Time Sensitive Networking (TSN) is fundamental for the reliable, low-latency networks that will enable the Industrial Internet of Things (IIoT). Wi-Fi has historically been considered unfit for TSN, as channel contention and collisions prevent deterministic transmission delays. However, this issue can be overcome by using Target Wake Time (TWT), which enables the access point to instruct Wi-Fi stations to wake up and transmit in non-overlapping TWT Service Periods (SPs), and sleep in the remaining time. In this paper, we first formulate the TWT Acceptance and Scheduling Problem (TASP), with the objective to schedule TWT SPs that maximize traffic throughput and energy efficiency while respecting Age of Information (AoI) constraints. Then, due to TASP being NP-hard, we propose the TASP Efficient Resolver (TASPER), a heuristic strategy to find near-optimal solutions efficiently. Using a TWT simulator based on ns-3, we compare TASPER to several baselines, including HSA, a state-of-the-art solution originally designed for WirelessHART networks. We demonstrate that TASPER obtains up to 24.97% lower mean transmission rejection cost and saves up to 14.86% more energy compared to the leading baseline, ShortestFirst, in a challenging, large-scale scenario. Additionally, when compared to HSA, TASPER also reduces the energy consumption by 34% and reduces the mean rejection cost by 26%. Furthermore, we validate TASPER on our IIoT testbed, which comprises 10 commercial TWT-compatible stations, observing that our solution admits more transmissions than the best baseline strategy, without violating any AoI deadline.

This paper presents VaN3Twin—the first open source, full-stack Network Digital Twin (NDT) framework for simulating the coexistence of multiple Vehicle-to-Everything (V2X) communication technologies with accurate physical-layer modeling via ray tracing. VaN3Twin extends the ms-van3t simulator by integrating Sionna Ray Tracer (RT) in the loop, enabling high-fidelity representation of wireless propagation, including diverse Line of Sight (LoS) conditions with a focus on LoS blockage due to other vehicles’ meshes, Doppler effect, and site-dependent effects—e.g., scattering and diffraction. Unlike conventional simulation tools, the proposed framework supports realistic coexistence analysis across Dedicated Short Range Communication (DSRC) and Cellular-V2X (C-V2X) technologies operating over a shared spectrum. A dedicated interference tracking module captures cross-technology interference at the time-frequency resource block level and enhances Signal to Interference plus Noise Ratio (SINR) estimation by eliminating artifacts such as the bimodal behavior induced by separate LoS/NLoS propagation models. Compared to field measurements, VaN3Twin reduces application-layer disagreement by 50% in rural and over 70% in urban environments with respect to current state of the art simulation tools, demonstrating its value for scalable and accurate digital twin–based V2X coexistence simulation.

Recent advancements in autonomous vehicle research highlight the importance of Machine Learning (ML) models in tasks like motion planning, trajectory prediction, and emergency management. To support AI development, we propose a novel approach for generating on-demand datasets using the Simulator of Urban Mobility (SUMO) and a Generative Adversarial Network (GAN). Our method focuses on capturing critical events such as sudden pedestrian crossings, near-misses, and collisions, providing essential data to improve vehicle models' responses to emergency situations.

Autonomous exploration of complex, unknown environments is a cutting-edge task not entirely solved by the scientific community. When an agent needs to explore a maze without any a priori information about the environment, the lack of proper destinations and explicit task objectives make traditional navigation policies inappropriate. While the literature presents some sporadic deterministic systems able to face the tasks, learning approaches still need an adequate investigation which could prove them to be more suitable and versatile for this purpose. In this paper, we present MARS, a path planner that exploits swarms of robots to optimize the exploration of complex unknown environments, such as mazes. To make the solution scalable, the proposed method exploits two cooperating modules: local and global planners. The local planner is modeled as a Markov Decision Process (MDP) and trained as a Reinforcement Learning (RL) multi-agent system. Each agent has access to image representations of a section of the global map, always centered in the robot reference frame, and decides the next navigation goal to complete the local exploration. The global planner is a deterministic system that recovers the navigation when a local solution is unavailable. The robots share the explored section with peers when they meet in a rendez-vous. We compared our approach to a single deterministic agent, a single RL agent and a close-to-optimal deterministic approach which deploys five greedy agents. The simulation results demonstrate MARS' efficiency, reaching near-optimal levels in significantly less time.

