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.

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

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

Hierarchical federated learning (HFL) has attracted academic attention to improve the efficiency of federated learning (FL) in real-world applications, however, little research has been done to explore the structural advantages of HFL against Byzantine attacks and to investigate how to make HFL immune to top-level server Single Point of Failure (SPOF). To explore this field and improve the robustness of HFL, we propose a novel generalized paradigm ABD-HFL for asynchronous Byzantine-resistant decentralized hierarchical federated learning, a multi-tier structure without a central server for FL tasks with a large number of devices. Based on the layered structure, an innovative universal Byzantine resistance mechanism is designed in ABD-HFL, which enables it to apply a combination of multiple Byzantine robust techniques, making ABD-HFL more powerful than any single application of such techniques. Besides, ABD-HFL is a fully decentralized HFL, there is no central server, but rather multiple nodes at the top level agree on the global model where malicious model updates are excluded. A new concept of pipeline learning workflow is also introduced to study communication efficiency in ABD-HFL, which is based on asynchronous communication between various levels to train and propagate the global model. Our numerical evaluation validates the advantage of ABD-HFL in terms of robustness and communication efficiency.

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

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

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

In this paper we propose and evaluate an innovative algorithm that enables the creation of Peer-to-Peer network overlays characterized by emergent multi-hubs. This approach generates overlays that balance between the randomness of a graph and the structure of a star network, resulting in networks that not only feature prominent hubs but also exhibit strong resilience to failures. By leveraging principles of preferential attachment and random attachment, our method allows hubs to form spontaneously, offering a decentralized and fault-tolerant solution ideal for applications requiring both low network diameter and high robustness. The protocol is entirely decentralized, operates asynchronously, and depends exclusively on local information. Nodes organically evolve into hubs and remain indistinguishable from other nodes (except in terms of the number of incoming links). The quantity of hubs that emerge can be predetermined by the application as a network parameter.

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.

The enormous success of direct communication applications has shed light on the practical interest of Device-to-device (D2D) communications. However, to set up a direct link between two neighboring nodes, they have first to detect each other, which introduces a delay before they can start sending and receiving data. The link establishment delay can be particularly unfavorable in situations of strong mobility, as the availability of the direct communication link depends on how long the devices stay within communication range of each other. This paper reports on our experiments to evaluate the link establishment delay. We focus on Android devices and use the Nearby Connection Application Programming Interface (API), which supports Bluetooth Classic and Bluetooth Low Energy (BLE) to perform link connectivity. In a nutshell, we observe that the link establishment delay requires several seconds to complete in the case of Bluetooth Classic and even tens of seconds for BLE.

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.

An important evolution of the users’ needs is represented by the on-demand access to the network, storage, and compute resources in order to dynamically match the level of resource consumption with their service requirements. The response of the network providers is to transition to an architecture based on softwarization and cloudification of the network functions. This is the rationale for the deployment of network functions virtualization (NFV) where virtual network functions (VNFs) may be chained together to create network services. Efficient online routing of demand across nodes handling the functions involved in a given service chain is the novel problem that we address in this paper. We provide an original formulation of this problem that includes link and CPU capacity constraints and is based on the construction of an expanded network. We derive the exact mathematical formulation and propose several heuristic algorithms taking into account the main system’s parameters. We conclude by deriving some interesting insights both about the algorithms and the network performance by comparing the heuristics with the exact solutions.

Network Functions Virtualization (NFV) aims to support service providers to deploy various services in a more agile and cost-effective way. However, the softwarization and cloudification of network functions can result in severe congestion and low network performance. In this paper, we propose a solution to address this issue. We analyze and solve the online load balancing problem using multipath routing in NFV to optimize network performance in response to the dynamic changes of user demands. In particular, we first formulate the optimization problem of load balancing as a mixed integer linear program for achieving the optimal solution. We then develop the ORBIT algorithm that solves the online load balancing problem. The performance guarantee of ORBIT is analytically proved in comparison with the optimal offline solution. The experiment results on real-world datasets show that ORBIT performs very well for distributing traffic of each service demand across multipaths without knowledge of future demands, especially under high-load conditions.

