate the ability to implement distributed spectrum adaptation in commercial WLAN deployments, along with the obtained performance benefits.

In this paper, we propose and characterize the performance of a novel video-aware Opportunistic Routing (OR) algorithm for multicast, using a one-hop and two-hop forwarding scheme in 802.11 mesh networks. We believe that the OR approach exploits the inherent broadcast nature of the wireless medium and adapts very well in the lossy wireless environment. Inferring from the above, we extend a state of the art routing algorithm, namely MORE, that leverages on this approach and offers multicast support, but is not efficiently applicable in video streaming. By employing our scheme, we enable support for video streaming application with hard time-constraints. In addition, we focus on the orchestration of the packet transmissions and the prioritization of the video traffic towards improving the video-perception quality of the end users. In order to evaluate the proposed scheme, we conducted experiments in a realistic medium-scale wireless testbed. Our results show that the proposed scheme increases the average video-perception quality, measured in PSNR, by up to 270% in some cases or up to 175% in average, compared to the MORE algorithm.

In this demo paper, we demonstrate and evaluate a novel Opportunistic Routing (OR) protocol for video multicast, namely Video-aware Multicast Opportunistic Routing (ViMOR), over 802.11 two-hop mesh networks. OR exploits the broadcast nature of the wireless medium and offers spatial diversity among the receivers. ViMOR extends MORE, a state of the art OR algorithm, by orchestrating packet transmissions and prioritizing video traffic, in order to conform with video streaming requirements. For the demonstration and evaluation of the proposed scheme, we proceeded with the development of the implementation on NITOS wireless testbed. Results showed a significant increase in average video-perception quality, compared to MORE protocol.

In this paper we evaluate an energy-aware relay selection mechanism which exploits channel state information and the availability of buffers at relays to perform flexible relaying based on a backpressure-driven optimization model. This model ensures the maximization of the cell throughput while maintains the stability of backlog queues. Performance evaluation is conducted using a System Level Simulator (SLS) which is fully compliant with IEEE 802.16m and supports various relaying scenarios. The Below Roof Top (BRT) relaying scenario is considered in this work. A holistic and flexible energy framework is implemented to capture the energy consumption of the cellular network nodes. The model maps the RF output power radiated at the antenna elements of each node including relays on the network to the total supply power of the node equipment. Two derivatives of the proposed mechanism, half-duplex and full-duplex are proposed and evaluated. Results of the two derivatives on BRT relaying scenario revealed noticeable increases for both cell throughput and system energy efficiency of the cell-edge users compared to the conventional relaying protocol and the non cooperative scheme.

Experimentation in testbeds is gaining increasing ground as a necessary validation step for every theoretical study in communication networks. However, the first-come-first-served policy employed today by most testbeds does not ensure the fair and efficient utilization of their resources, which often lie idle. Ideally, every testbed should be utilized as much as possible and serve the most important requests. In this paper we introduce a novel resource scheduling mechanism for the wireless testbed NITOS. The proposed scheme is based on VCG auctions and includes an allocation and a pricing rule which induce the users to judiciously submit experiment requests. We prove theoretically and demonstrate numerically that this scheme ensures that the testbed resources (nodes and channels) are assigned to the users with the highest needs. Our mechanism can be incorporated in the next generation resource management systems for NITOS and similar testbeds.

Cognitive radio systems have gathered a lot of research interest during the last decade. Accuracy of spectrum sensing and efficiency of free spectrum utilization are considered as the primary objectives in this emerging technology, which promises a boost in wireless network performance, through exploitation of underutilized licensed frequency bands. As the focus of researchers is usually on these two major challenges, other aspects have been in part underestimated. In this work, we consider two factors that are rather important for evaluation of cognitive platforms, namely sensing delay and energy efficiency. The first is related to the latency induced by the spectrum sensing process and its impact on sensing efficiency, which is tightly connected to both the QoS performance of secondary users and the protection of primary users. On the other hand, energy consumption is considered as a crucial issue in all types of wireless communications, due to restricted battery autonomy of mobile devices, as well as for moving towards "greener" solutions in telecommunications. Therefore, it is important to extend existing testbed experimentation tools and develop new ones, in order to equip cognitive testbeds with such advanced monitoring capabilities. In this work, we present a monitoring procedure that has been directly integrated in the experimentation tools of the CREW testbed federation and demonstrate how it aids in the online evaluation of four different cognitive platforms in terms of the aforementioned metrics.