Service orchestrators such as Kubernetes are widely employed to automate the handling and scheduling of workloads, which involves determining the most suitable physical node on which to start a new task. The expanding application of Machine Learning (ML) algorithms, and in particular Reinforcement Learning (RL), opens up new development opportunities to make runtime decisions that can account for multiple metrics and varying network conditions. However, current RL-based solutions are unable to fit the growing complexity of distributed applications and infrastructure, characterized by a more heterogeneous resource continuum and the increasing need to minimize energy consumption while satisfying tasks' requirements. To fill this gap, we propose RL-ICE as an innovative scheduler that can work in such a cloud continuum by leveraging a multi-cluster and hierarchical RL to satisfy both user Quality of Experience (QoE) metrics and tenant's costs. We test RL-ICE in a simulated large-scale environment and in a real-world Kubernetes setup. In both scenarios, our solution effectively balances user-perceived latency, energy consumption, and deployment costs. Additionally, RL-ICE can dynamically respond to network failures by migrating microservices to maintain efficient management of resources.

The deployment of cooperative-intelligent transport systems (C-ITS) has started, and standardization and research activities are moving forward to improve road safety and vehicular efficiency. An aspect that is still felt as a limitation by the research groups active in the field, is the difficulty to validate the solutions with real hardware and software, because of the huge investments that are needed when multiple equipped vehicles need to be considered. In this work, we present a platform with low-cost hardware based on a Raspberry Pi and a Wi-Fi module transmitting at 5.9 GHz, and on the open-source software Open Stack for Car (OScar), which is compliant with the ETSI C-ITS standards. With a limited cost in the order of 200 €, the platform realizes a device which is standard compliant and can be used as either on-board unit (OBU) or road side unit (RSU). The limited cost makes the testbed scalable to several units with limited budget and the limited size makes it also deployable on mini-cars to test advanced connected and autonomous vehicle (CAV) networks and applications. Our tests demonstrate its interoperability with other devices, compliance in terms of power spectrum, and a range of a few hundred meters in line-of-sight (LOS) conditions using the standard settings of ITS-G5.

Connectivity is a key enabler for next-generation autonomous vehicles. Developing and validating Connected and Autonomous Vehicle (CAV) services often requires extensive field testing, especially after initial pre-deployment tests. However, multiple field tests introduce high costs and logistical challenges, with no guarantee of consistent environmental conditions.This paper presents TRACEN-X (Telemetry Replay and Analysis of CAN bus and External Navigation data), the first open framework for collecting and reproducing navigation, sensor, and Controller Area Network (CAN) bus data from real-world scenarios in a controlled lab environment. By recreating real-world conditions, TRACEN-X streamlines development and validation, reducing costs and complexity. We validated our framework using data from a connected vehicle with ADAS sensors and a V2X-equipped stroller, demonstrating its potential in combination with a vehicular network simulator and an open ETSI C-ITS stack implementation.

In recent years, containerized deployment models have gained favor across many domain of applications. Kubernetes, the de-facto standard for containers orchestration, can efficiently manage heterogeneous devices, but fails to adapt to possibly stringent requirements, as it only considers computing metrics for scheduling decisions. In addition, the rising prominence of distributed cloud environments, which enable the development of highly available, performant solutions, requires modifications to the default Kubernetes scheduler. To address these challenges, we introduce LAIS, a multi-cluster Kubernetes scheduler optimized for end-to-end latency measurements to enhance user Quality of Experience (QoE). Unlike existing approaches, we define a geographically distributed environment and deploy a solution that satisfies user-specified intents in terms of latency. Depending on user needs, LAIS can either meet a specific latency constraint or schedule pods in the cluster with the lowest latency. After implementing LAIS in a multi-cluster environment, we found it highly effective in accommodating a range of user intents, outperforming the default Kubernetes scheduler in this regard.