Network service providers have to cope with the growing on-demand need from end-users as well as the diversity of usage. The softwerization and cloudification of the network components offer an interesting solution to achieve the agility necessary to dynamically match the requirement with the level of resource consumption. This materializes with the deployment of Network Functions Virtualization (NFV) where Virtual Network Functions (VNFs) may be chained together to create network services. This paper explores important design and architectural issues related to this approach. We study the resource allocation problem in an NFV system for minimizing its cost under constraints on interconnectivity among VNFs, system resources, and service requirements. We formalize the problem in a comprehensive manner taking into account a broad set of relevant parameters. The static (offline) and dynamic (online) cases are considered. We propose and analyze three heuristic algorithms: two for handling large dimensions of the offline problem and one designed to address the online scenario. The evaluation shows that our solutions outperform the state of the art [1] with respect to critical performance index. Finally, we focus on the online scenario, evaluate the impact of migrating a set of running demands, and propose a simple migration technique.

Gathering the required measurements to produce accurate results for mobile communications and wireless networking protocols, technologies and applications, relies on the use of expensive experimental computer networking facilities. Until very recently, large-scale testbed facilities have existed in separate silos, each with its own authentication mechanisms and experiment support tools. There lacked a viable federation model that reconciled the challenges posed by how to provide a single entry point to access heterogeneous and distributed resources, and how to federate these resources that are under the control of multiple authorities. The OneLab experimental facility, which came online in 2014, realizes this model, making a set of world-class testbeds freely available to researchers through a unique credential for each user and a common set of tools. We allow users to deploy innovative experiments across our federated platforms that include the embedded object testbeds of FIT IoT-Lab, the cognitive radio testbed of FIT CorteXlab, the wireless testbeds of NITOS-Lab, and the internet overlay testbed PlanetLab Europe (PLE), which together provide thousands of nodes for experimentation. Also federated under OneLab are the FUSECO Playground, which includes cloud, M2M, SDN, and mobile broadband; w-iLab.t wireless facilities; and the Virtual Wall testbed of wired networks and applications. Our demo describes the resources offered by the OneLab platforms, and illustrates how any member of the MobiCom community can create an account and start using these platforms today to deploy experiments for mobile and wireless testing.

The need for Internet access from moving vehicles has been steadily increasing in the past few years. Solutions that rely on cellular connectivity are becoming impractical to deploy due to technical and economic reasons. Car-Fi proposes an approach that leverages existing home Wi-Fi access points configured in dual-access mode, in order to offload all data traffic from the congested and expensive cellular infrastructure to whatever Wi-Fi network is available. Thanks to an improved scanning algorithm and numerous optimizations to the connection setup, Car-Fi makes downloading large amounts of data from a moving car feasible.

This paper proposes Navigo, a location based packet forwarding mechanism for vehicular Named Data Networking (NDN). Navigo takes a radically new approach to address the challenges of frequent connectivity disruptions and sudden network changes in a vehicle network. Instead of forwarding packets to a specific moving car, Navigo aims to fetch specific pieces of data from multiple potential carriers of the data. The design provides (1) a mechanism to bind NDN data names to the producers' geographic area(s); (2) an algorithm to guide Interests towards data producers using a specialized shortest path over the road topology; and (3) an adaptive discovery and selection mechanism that can identify the best data source across multiple geographic areas, as well as quickly react to changes in the V2X network.