In this paper, we present an implementation design of a TDMA protocol for the canonical diamond-topology network containing a source, two relays and a destination (single unicast session). Getting inspired by the established Lyapunov-methodology, we propose an online strategy for the relay selection/scheduling problem. In contrast to existing works, we implement this strategy inside the proposed TDMA protocol in order to operate over a CSMA enabled Wi-Fi infrastructure-less network. We elaborate a network controller within the TDMA frame to solve a global optimization problem at each time slot in a centralized manner. In our formulation, we consider the class of scheduling policies that select concurrently a non-interfering subset of links. Our architecture is tailored to achieve the objectives of stabilizing the network and either maximizing throughput or minimizing the total power consumption. Our scheme has been implemented and tested thoroughly through experimentation in the NITOS wireless testbed by exploiting Wi-Fi technology features. The results revealed significant increase in networking efficiency for throughput maximization.

Queue-based cross-layer optimization algorithms have recently been a subject of intensive research in wireless networks. Their purpose is to guarantee stable operation and to achieve some form of fairness among users, whenever the traffic demand exceeds network capacity. Despite the plethora of work in this field, the scenario where one or more nodes declare false queue backlog values in order to gain throughput advantage remains unexplored. In this paper we examine this type of selfish misbehavior, concentrating on a specific class of algorithms, the so-called quadratic Lyapunov-function-based algorithms (QLA). In particular, the effect of backlog misreporting on a single-hop access network with contending stations is evaluated through simulations. A simple framework for the detection of misbehaving nodes is proposed, under the assumption that the access-point is aware of the utility functions of the stations. The detection approach exploits the fact that under QLA the throughput of a node must be approximately equal to an “expected” value, derived from the reported queue backlogs.

Relay-assisted cooperative wireless communication has been shown to have significant performance gains over the legacy direct transmission scheme. Compared with single relay based cooperation schemes, utilizing multiple relays further improves the reliability and rate of transmissions. Distributed space-time coding (DSTC), as one of the schemes to utilize multiple relays, requires tight coordination between relays and does not perform well in a distributed environment with mobility. In this paper, a cooperative medium access control (MAC) layer protocol, called STiCMAC, is designed to allow multiple relays to transmit at the same time in an IEEE 802.11 network. The transmission is based on a novel DSTC scheme called randomized distributed space-time coding (R-DSTC), which requires minimum coordination. Unlike conventional cooperation schemes that pick nodes with good links, STiCMAC picks a transmission mode that could most improve the end-to-end data rate. Any station that correctly receives from the source can act as a relay and participate in forwarding. The MAC protocol is implemented in a fully decentralized manner and is able to opportunistically recruit relays on the fly, thus making it robust to channel variations and user mobility. Simulation results show that the network capacity and delay performance are greatly improved, especially in a mobile environment.

The ultimate success of the Wireless Mesh Network paradigm (WMN) in large scale deployments depends on the ability to test it in real world scenarios. A typical application scenario which is worth to be investigated in such a context is peer-to-peer traffic management. The creation of large scale testbeds for evaluating wireless mesh technologies and protocols, and for testing their ability to support real world applications in realistic environments, is then a crucial step. OMF (cOntrol and Management Framework) is a well-established control, measurement, and management framework for wireless testbeds. In this paper we present how we integrated an OMF-based wireless testbed in the planetary-scale PlanetLab testbed, making it possible for PlanetLab users to run experiments spanning on both PlanetLab and an OMF-based wireless testbed. In order to demonstrate the usefulness of such an integrated scenario, we tested on it an innovative peer-to-peer traffic optimization technique for the BitTorrent file sharing application. The possibility of running this kind of experiments highlighted several real-world issues which could be investigated thanks to our hybrid experimental scenario.

Efficient association of a station with the appropriate access point has always been a challenging problem. The standard approach of considering only the Received Signal Strength, has recently been substituted by more efficient schemes that consider channel conditions, cell population etc. However, in spite of the large variety of approaches, several factors that determine to a large extent user throughput after association with an access point have been overlooked. In this work, we propose innovative metrics on which association should be based. First, we capture the contention from one-hop and interference from two-hop neighbors that is inherent in IEEE 802.11 WLAN environments. Second we include the PHY transmission rate and show preference to higher rates that reduce the above effects. Third, unlike most relevant approaches, we define an activity factor that reveals the anticipated activity due to backlogged traffic. We devise an association protocol suite, through which messages containing the information above are passed between the AP and the user to support association decisions for the uplink and downlink. We implement the proposed mechanism using the MAD-WiFi open source driver and moreover show through experiments in a wireless testbed that it significantly improves user performance in real conditions.