Time Sensitive Networking (TSN) is fundamental for the low-latency, reliable, and energy-efficient networks that will enable the Industrial Internet of Things (IIoT). Wi-Fi has historically been considered unfit for TSN, as channel contention and collisions prevent deterministic transmission delays. However, this issue can be overcome using Target Wake Time (TWT) to instruct Wi-Fi stations to wake up and transmit in non-overlapped TWT Service Periods (SPs) and sleep in the remaining time. In this paper, we first formulate the TWT Acceptance and Scheduling Problem (TASP), whose objective is to schedule TWT SPs as to maximize traffic throughput and energy efficiency while respecting Age of Information (AoI) constraints. Then, since the TASP is NP-hard, we propose the TASP Efficient Resolver (TASPER), a heuristic strategy to find near-optimal solutions efficiently. Finally, we compare TASPER with several baselines through numerical analysis and simulations, which we performed using a TWT-compatible simulator based on ns-3. We demonstrate that TASPER schedules traffic with up to 21.23% higher priority-weighted admission ratio and saves up to 7.42% energy compared to the ShortestFirst strategy, all while satisfying AoI constraints for 99.5% of transmissions.

The automotive field is evolving towards high levels of automation, requiring seamless data exchange between vehicles through Vehicle-to-Everything (V2X) communications. Direct V2X technology is already being deployed on commercial vehicles, and it has the potential to deliver a range of safety and efficiency benefits on the road. However, the deployment of V2X-based applications is a complex process that demands extensive testing before these systems can be widely used by the public; indeed, high costs and safety concerns are among the main hurdles to overcome before applications leveraging V2X communication can become a reality. It is thus critical to reliably validate through simulation and emulation both the V2X technologies and the applications in realistic scenarios, before performing large-scale road tests. To address this pressing need, we present an open source framework for the virtual validation of V2X-based applications, amenable to the development and testing not only of different access technologies within the same environment (IEEE 802.11p, LTE-V2X, 5G NR-V2X, and LTE), but also of any kind of V2X-based application using ETSI-compliant messages. Our framework, called ms-van3t, is based on the ns-3 and SUMO (Simulation of Urban MObility) simulators, it implements a full ETSI C-ITS stack for CAM, DENM and IVIM messages, and it provides several novel features not found elsewhere. Further, ms-van3t enables the testing of V2X-based applications in HIL (Hardware-In-the-Loop) scenarios, thanks to a dedicated emulation mode, and it allows users to easily select different physical and MAC layer models, seamlessly collecting performance statistics. To showcase the capabilities of the framework, we present three sample applications as well as the performance results we obtained in terms of both application-related and network-related key performance indicators.

The practice of people counting serves as an indispensable tool for meticulously monitoring crowd dynamics, enabling informed decision-making in critical situations, and optimizing the management of urban spaces, facilities, and services. Beyond its fundamental role in safety and security, tracking people’s flows has evolved into a necessity for diverse business applications and the effective administration of both outdoor and indoor urban environments. In the ongoing exploration of the study, emphasis is placed on employing a passive counting technique. This method leverages WiFi probe request messages emitted by smart devices to assess the number of devices, providing a reliable estimate of the number of people in a specific area. However, it is crucial to acknowledge the dynamic landscape of privacy regulations and the concerted efforts by leading smart-device manufacturers to fortify user privacy, as evidenced by the adoption of MAC address randomization. In response to these considerations, an enhanced iteration of the WiFi traffic generator has been introduced. This upgraded version is designed to generate realistic datasets with ground truth, aligning with the evolving privacy landscape. Additionally, leveraging a profound understanding of probe requests and the capabilities of the designed generator, a novel crowd monitoring solution that incorporates machine learning techniques, named ARGO, has been developed. This innovative approach effectively addresses challenges posed by randomized MAC addresses, incorporating Bloom filters to ensure a formal “deniability” that complies with stringent regulations, including the European GDPR (European Parliament, Council of the European Union, Regulation (EU), 2016). The proposed solution adeptly addresses the pivotal task of people counting by harnessing WiFi probe request messages. Significantly, it prioritizes users’ privacy, aligning with the foundational principles outlined in regulations such as the European GDPR.