Sharing available resources in cognitive radio networks can benefit from spectrum handoff to enhance the rate performance by switching from current unavailable channels to the available ones. However, spectrum handoff can cause transmission interruptions leading to the degradation of services. In this work, we aim to balance the tradeoff between benefits of spectrum handoff and their negative impacts on spectrum sharing. Therefore, we develop an adaptive hybrid strategy that includes novel static and dynamic spectrum sharing based essentially on a rate compensation concept. The former is suitable when spectrum handoff is not necessary. The latter allows performing spectrum handoff to compensate the lost rate from the unavailable periods and improve the rate performance. We compare our hybrid strategy with a fully dynamic one and an optimization framework. Through simulations, we show that our strategy reduces the number of handoffs significantly while the achieved rate is fulfilling compared to the optimal.

The global trend of Future Internet related experimentally driven research has gained a strong momentum. In this context sustainability and the best use of developed infrastructures are highly critical. While many facilities have reached a level of maturity that allows them to be opened up to a wider use, mainly the academic sectors have been targeted. The Fanning out Testbeds-as-a-Service for the EIT ICT1 (FanTaaStic) project explores best practices, has performed a gap analysis, investigates applicable business models and works out a concept for the operation of a self-sustainable commercial testbed service offering. For defining the operational model, an analysis of available tools and frameworks from past and current Future Internet testbed projects is being carried out, based on a standard framework that defines the required processes and applications for the carrier grade operation of telecommunication infrastructures and provisioning of services. The business model is defined as a gradually growing service offering, based on categories of a framework for IT Service Management in federated e-infrastructures. The applicability of both, the developed business and operational model for such a sustainable federation will initially be implemented and evaluated in 2014.

In this work, we propose a heuristic for dynamic spectrum sharing in cognitive radio networks. The concept of rate compensation is introduced so that cognitive radio users are able to achieve their rate requirement by performing adequately spectrum handoffs. Indeed, performing spectrum handoff can increase the achieved rate obtained by moving from unavailable channels to available ones. However, handoffs should also be reduced to decrease handoff delays and access contention in the network which can in turn impact the achieved rate.

In cognitive radio networks, spectrum sharing among secondary users can benefit from having more transmission opportunities through spectrum handoff. However, the spectrum sharing strategy can also increase negatively the number of performed handoffs. In this work, we study formally the impact of spectrum sharing on spectrum handoff and at the same time the impact of the spectrum handoff on the overall achievable rates from spectrum sharing. We base our study on three proposed spectrum sharing strategies: static, instantaneous and global. We show that depending on the network configuration, static and/or instantaneous can be almost as powerful as the global strategy. In case handoffs are necessary, the global strategy reduces them significantly. Furthermore, we analyze the benefits of multi-channel communications on the quality of sharing in terms of fairness, achieved rate and number of handoffs.

News articles are a captivating type of online content that capture a significant amount of Internet users' interest. They are particularly consumed by mobile users and extremely diffused through online social platforms. As a result, there is an increased interest in promptly identifying the articles that will receive a significant amount of user attention. This task falls under the broad scope of content popularity prediction and has direct implications in various contexts such as caching strategies or online advertisement policies. In this paper we address the problem of predicting the popularity of news articles based on user comments. We formulate the prediction task into a ranking problem where the goal is not to infer the precise attention that a content will receive but to accurately rank articles based on their predicted popularity. To this end, we analyze the ranking performance of three prediction models using a dataset of articles covering a four-year period and published by 20minutes.fr, an important French online news platform. Our results indicate that prediction methods improve the ranking performance and we observed that for our dataset a simple linear prediction method outperforms more dedicated prediction methods.

In Cognitive Radio Networks, secondary users are required to handoff from a wireless channel to another in order to cope with the dynamic spectrum environment imposed by primary users activity. Spectrum handoff may cause service interruptions or significant delays leading to the degradation of the quality of transmission. In this paper, we propose and study a novel spectrum handoff strategy aiming at reducing the unnecessary handoff operations while considering a delay bound requirement. First, the expected delay of application packets is computed as a function of the queue associated with a channel. This delay reflects the status of the channel and its usability for transmitting packets. Second, and in order to prevent the unnecessary handoffs, we use the expected delays to estimate a delay violation ratio that guides the spectrum handoff and selection decisions. Besides, we study the utility of using additional backup channels at the time of handoff to alleviate the problem of fast successive handoffs. We show through simulations that our strategy controls better the tradeoff between channel handoffs and achieving the delay requirement compared to random handoff and low channel occupancy handoff.