The performance unfairness problem in a single cell IEEE 802.11 wireless local area network (WLAN) is considered. While existing research is based on the assumption that all nodes have the same transmission success probability and per-node throughput, this fairness exists only if all nodes within range of the access point can sense each other. Recent measurements suggest that this is not necessarily true and terminals can be hidden from each other. In this paper, the impact of hidden terminals on the performance unfairness among individual nodes is investigated via analysis, simulation and experimental measurements in a real network. In the presence of hidden terminals, it is observed that the widely accepted conclusion of equal performance among nodes does not hold any more. Instead, nodes far from the access point (AP) see more hidden terminals than those close to the AP, so they get more packet losses and lower throughput. This phenomenon is not due to inter-cell interference, or channel disparities among nodes, and is significant even when the RTS/CTS mechanism designed to mitigate the impact of hidden terminals is turned on. The simulation results show that for a 16-node WLAN with a fixed data rate of 6 Mbps, the throughput of a node close to the AP is more than twice that of an edge node, due to hidden terminals.

Physical (PHY) layer cooperation in a wireless network allows neighboring nodes to share their communication resources in order to create a virtual antenna array by means of distributed transmission and signal processing. A novel medium access control (MAC) protocol, called CoopMAC, has been recently proposed to integrate cooperation at the PHY layer with the MAC sublayer, thereby achieving substantial throughput and delay performance improvements. CoopMAC capitalizes on the broadcast nature of the wireless channel and rate adaptation, recruiting a single relay on the fly to support the communication of a particular source-destination pair. In this paper, we propose a cross-layer rate-adaptive design that opportunistically combines the recruitment of multiple cooperative nodes and carrier sensing multiple access with collision avoidance. We focus on a single-source single-destination setup, and develop a randomized cooperative framework, which is referred to as randomized CoopMAC (RCoopMAC). Thanks to the randomization of the coding rule, the RCoopMAC approach enables the blind participation of multiple relays at unison relying only on the mean channel state information (CSI) of the potential cooperating nodes, without introducing additional signaling overhead to coordinate the relaying process. The proposed RCoopMAC scheme is not only beneficial in substantially improving the link quality and therefore the sustainable data rates but, thanks to the decentralized and agnostic coding rule, it also allows to effectively recruit multiple relays in a robust fashion, i.e., even when the required mean CSI is partially outdated.

Wireless video multicast enables delivery of popular events to many mobile users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. In this paper, an integration of layered video coding, packet level forward error correction, and two-hop relaying is proposed to enable efficient and robust video multicast in infrastructure-based wireless networks. First, transmission with conventional omni-directional antennas is considered where relays have to transmit in non-overlapping time slots in order to avoid collision. In order to improve system efficiency, we next investigate a system in which relays transmit simultaneously using directional antennas. In both systems, we consider a non-layered configuration, where the relays forward all received video packets and all users receive the same video quality, as well as a layered setup, where the relays forward only the base-layer video. For each system setup, we consider optimization of the relay placement, user partition, transmission rates of each hop, and time scheduling between source and relay transmissions. Our analysis shows that the non-layered system can provide better video quality to all users than the conventional direct transmission system, and the layered system enables some users to enjoy significantly better quality, while guaranteeing other users the same or better quality than direct transmission. The directional relay system can provide substantial improvements over the omni-directional relay system. To support our results, a prototype is implemented using open source drivers and socket programming, and the system performance is validated with real-world experiments.

Video multicast over wireless local area networks (WLANs) faces many challenges due to varying channel conditions and limited bandwidth. A promising solution to this problem is the use of packet level forward error correction (FEC) mechanisms. However, the adjustment of the FEC rate is not a trivial issue due to the dynamic wireless environment. This decision becomes more complicated if we consider the multi-rate capability of the existing wireless LAN technology that adjusts the transmission rates based on the channel conditions and the coverage range. In order to explore the above issues we conducted an experimental study of the packet loss behavior of the IEEE 802.11b protocol. In our experiments we considered different transmission rates under the broadcast mode in indoor and outdoor environments. We further explored the effectiveness of packet level FEC for video multicast over wireless networks with multi-rate capability. In order to evaluate the system quantitatively, we implemented a prototype using open source drivers and socket programming. Based on the experimental results, we provide guidelines on how to efficiently use FEC for wireless video multicast in order to improve the overall system performance. We show that the Packet Error Rate (PER) increases exponentially with distance and using a higher transmission rate together with stronger FEC is more efficient than using a lower transmission rate with weaker FEC for video multicast.