Everyday, as we go about our business in a city, we carry around several devices such as smartphones, tablets or even laptops, most of them with an active WiFi interface. This interface “leaks” wireless traces, or footprints, in the form of beacon or probe packets that can be used to identify the presence of people in certain areas. In particular, the analysis of device footprints allows the detection, tracking and monitoring of people in indoor and outdoor scenarios. In this paper, we focus on the probe request messages broadcast by wireless devices and we analyze the behaviour and the characteristics of these messages from different devices, coming from various vendors, with different operating systems and features, also considering the user interaction with them. In particular, we provide a detailed picture of the adoption of MAC address randomization techniques, and on the variety of fields present within the probe request messages.

Cutting-edge advances in wireless networking will soon enable a new generation of safer, smarter, and more autonomous vehicles. These vehicles will rely on real-time execution of complex Deep Learning (DL) tasks as well as high-speed multimedia streaming between road users for navigation purposes. Relying entirely on cellular networks (i) puts an unnecessary burden on an already overcrowded and expensive licensed spectrum; (ii) increases the latency of edge-offloaded tasks to intolerable levels for vehicular applications. Alongside the usage of a proper network infrastructure, vehicles will need to support on-board and offloaded cooperative intelligence. On this basis, we propose Edge-V , the first framework enabling practical vehicular edge intelligence and high-speed vehicular connectivity, using only unlicensed spectrum bands. Through a DSRC link, Edge-V acquires real-time localized knowledge, and coordinates the use of point-to-point millimeter Wave (mmWave) technologies to deliver high-bandwidth connectivity between vehicles. Edge-V also foresees smart offloading if on-board computing resources are insufficient. We prototype and evaluate Edge-V in a real-world laboratory testbed, showing its advantages with respect to cellular and cloud-based approaches.

Platooning is a popular vehicular application for autonomous driving on which the Platoon Leader (PL) manages all maneuvers using context information from Vehicle-to-Vehicle (V2V) messages. However, redundant context information from nearby vehicles in the platoon can increase computational costs for the PL. To solve this issue, vehicular micro-clouds can be formed to enable collective data processing and aggregation, thus reducing the PL’s perception workload. The proposed solution, called Platoon Local Dynamic Map (P-LDM), creates a single database of context information, distributing the data aggregation load among all members of the platoon. Simulation results evaluate the effectiveness of the proposed solution and compare it to typical Cooperative Perception mechanisms.

Given the plethora of sensors with which vehicles are equipped, today's automated vehicles already generate large amounts of data, and this is expected to increase in the case of autonomous vehicles, to enable data-driven solutions for vehicle control, safety and comfort, as well as to effectively implement convenience applications. It is expected that a crucial role in processing such data will be played by machine learning models, which, however, require substantial computing and energy resources for their training. In this paper, we address the use of cooperative learning solutions to train a Neural Network (NN) model while keeping data local to each vehicle involved in the training process. In particular, we focus on Federated Learning (FL) and explore how this cooperative learning scheme can be applied in an urban scenario where several cars, supported by a server located at the edge of the network, collaborate to train a NN model. To this end, we consider an LSTM model for trajectory prediction - a task that is an essential component of many safety and convenience vehicular applications, and investigate the performance of FL as the number of vehicles contributing to the learning process, and the data set they own, vary. To do so, we leverage realistic mobility traces of a large city and the FLOWER FL platform.