Abstract-This work is concerned with scaling decentralized content-based publish/subscribe (CBPS) networks for Internet-wide content distribution. A fundamental step for CBPS networks to reach the Internet-scale is to move from the exhaustive filtering service model, where a subscription selects every relevant publication, to a service model capturing the quantitative and qualitative heterogeneity of information consumers' requirements. In previous work, we described a service model allowing information consumers to express the maximum number of publications they would like to receive per service period and how to take advantage of such knowledge to pace the dissemination process. This paper extends by introducing a generic service model that seamlessly supports content-based information retrieval and dissemination and investigates through extensive simulations the performances of six caching policies in terms of consumers satisfaction and bandwidth usage.

Despite its existing incentives for leecher cooperation, BitTorrent file sharing fundamentally relies on the presence of seeder peers. Seeder peers essentially operate outside the BitTorrent incentives, with two caveats: slow downlinks lead to increased numbers of "temporary" seeders (who left their console, but will terminate their seeder role when they return), and the copyright liability boon that file segmentation offers for permanent seeders. Using a simple epidemic model for a two segment BitTorrent swarm, we focus on the BitTorrent rule to disseminate the (locally) rarest segments first. With our model, we show that the rarest-segment first rule minimizes transition time to seeder (complete file acquisition) and equalizes the segment populations in steady-state. We discuss how alternative dissemination rules may beneficially increase file acquisition times causing leechers to remain in the system longer (particularly as temporary seeders). The result is that leechers are further enticed to cooperate. This eliminates the threat of extinction of rare segments which is prevented by the needed presence of permanent seeders. Our model allows us to study the corresponding trade-offs between performance improvement, load on permanent seeders, and content availability, which we leave for future work.

The future of Vehicular Ad-Hoc Networks (VANET) will rely mainly on the support of efficient information dissemination protocols, whether we talk about safety applications that warn the driver of an imminent collision, a simple update on traffic conditions, or road-side advertisements. One of the greatest challenges when designing such protocols is how to deliver packets efficiently in a highly mobile environments under intermittent connectivity. Surprisingly, this problem has been under-investigated in the literature. In this paper, we propose, design, and evaluate SERVUS1, a robust dissemination protocol that guarantees packet propagation with high delivery ratio and low overhead. SERVUS includes a new broadcast management mechanism that takes advantage of the inherent behavioral properties of the VANET environment. In particular, the proposed protocol can update isolated nodes (or clusters) with missing information while ensuring homogeneous information dispersal at low overhead. Through a number of analyses, we show that SERVUS is highly efficient with regard to the tradeoff between reliability and cost, and overcomes important issues like the broadcast storm problem and the topological temporal fragility.

International audience

Is the temporal dimension alone sufficient to characterize contacts in opportunistic networks? Several studies analyze the temporal aspect of contacts with significant results concerning contact and inter-contact distributions. Nevertheless, only the temporal dimension does not give a complete overview of contact characterization. In this paper, we propose the surround indicator as a metric to exhibit the contact's surrounding environment in opportunistic networks. We evaluate the surround indicator on two existing datasets and show that contacts have too heterogeneous and too unstable surrounds to be considered only in terms of duration. Besides a large variability of the surrounding environment within the duration of a single contact, it is frequent to observe contacts of identical duration that exhibit differences in their surrounds of more than a hundred times.