Wireless video multicast enables delivery of popular events to many mobile users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. A promising solution to this problem is the use of packet level (FEC) mechanisms. However, the adjustment of the FEC rate is not a trivial issue due to the dynamic wireless environment. This decision becomes more complicated if we consider the multi-rate capability of the existing wireless LAN technology that adjusts the transmission rates based on the channel conditions and the coverage range. In this paper, we explore the dynamics of Forward Error Correction (FEC) schemes in multi-rate wireless local area networks. We study the fundamental behavior of a 802.11g network which already has embedded error correction in physical layer, under unicast and broadcast modes in a real outdoor environment. We then explore the effectiveness of packet level FEC over wireless networks with multi-rate capability. In order to evaluate the system quantitatively, we implemented a prototype using open source drivers, and ran experiments. Based on the experimental results, we provide guidelines on how to efficiently use FEC for wireless multicast services in order to improve the overall system performance. We argue that even there is a physical layer error correction, using a higher transmission rate together with stronger FEC is more efficient than using a lower transmission rate with weaker FEC for multicast.

Efficient channel selection is essential in 802.11 mesh deployments, for minimizing contention and interference among co-channel devices and thereby supporting a plurality of QoS-sensitive applications. In this paper, we propose ARACHNE, a routing-aware channel selection protocol for wireless mesh networks. ARACHNE is distributed in nature, and motivated by our measurements on a wireless testbed. The main novelty of our protocol comes from adopting a metric that captures the end-to-end link loads across different routes in the network. ARACHNE prioritizes the assignment of low-interference channels to links that (a) need to serve high-load aggregate traffic and/or (b) already suffer significant levels of contention and interference. Our protocol takes into account the number of potential interfaces (radios) per device, and allocates these interfaces in a manner that efficiently utilizes the available channel capacity. We evaluate ARACHNE through extensive, trace-driven simulations. We observe that our protocol improves the total network throughput, as compared to three other channel allocation strategies.

Cooperative communication is a technique that can be employed to meet the increased throughput needs of next-generation WiMAX systems. In a cooperative scenario, multiple stations can jointly emulate the antenna elements of a multi-input multi-output (MIMO) system in a distributed fashion. Although distributed space-time coding (DSTC) is being considered by the IEEE 802.16j/16 m standards for spatial diversity gain, it has several inherent drawbacks. These are addressed in the recently invented randomized distributed space-time coding, called R-DSTC. In this paper, we present the framework for the R-DSTC technique in the emerging relay-assisted WiMAX network, and develop a cooperative medium access control (MAC) layer protocol, called CoopMAX, for R-DSTC deployment in an IEEE 802.16 system. Our scheme couples the MAC layer with the physical (PHY) layer for performance optimization. The PHY layer yields significant diversity gain, while the MAC layer achieves a substantial end-to-end throughput gain. Through extensive simulations, we evaluate the performance of CoopMAX and show that it can generate capacity gains of up to about 77% for an IEEE 802.16 network.

Wireless video multicast enables delivery of popular events to many wireless users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. In our previous work, we integrated layered video coding with cooperative communication to enable efficient and robust video multicast in infrastructure-based wireless networks. Through simulation and analysis, we showed that cooperative multicast improves the multicast system performance and the coverage area. In this work, we integrate the proposed system with packet level forward error correction (FEC) and evaluate the viability of the system in a realistic environment. We implement the system at the MAC layer and report the experimental results in a medium size (i.e., 8 stations) testbed. The experimental results confirm that the new cooperative MAC protocol for multicast, delivers superior performance.

Medium access control (MAC) is a key issue for the efficiency of IEEE 802.11 wireless local area network. Legacy 802.11 DCF, using CSMA/CA and a random back-off mechanism, provides a simple-to-implement solution, however it achieves very low throughput. Exploiting the existence of a central entity in the infrastructure mode of an 802.11 wireless network, we propose a new centralized scheduling-based MAC protocol which is based on the popular DCF mechanism. This simple yet efficient scheme illustrates a new paradigm for realistic medium access control protocol design for next generation wireless networks. We have evaluated the performance of the proposed protocol by extensive simulations in different scenarios of wireless networks. Simulation results show that the new protocol significantly improves the network performance in terms of throughput and medium access delay.