The continuous growth of the urban population and the high development and maintenance costs of infrastructure-based approaches make it necessary the utilization of distributed schemes. Among distributed systems, there is an increasing interest in edge cloud models, both for vehicular and pedestrian applications. In this paper, we developed a distributed application based on the micro cloud concept formulated in the field of vehicular edge computing for spreading content items in an indoor pedestrian environment. Results highlight how it is possible to reach both 100% content items spread and up to 66% reduction over channel collisions.

Centralized services with Vehicle-to-Infrastructure communication can be leveraged to provide precise and up-to-date maneuver recommendations to autonomous vehicles, enabling higher levels of automation. As centralized services have a complete view of the situation on the road, they can be exploited to enable L4 automation & beyond, as opposed to having each node manage an automated maneuver in a distributed way. It is thus pivotal to develop and deploy efficient real-time services, at the edge of the network, to enable low-latency maneuver management services. This paper presents an innovative open-source, 5G & Multi-Access Edge Computing enabled service, called Server Local Dynamic Map (S-LDM), aimed at collecting information about vehicles, based on standard-compliant messages, to create a map of the road, which can then be efficiently shared with other services managing L4 automation. We validated our service on both simulation and pilot trial on a production 5G network involving three main European Mobile Network Operators and real vehicles from Stellantis.

The testing of vehicular communication technologies, the study of urban mobility patterns, the evaluation of new traffic policies cannot dispense from vehicle mobility simulation. As is often the case, the larger the dataset, the better. Indeed, in recent years, many projects in the fields of mobility or vehicular communication have sought new traffic simulators with extended areas of investigation, possibly covering a whole city and its suburbs. In this spirit, we have modeled an urban traffic simulation in a 600-Km2area in and around the Municipality of Turin, leveraging the SUMO tool. This paper aims at reporting in detail the methodology we followed in the creation of this dataset. Our results demonstrate that a complete modeling of such a wide area is possible at the expense of minor simplifications, reaching a very good level of approximation.

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.

Traffic simulations are becoming a standard way to study urban mobility patterns, to evaluate new traffic policies and to test modern vehicular technologies. For this reason, in recent years, mobility projects pushed towards an increase in the demand of traffic simulators and towards an extension of their area of investigation, aiming at covering a whole city and its suburbs. In this paper we describe the methodology we followed in the creation of a large-scale traffic simulation of a 400-Km2area around the Municipality of Turin. Our preliminary results demonstrate that a complete modeling of such a wide tool is possible at the expense of minor simplifications.

The challenge of enabling the communications between the vehicle and its surroundings is being faced by the entire automotive industry, while the main standardization bodies are undergoing a huge effort to propose new solutions and improve the existing ones. The lack of open source solutions for vehicular communications penalizes the technology advances, and for this reason we present an open source platform based on PC Engines' boards and Unex's WNICs for the testing of V2X (vehicle-to-everything) applications. Our platform enables the connectivity over a 802.11p channel between two boards that can be deployed as wireless dongles, so it can be used to extend the network capabilities of any kind of computing system. The testbed has been setup to work with several applications: from video streaming, to online gaming, to a containerized version of a latency tester, called LaTe.

Latency is one of the key parameters of any networked system, from vehicular networks to real time video streaming. Being capable of measuring such a parameter can be very important in assessing the performances of devices under test. In this paper, we discuss how we designed a lightweight, flexible, custom latency measurement protocol, LaMP, completely agnostic of lower-layer protocols. We also present the first open source tool leveraging LaMP, called LaTe, running on any Linux-based device, which has been validated through several tests, both involving general purpose laptops and embedded devices for vehicular communications, for which the most important results are presented.

The automotive industry is more and more looking into solutions for connected vehicles, enabling use cases which would be otherwise not realizable. New solutions and protocols are currently being developed both by IEEE and 3GPP; due to its characteristics, IEEE 802.11p will probably be the protocol of choice for all the short range communications between vehicles. As, at the best of our knowledge, very few open source solutions exist, we present an open source platform, based on PC Engines embedded boards and Unex DHXA-222 cards, for the testing and characterization of the vehicular amendment of 802.11.

Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the extended virtual sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed.