The success of the IEEE 802.11 standard has pushed the research community to put a not negligible effort in the design, analysis, and development of new wireless solutions during the last decade. More recently, the apparition of the wireless mesh technology incited a number of wireless testbeds to be deployed in several countries, in both academic and industrial environments. The LIP6 laboratory of the Université Pierre et Marie Curie is a main actor in the context of wireless mesh networking and has deployed its own testbed. Custom wireless routers built up from off-the-shelf hardware and open source software form this testbed, which is called MeshDVNet. On this platform the LIP6 has designed and implemented the MeshDV demon, an integrated approach for routing and mobility management, which hides to end-users all the complexity of wireless mesh networks. MeshDV allows end-users to easily access the network without the constrain of embedding any kind of additional software (e.g. routing demon, protocol stack extensions).

In the widely used 802.11 standard, the so-called performance anomaly is a well-known issue. Several works have tried to solve this problem by introducing mechanisms such as packet fragmentation, backoff adaptation, or packet aggregation during a fixed time interval. In this paper, we propose a novel approach solving the performance anomaly problem by packet aggregation using a dynamic time interval, which depends on the busy time of the wireless medium. Our solution differs from other proposition in the literature because of this dynamic time interval, which allows increasing fairness, reactivity, and in some cases efficiency. In this article, we emphasize the performance evaluation of our proposal.

In age-based routing protocols, nodes forward data toward a position which is an estimation of the destination's coordinates. The fresher the estimation, the higher the probability to reach the destination. In such a class of solutions, it is fundamental that accurate estimations of nodes' positions be disseminated throughout the network in order for the forwarding algorithm to converge in a reasonable time and with as few hops as possible. In general, dissemination is done following uniform distributions. In this paper, we argue that coordinate dissemination should rely on measured parameters (thus adaptive to the network conditions), such as the mobility of a node. We investigate the impact of node locality on the performance of dissemination algorithms and propose a new algorithm that adjusts the dissemination level of a node's coordinates based on the displacements of the node. Through extensive simulations, we show that our proposal leads to both faster discovery and significant reduction of the overhead when compared to flat solutions.

As the size of mobile self-organizing networks increases, the efficiency of location services must increase as well so that addressing/routing scalability does not become an issue. In this paper, we propose a novel architecture, called Twins, tailored for self-organizing networks. Twins architecture involves addressing and locating nodes in large networks, forwarding packets between them, and managing in the presence of mobility/topology changes. Twins defines a logical multidimensional space for addressing and forwarding, while location service and management operations make use of a one-dimensional space. To improve scalability and performance, forwarding is hop-by-hop with greedy next-hop choice and the location service uses a rendezvous paradigm to distribute information among nodes. In this paper, we describe the Twins architecture and present a performance evaluation to assess scalability, fairness in the overhead distribution among nodes, and routing robustness.

Wireless Mesh Networks (WMNs) are an emerging architecture based on multi-hop transmission. ISPs considers WMNs as a potential future technology to offer broadband Internet access. Therefore, in WMNs, the throughput capacity of the wireless backbone becomes a key factor, limiting the scalability in terms of users able to effectively take advantage of the network. Increasing the effective throughput capacity will support WMNs become a very cost-effective solution for wireless ISPs.Based on the analysis of the theoretical capacity bound derived by Gupta et al., we propose MRS (Mesh Routing Strategy), a novel cross layer routing solution. MRS is specifically designed for Wireless Mesh Networks, where routing is challenging due to the unreliable wireless medium. Traditional routing paradigms are not able to overcome this issue, resulting in an average throughput experienced by the network far lower than the theoretical throughput capacity bound. This paper shows how a cross-layer approach will consistently improve the throughput experienced by large-scale multi-hop networks. We show through simulation that our proposal increases throughput while reducing the average transmitted power. Furthermore, MRS limits interference and gets closer to the theoretical throughput capacity bound.

The need for efficient location mechanisms is an important issue in scalable self-organizing networks. Existing solutions are inherently dependent on the spatial distribution of nodes in the topology. This leads to limitations that go against the principles of self-organization. In this paper, we propose Twins, an easily-managed location service for self-organizing networks. Twins defines a logical multidimensional space that is a strict mathematical representation of the network geographic space. This representation is obtained through Hilbert space-filling curves. The geographic space is used for addressing and routing, while localization is based on the curve. Control messages are routed based on the logical structure while data packets are routed in a hop-by-hop basis with greedy next-hop choice. In this paper, we evaluate the Twins management operations in terms of fairness of space sharing and logical/geographic distances between nodes and their location servers. Our results show that Twins assures a fair distribution of control overhead and scales well with the number of nodes.