The user association mechanism specified by the IEEE 802.11 standard does not consider the channel conditions and the AP load in the association process. Employing the mechanism in its plain form in wireless mesh networks we may only achieve low throughput and low user transmission rates. In this paper we design a new association framework in order to provide optimal association and network performance. In this framework we propose a new channel-quality based user association mechanism inspired by the operation of the infrastructure-based WLANs. Besides, we enforce our framework by proposing an airtime-metric based association mechanism that is aware of the uplink and downlink channel conditions as well as the communication load. We then extend the functionality of this mechanism in a cross-layer manner taking into account information from the routing layer, in order to fit it in the operation of wireless mesh networks. Lastly, we design a hybrid association scheme that can be efficiently applied in real deployments to improve the network performance. We evaluate the performance of our system through simulations and we show that wireless mesh networks that use the proposed association mechanisms are more capable in meeting the needs of QoS-sensitive applications.

In the next-generation WiMAX system, cooperative communication is being considered as an advanced technique to increase the throughput and improve the signal quality. In a cooperative scenario, multiple stations can jointly emulate the antenna elements of a multi-input multi-output (MIMO) system in a distributed fashion. Unlike conventional space-time coding (STC) mechanisms used by a IEEE 802.16e antenna array, distributed space-time coding (DSTC) is employed across the cooperating stations to achieve a higher spatial diversity gain. In this paper, we present the framework for DSTC in the emerging relay-assisted WiMAX network, and develop a cooperative MAC layer protocol, called CoopMAX, for DSTC deployment in a WiMAX system. Through extensive simulations, we evaluate the performance of CoopMAX and show that DSTC can yield capacity gains of up to about 50% for the uplink of an IEEE 802.16 network.

In a distributed wireless network, it is possible to employ several relays and mimic a multiple antenna transmission system. In this paper we propose a MAC layer solution that allows multiple relays to send information to the receiver at unison, using a randomized distributed space time code. The randomized space-time coding can recruit relays on the fly, thus significantly reducing signaling overhead. The cross-layer design between physical layer and MAC layer involves relay discovery and rate adaptation, and results in improvements in throughput and delay performance. The design is dynamic and can be adapted to changing network conditions. The proposed MAC scheme can be integrated into various wireless technologies such as distributed contention based networks (e.g., IEEE 802.11 BSS and ad hoc mode) as well as centralized multiple access networks (e.g., IEEE 802.16).

Wireless video multicast enables delivery of popular events to many wireless users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. We propose to integrate layered video coding with cooperative communication to enable efficient and robust video multicast in infrastructure-based wireless networks. We determine the user partition and transmission time scheduling that can optimize a multicast performance criterion.

As the two key technologies that have the potential to reshape the landscape of next-generation wireless network, cooperative communications and directional antenna system so far have been developed in parallel, if not in isolation from each other. In order to establish a thorough comparison between the relative system performance of cooperative diversity and directional transmission in an adhoc environment, we design and quantitatively evaluate three medium access control protocols, namely O-CoopMAC, D-NoopMAC and D-CoopMAC. The study yields the unexpected yet crucial observation that cooperative forwarding significantly limits the spatial reuse created by transmission directionality, and therefore can appreciably degrade the performance of a directional system with a sufficiently narrow antenna beam. To the best knowledge of the authors, this is the first paper to systematically compare the performance of these two technologies in an adhoc environment and reveal the key fact that cooperation and directionality can be rather foes than friends!

In this paper, we explore the use of directional antennas in relay transmission to improve the performance of video multicast with omni-directional relays in infrastructure- based wireless networks. We describe the system setup with directional relays and determine the user partition along with transmission time scheduling that can optimize a multicast performance criterion. We demonstrate that directional relays significantly improve the multicast system performance compared to omni-directional relays. Furthermore, it also provides larger coverage area.