5G network nodes, fronthaul and backhaul alike, will have both forwarding and computational capabilities. This makes energy-efficient network management more challenging, as decisions, such as activating or deactivating a node, impact on both the ability of the network to route traffic and the amount of processing it can perform. To this end, we formulate an optimization problem accounting for the main features of 5G nodes and the traffic they serve, allowing joint decisions about: 1) the nodes to activate; 2) the network functions they run; and 3) the traffic routing. Our optimization module is integrated within the management and orchestration framework of 5G, thus enabling swift and high-quality decisions. We test our scheme with both a real-world testbed based on OpenStack and OpenDaylight, and a large-scale emulated network whose topology and traffic come from a real-world mobile operator, finding it to consistently outperform state-of-the art alternatives and closely match the optimum.

Next-generation mobile networks will be heavily based on virtualization and their pervasiveness raises many questions regarding the energy efficiency of an architecture that requires distributed computing resources at the network edge. In this paper, we focus on the two main virtualization approaches, i.e., virtual machines and containers, which play a primary role in the provisioning of MEC-based services for mobile users. Specifically, we compare the two approaches from the viewpoint of the power consumption they are associated with - a metric significantly affecting the network provider's costs as well as the ICT environment footprint. Through a set of realworld experiments, using real-world video streaming and gaming applications, we assess not only the magnitude of the power consumption we incur, but also how it evolves as the workload increases. Our results show that containers are both more powerefficient and more scalable than virtual machines.

A 5G network provides several service types, tailored to specific needs such as high bandwidth or low latency. On top of these communication services, verticals are enabled to deploy their own vertical services. These vertical service instances compete for the resources of the underlying common infrastructure. We present a resource arbitration approach that allows to handle such resource conflicts on a high level and to provide guidance to lower-level orchestration components.

We consider the problem of energy-efficient network management in 5G systems, where backhaul and fronthaul nodes have both networking and computational capabilities. We devise an optimization model accounting for the main features of 5G backhaul and fronthaul, and jointly solve the problems of (i) node switch on/off, (ii) VNF placement, and (iii) traffic routing. We implement an optimization module within an application on top of an SDN controller and NFV orchestrator, thus enabling swift, high-quality decisions based on current network conditions. Finally, we validate and test our scheme with real-world power consumption, network topology and traffic demand, assessing its performance as well as the relative importance of the main contributions to the total power consumption of the system.

One of the key applications envisioned for C-V2I (Cellular Vehicle-to-Infrastructure) networks pertains to safety on the road. Thanks to the exchange of Cooperative Awareness Messages (CAMs), vehicles and other road users (e.g., pedestrians) can advertise their position, heading and speed and sophisticated algorithms can detect potentially dangerous situations leading to a crash. In this paper, we focus on the safety application for automotive collision avoidance at intersections, and study the effectiveness of its deployment in a C-V2I-based infrastructure. In our study, we also account for the location of the server running the application as a factor in the system design. Our simulation-based results, derived in real-world scenarios, provide indication on the reliability of algorithms for car-to-car and car-to-pedestrian collision avoidance, both when a human driver is considered and when automated vehicles (with faster reaction times) populate the streets.

Users of device-to-device (D2D) communication need efficient content discovery mechanisms to steer their requests toward the node in their neighborhood that is most likely to satisfy them. The problem is further compounded by the lack of a central coordination entity as well as by the inherent mobility of devices, which leads to volatile topologies. In this paper, we first discuss group-based communication among non-rooted Android devices using Wi-Fi direct, a protocol recently standardized by the Wi-Fi alliance. We propose intra- and inter-group communication methodologies, which we validate through a simple testbed where content-centric routing is used. Next, we address the autonomous formation of groups with the goal of achieving efficient device resource utilization as well as full connectivity. Finally, we evaluate the performance of our group formation procedure both in simulation and in a real testbed involving Android devices in different topologies.