Geolocation of Internet hosts enables a diverse and interesting new class of location-aware applications. Previous measurement-based approaches use reference hosts, called landmarks, with a well-known geographic location to provide the location estimation of a target host. This leads to a discrete space of answers, limiting the number of possible location estimates to the number of adopted landmarks. In contrast, we propose Constraint-Based Geolocation (CBG), which infers the geographic location of Internet hosts using multilateration with distance constraints, thus establishing a continuous space of answers instead of a discrete one. CBG accurately transforms delay measurements to geographic distance constraints, and then uses multilateration to infer the geolocation of the target host. Our experimental results show that CBG outperforms the previous measurement-based geolocation techniques. Moreover, in contrast to previous approaches, our method is able to assign a confidence region to each given location estimate. This allows a location-aware application to assess whether the location estimate is sufficiently accurate for its needs.

The deployment of a geographic location service for Internet hosts enables a whole new class of location-aware applications. We focus on a technique that infers host locations using delay measurements to geographically distributed landmarks, which are hosts with a known geographic location. The problem we deal with is where to place such landmarks and the probe machines that perform the delay measurements. We propose a demographic placement approach to improve the representativeness of each landmark with respect to the hosts to be located. Results show that a relatively small number of landmarks is sufficient to cover the most part of hosts to be located. For a fixed number of landmarks, the demographic approach reduces the distances from most hosts to the nearest landmark. Considering the probe machines, we show that they have to be sparsely placed to avoid gathering redundant data.

This paper proposes the Tribe protocol, an indirect routing strategy for wireless self-organizing networks. The protocol is intended to be applied in environments with large number of users, where mobility is taken into account, and the correct operation of the system does not require the support of a fixed (wired or wireless) infrastructure. In Tribe, nodes build a network infrastructure which describes the node's relative location according to the current node's neighborhood. Furthermore, routing is unique and completely independent of any global connectivity ensured by a network-level routing protocol. The architecture is generic, self-organizing, and independent of IP-like addressing limitations.

Quality of service (QoS) based routing provides QoS guarantees to multimedia applications and an efficient utilization of the network resources. Nevertheless, QoS routing is likely to be a costly process that does not scale when the number of nodes increases. Thus, the routing algorithm must be simple. This paper proposes and analyses the performance of a link-state QoS routing algorithm that takes into account three metrics: propagation delay, available bandwidth, and loss probability. A heuristic based on the residual loss probability and metric-combination is used to turn the algorithm scalable and solvable in polynomial time. The simulation results show that our algorithm is a promising solution to construct paths constrained on three metrics.

This paper compares the performance of tail drop and three different flavors of the RED (random early detection) queue management mechanism: RED with a standard parameter setting, RED with an optimized parameter setting based on a model of RED with TCP flows, and finally a version of RED with a smoother drop function called "gentle RED ". The performance is evaluated under various load situations for FTP-like and Web-like flows, respectively. We use measurements and simulations to evaluate the performance of the queue management mechanisms and assess their impact on a set of operator oriented performance metrics. We find that in total (i) no performance improvements of RED compared to tail drop can be observed; (ii) fine tuning of RED parameters is not sufficient to cope with undesired RED behavior due to the variability in traffic load; and (iii) gentle RED is capable of resolving some of the headaches on RED but not all.