Due to the unreliable nature of the wireless medium, provisioning of quality of service (QoS) in wireless LANs is far more complicated than in wired networks. In order to address this challenge, IEEE 802.11e defines a framework for QoS support where packets are prioritized based on their traffic characteristics. In this paper, we propose two new QoS support schemes. One is based on a ldquouser centricrdquo approach and the other on a ldquopacket content basedrdquo approach. The new mechanisms, in addition to the traffic itself, take into consideration the identification of the station that generates the traffic or the content of the traffic. Therefore, they use a second prioritization level on top of the one that is implemented in IEEE 802.11e. In the ldquouser centricrdquo approach, the mechanism defines groups of stations based on their MAC addresses and assigns different priorities to different groups. Under this classification, stations are served based on the prioritization of the group they belong. Among stations with same priority, traffic is scheduled based on the priorities given by 802.11e. On the other hand, in the case of the ldquopacket content basedrdquo approach, the mechanism defines groups of words or phrases with their respective priority. A packet that includes words of a specific group is scheduled based on the priority that the particular content defines. The new schemes are simple yet efficient, since they are adapted to the realistic needs of todaypsilas WiFi networks. In order to evaluate the performance of these proposed schemes, we implement them using open source drivers in a Linux platform. We run experiments in a medium-size testbed. Experimentation results clearly demonstrate the performance superiority of the new schemes, as compared to the legacy IEEE 802.11e.

Cooperation in wireless networks has shown significant performance gains in comparison to legacy wireless networks. Cooperative wireless protocols achieve such efficiency by enabling cooperation among nodes to exploit spatial diversity. CoopMAC is a medium access control (MAC) protocol that enables cooperation by using an intermediate node as a helper to a direct communication under poor channel conditions. The helper is typically located in a position where it experiences a good channel with both the source and destination. Therefore, it increases the efficiency of the communication by forwarding a packet from the source to the destination using high transmission rates. In an earlier attempt, we demonstrated the benefits of cooperation at the MAC layer by implementing the CoopMAC protocol using an open source wireless driver platform. However, due to some limitations posed by the hardware, the full potential of the protocol could not be explored. In this paper, we proceed with a complete implementation of the cooperative MAC protocol using an OFDM based software defined radio (SDR) platform. We investigate the benefits of the SDR approach, describe the details of the implementation, as well as the experiments we run in order to evaluate the protocol. Experimental results show that CoopMAC can easily be implemented and can lead to a significant improvement in the performance of wireless networks.

Dense deployments of hybrid WLANs result in high levels of interference and low end-user throughput. Many frequency allocation mechanisms for WLANs have been proposed by a large body of previous studies. However, none of these mechanisms considers the load that is carried by APs in terms of channel conditions, number of affiliated users as well as traffic-load, in conjunction. In this paper, we propose LAC, a load-aware channel allocation scheme for WLANs, which considers all the above performance determinant factors. LAC incorporates an airtime cost metric into its channel scanning process, in order to capture the effects of these factors and select the channel with the maximum long-term throughput. We evaluate LAC through extensive OPNET simulations, for many different traffic scenarios. Our simulations demonstrate that LAC outperforms other frequency allocation policies for WLANs in terms of total network throughput by up to 135%.

In 802.11-based wireless networks the stations (STAs) are associated with the available access points (APs) and communicate through them. In traditional handoff schemes the STAs get information about the active APs in their neighborhood by scanning the available channels and listen to transmitted beacons. This paper proposes a 802.11k compliant framework for cooperative handoff where the STAs are informed about the active APs by exchanging information with neighboring STAs. In this way we minimize the delay of the scanning procedure. We evaluate the performance of our mechanisms through simulations and we show that when our cooperative framework is applied the network performance is significantly improved. Consequently, our system is more capable in meeting the needs of QoS-sensitive applications.

Due to the unreliable nature of the wireless medium, provisioning of the quality of service (QoS) in wireless LANs is by far more complicated than in wired networks. In this demo we show the efficiency of a new QoS support scheme that is based on an user centric approach. The new mechanism takes under consideration the identification of the station that generates the traffic additionally to the traffic itself. Therefore, it uses a second prioritization level on the top of the one that is implemented in IEEE 802.11e. It first defines groups of stations based on their MAC addresses and it assigns different priorities to different groups. Under this classification, stations are served based on the prioritization of the group they belong. Among stations with same priority, traffic is scheduled based on the priorities given by 802.11e. The new scheme is implemented using open source drivers and 802.11g WiFi cards. In the demo, a video clip is streamed from a server to a client with high priority under heavy traffic load. The underlying wireless transport alternates between the classic IEEE 802.11e and the new prioritization scheme. The new scheme delivers a smooth and jitter-free user experience, while the video playout over IEEE 802.11e experiences noticeable jitter and frequent distortions. The demo clearly demonstrates the performance superiority of the new implemented scheme, as compared to the legacy IEEE 802.11e.