The IP multicast architecture was not widely deployed because multicast address allocation is difficult and there is no scalable solution to inter-domain multicast routing. Hence, there is an interest in developing protocols that allow the progressive deployment of the multicast service by supporting unicast clouds. This paper proposes HBH (hop-by-hop multicast routing protocol). HBH adopts the source-specific channel abstraction to simplify address allocation and implements multicast distribution using recursive unicast trees. In this model, data packets have unicast destination addresses. Therefore, HBH supports pure unicast routers transparently. The branching-nodes recursively create packet copies to implement the distribution. HBH constructs a shortest-path tree even in the presence of asymmetric unicast routing. Consequently, HBH provides best routes in asymmetric networks, and is suitable for an eventual implementation of QoS-based routing. Additionally HBH reduces tree bandwidth consumption in asymmetric networks when compared to other approaches. The results obtained from simulation support our statements.

IP Multicast is facing a slow take-off although it is a hotly debated topic since more than a decade. Many reasons are responsible for this status. Hence, the Internet is likely to be organized with both unicast and multicast enabled networks. Thus, it is of utmost importance to design protocols that allow the progressive deployment of the multicast service by supporting unicast clouds. This paper proposes HBH (Hop-By-Hop multicast routing protocol). HBH adopts the source-specific channel abstraction to simplify address allocation and implements data distribution using recursive unicast trees, which allow the transparent support of unicast-only routers. Additionally, HBH is original because its tree construction algorithm takes into account the unicast routing asymmetries. As most multicast routing protocols rely on the unicast infrastructure, these asymmetries impact the structure of the multicast trees. We show through simulation that HBH outperforms other multicast routing protocols in terms of the delay experienced by the receivers and the bandwidth consumption of the multicast trees.

This paper points out a phase effect with RED-like queue management algorithms and two-way TCP traffic. It is shown by simulation and measurement that the RED (random early detection) queue converges to a state of heavy oscillation in the presence of two-way bulk-data TCP traffic, causing suboptimal link utilization and service differentiation between in-profile and out-of-profile packets. Besides investigating the reasons for the oscillatory behavior, we show that the oscillations persist in scenarios with two-way Web-like TCP traffic and disappear in the case of significant amounts of cross traffic and/or usage of drop-tail instead of RED.

Increasing uncontrolled best-effort traffic deteriorates the ability of TCP to control congestion and is a source of high drop rates. This paper proposes an efficient congestion avoidance mechanism (ECAM) suitable for uncontrolled unicast and multicast best-effort flows. ECAM relies on and enhances a previous congestion avoidance mechanism for non-reliable flows that we have proposed previously. The key reason to the generality of our mechanism is the decoupling of congestion control from reliability through the use of source quench-like congestion feedback to the senders. As one of the main causes for high packet loss rates in the Internet is the lack of early notification of network congestion, we use active queue management techniques that have been developed to cope with this problem. Hence, we originally adapt random early detection (RED) and early congestion notification (ECN) for UDP in order to allow the UDP flows to be adaptive to network dynamics. We demonstrate through simulation that ECAM significantly reduces packet loss, enhance UDP to fairly coexist with TCP flows and provides an efficient and simple solution to congestion avoidance.

The authors study the problem of interconnecting several local area networks (LAN) across a highspeed backbone network (HSN). Because of the important gap between the transmission rate of the HSN compared to the ones of the LAN, a risk of severe congestion appears at the interconnection router that feeds the destination LAN. They study this problem using simplifying assumptions that allow them to develop a simulation model in the form of a queueing system. Simulation sessions made in simple cases show that significant gains in network utilization can be achieved by use of a real-time closed-loop control between the interconnection routers, at the expense of only a little additional traffic. They conclude with remarks about implementation possibilities and possible cooperation with other algorithms.>

An open queuing network for analyzing multilayered window flow control mechanisms consisting of different subnetworks is presented. The number of customers in each subnetwork is controlled by a semaphore queue. The queuing network is analyzed approximately using decomposition and aggregation. The approximation was validated against exact numerical and simulation data, and it was found to have an acceptable relative error. The queuing model is easy to implement, and it can be included in a software package. Using this queuing network, a case study involving the modeling and analysis of the ISO X25 flow control mechanism is presented.>