In multicast/broadcast services over infrastructure- based/cellular wireless networks (e.g. 3G cellular networks, WiMax, DVB), data is transmitted to multiple recipients from an access point/base station. Multicast greatly improves the network efficiency to distribute data to multiple recipients as compared to multiple unicast sessions of the same data to each receiver individually, by taking advantage of the shared nature of the wireless medium. However it is difficult to guarantee the reception reliability of multiple multicast/broadcast recipients because the wireless medium is error prone and each receiver experiences different channel conditions. An additional difficulty is that multicast/broadcast services in many networks such as 3G multimedia multicast services do not provide a reverse communications channel for the receivers to request the retransmission of lost data packets. This research proposes a novel method to provide QoS support by using an assistant network to recover the loss of multicast data in the principal network. Wireless devices are connected to the principal network to receive the multicast data. A wireless device may lose some of the multicast data sent over the principal network. The wireless devices form an assistant network to recover the lost multicast data cooperatively from their peers. The performance of this recovery mechanism has been investigated using extensive simulation experiments.

The IEEE 802.16J protocol for a multi-hop relay (MMR) WiMAX network is being developed to increase data rates and extend service coverage as an enhancement of existing WiMAX standards. The IEEE 802.16J protocol supports transparent and non-transparent modes. In the transparent mode, only data traffic is relayed by an intermediate relay station (RS) between a mobile station (MS) and the base station (BS), while in the non-transparent mode, both signaling and data traffic are forwarded by RSs. Furthermore, non-transparent mode is either distributed or centralized with regard to scheduling. The difference between them resides in that distributed scheduling enables RSs to participate in bandwidth allocation (BWAlloc), while centralized scheduling leaves all BWAlloc coordinated by the BS. In this paper, we propose a novel multi-hop polling service (mPS) for non-transparent centralized scheduling in a multi- hop 802.16J environment. Our model is adaptive to the traffic pattern so as to provide bandwidth efficiency over access and relay links. Besides, aggregation of bandwidth requests (BWReq) from MSs is conducted at the RS to further save bandwidth. The performances of mPS with BWReq aggregation is evaluated via simulations which demonstrate our approach outperforms the current multi-hop bandwidth request mechanism in terms of overall spectrum efficiency.

Cooperative communications is a promising technology that tends to change the conventional access and transmission schemes in wireless networks. By enabling additional collaboration from nodes that otherwise will not directly participate in the transmission, it enables spatial diversity and dramatically improves the performance of the network. In this paper we propose a cross-layer cooperative protocol based on a MAC protocol called CoopMAC (Liu et al., 2005; Korakis et al., 2007; and Liu et al., 2006) for ad-hoc wireless networks in order to leverage cooperation in both MAC and PHY layer. Exploiting physical layer combining at the receiver, this simple yet efficient scheme illustrates a new paradigm for realistic cross-layer cooperative protocol design for next generation wireless ad-hoc networks. We have evaluated the performance of the proposed protocol by extensive simulations in a large scale wireless ad-hoc network. Simulation results show that the new protocol significantly improves the network performance in terms of throughput and delay.

In this paper, we propose a new Medium Access Control (MAC) protocol for full exploitation of directional antennas in wireless networks. The protocol introduces a circular directional transmission of the Request To Send (RTS) control packet, spreading around a station information about the intended communication. The stations that receive the directional RTS, using a simple scheme of tracking the neighbors' directions, defer their transmission toward the beams that could harm the ongoing communication. In this way, the proposed protocol takes advantage of the benefits of directional transmissions as the increase of spatial reuse and of coverage range. Additionally, it reduces the hidden-terminal problem, as well as the deafness problem, two main factors for the decrease of the efficiency of directional transmissions in ad hoc networks. The performance evaluation of the protocol shows that it offers a significant improvement in static, as well as mobile, scenarios, as compared to the performance of the proposed protocols that use omnidirectional or directional transmissions.

In IEEE 802.11-based wireless mesh networks a user is associated with an access point (AP) in order to communicate and be part of the overall network. The association mechanism specified by the IEEE 802.11 standard does not consider the channel conditions and the AP load in the association process. Employing the mechanism in its plain form in wireless mesh networks we may only achieve low throughput and low user transmission rates. In this paper, we propose an association mechanism that is aware of the uplink and downlink channel conditions. We introduce a metric that captures the channel conditions and the load of the APs in the network. The users use this metric in order to optimally associate with the available APs. We then extend the functionality of this mechanism in a cross-layer manner taking into account information from the routing layer. The novelty of the mechanism is that the routing QoS information of the back haul is available to the end users. This information can be combined with the uplink and downlink channel information for the purpose of supporting optimal end-to-end communication and providing high end-to-end throughput values. We evaluate the performance of our system through simulations and we show that 802.11-based mesh networks that use the proposed association mechanism are more capable in meeting the needs of QoS-sensitive applications.

Due to the broadcast nature of wireless signals, a wireless transmission intended for a particular destination station can be overheard by other neighboring stations. A focus of recent research activities in cooperative communications is to achieve spatial diversity gains by requiring these neighboring stations to retransmit the overheard information to the final destination. In this paper we demonstrate that such cooperation among stations in a wireless LAN (WLAN) can achieve both higher throughput and lower interference. We present the design for a medium access control protocol called CoopMAC, in which high data rate stations assist low data rate stations in their transmission by forwarding their traffic. In our proposed protocol, using the overheard transmissions, each low data rate node maintains a table, called a CoopTable, of potential helper nodes that can assist in its transmissions. During transmission, each low data rate node selects either direct transmission or transmission through a helper node in order to minimize the total transmission time. Using analysis, simulation and testbed experimentation, we quantify the increase in the total network throughput, and the reduction in delay, if such cooperative transmissions are utilized. The CoopMAC protocol is simple and backward compatible with the legacy 802.11 system. In this paper, we also demonstrate a reduction in the signal-to-interference ratio in a dense deployment of 802.11 access points, which in some cases is a more important consequence of cooperation

In wireless LANs that provide multi-rate support (IEEE 802.11a, 802.11b), stations that experience poor channel quality tend to use low transmission rates to reduce the bit-error-rate (BER) of each transmission. This phenomenon usually leads to a throughput fairness problem between the stations with good channel quality and those without. This fairness problem has been shown to result in throughput degradation for the whole network [8]. The MAC protocol proposed in [5] addresses this issue using an efficient cooperative scheme. Under this scheme, low rate stations are assisted by a high rate station, referred to as helper stations, in its transmissions. With such assistance, the low rate station will be able to transmit data at a higher rate in a two-hop manner using the helper station. We implemented this new protocol in a Linux testbed. This paper describes the assumptions, the implementation process and the challenges we were presented with. We evaluated the protocol using our testbed through experiments. The implementation of the protocol shows that it performs efficiently in supporting TCP applications.

The deployment of traditional higher layer protocols (especially the IEEE 802.11 MAC protocol at the MAC layer) with directional antennae could lead to problems from an increased number of collisions; this effect is primarily seen due to three specific effects: (i) an increase in the number of hidden terminals; (ii) the problem of deafness and, (iii) a difficulty in determining the locations of neighbors. In this work we propose a new MAC protocol that incorporates circular RTS and CTS transmissions. We show that the circular transmission of the control messages helps avoid collisions of both DATA and ACK packets from hidden terminals. Our protocol intelligently determines the directions in which the control messages ought to be transmitted so as to eliminate redundant transmissions in any given direction. We perform extensive simulations and analyze the obtained results in order to compare our scheme with previously proposed protocols that have been proposed for use in directional antenna equipped ad hoc networks. Our simulation results clearly demonstrate the benefits of incorporating both circular RTS and CTS messages in terms of the achieved aggregate throughput.

Directional antennas in ad hoc networks offer many benefits compared with classical omnidirectional antennas. The most important include significant increase of spatial reuse, coverage range and subsequently network capacity as a whole. On the other hand, the use of directional antennas requires new approach in the design of a MAC protocol to fully exploit these benefits. Unfortunately, directional transmissions increase the hidden terminal problem, the problem of deafness and the problem of determination of neighbors' location. In this paper we propose a new MAC protocol that deals effectively with these problems while it exploits in an efficient way the advantages of the directional antennas. We evaluate our work through simulation study. Numerical results show that our protocol offers significant improvement compared to the performance of omni transmissions.

The medium access control (MAC) protocol and the underlying traffic scheduling mechanism for the radio interface are critical components of a wireless ATM network, aiming to provide ATM-like quality of service (QoS) guarantees over the air. A traffic scheduling algorithm for a wireless ATM customer premises network is proposed. The algorithm operates in the mobile terminals, and complements a centralized scheduling mechanism operating in the base stations, for improved performance. The proposed algorithm is delay oriented to meet the requirements of the various traffic classes defined by the ATM architecture. The system performance improvement attained by the proposed algorithm is evaluated via simulations.