In this paper, we propose a machine learning approach to automatically classify non-informative and informative contents shared on Twitter during disasters caused by natural hazards. In particular, we leverage on previously sampled and labeled datasets of messages posted on Twitter during or in the aftermath of natural disasters. Starting from results obtained in previous studies, we propose a language-agnostic model. We define a base feature set considering only Twitter-specific metadata of each tweet, using classification results from this set as a reference. We introduce an additional feature, called the Source Feature, which is computed considering the device or platform used to post a tweet, and we evaluate its contribution in improving the classifier accuracy.

5G networks are expected to be highly energy efficient, with a 10 times lower consumption than today's systems. An effective way to achieve such a goal is to act on the backhaul network by controlling the nodes operational state and the allocation of traffic flows. To this end, in this paper we formulate energy-efficient flow routing on the backhaul network as an optimization problem. In light of its complexity, which impairs the solution in large-scale scenarios, we then propose a heuristic approach. Our scheme, named EMMA, aims to both turn off idle nodes and concentrate traffic on the smallest possible set of links, which in its turn increases the number of idle nodes. We implement EMMA on top of ONOS and derive experimental results by emulating the network through Mininet. Our results show that EMMA provides excellent energy saving performance, which closely approaches the optimum. In larger network scenarios, the gain in energy consumption that EMMA provides with respect to the simple benchmark where all nodes are active, is extremely high under medium-low traffic load.

The proliferation of smartphones and related high-bandwidth services such as video streaming or cloud services are leading to increasing traffic demands, hence to the need of more efficient mobile networks. As current 4G networks are nearing their capacity, there is growing interest for the inclusion of new spectrum bands for LTE networks, among which unlicensed spectrum features prominently. Coexistence issues are all but certain, especially in the 5 GHz unlicensed band, where incumbent, ubiquitous WiFi devices are likely to be affected. Many coexistence solutions are currently being investigated, at both physical and MAC layer. In this work, we explore the impact of a MAC-layer solution that is supposed to allow graceful co-channel coexistence, finding that there are indeed some cases where, depending on the choice of parameters and configuration, the performance of WiFi traffic can be seriously degraded.

n LTE-Advanced networks, the steady demand for higher data rates by users is met through several techniques. One of the most promising solutions is CoMP (Coordinated MultiPoint), which allows the involvement of multiple eNB in the transmission and reception process with a marked increase in throughput for users at the edge of cells. While downlink CoMP has been the focus of many works in the literature, in this paper we examine Uplink CoMP with Coordinated Scheduling in Heterogeneous Networks running LTE-A. We investigate its system-level performance through simulation in various realistic scenarios with frequency-selective Rayleigh fading. Specifically, we are interested in comparing the per- formance of Uplink CoMP with different cell types as well as different user participation to CoMP transmissions. Our simulation results confirm that edge users benefit from Uplink CoMP, although the overall throughput decreases. In order to mitigate the latter effect, we introduce two new parameters, called CoMP margin and CoMP Pool Percentage (CPP), and provide guidelines for their effective use.

Efficient Multimedia Broadcast and Multicast Services (MBMS) to heterogeneous users in cellular networks imply adaptive video encoding, layered multimedia transmission, optimized transmission parameters, and dynamic broadcast area definition. This paper deals with MBMS by proposing a multi-dimensional approach for broadcast area definition, which provides an effective solution to all of the above aspects. By using multi-criteria K-means clustering, our scheme provides users with high levels of Quality-of-Experience (QoE) of multimedia services. Adaptive video encoding and allocation of radio resources (i.e., time-frequency resource blocks, and modulation and coding scheme) are performed based on user spatial distribution, channel conditions, service request, and user display capabilities. Simulation results show that our solution provides a 70% improvement in user QoE and 86% in number of served customers, as compared to an existing multimedia broadcast scheme.

An effective way to provide popular content in LTE networks is through broadcast and multicast services (a.k.a. eMBMS). This requires to aggregate cells into areas where transmissions are synchronized in time so that each area broadcasts the same set of content items, on the same radio resources. We look at an aspect of LTE broadcasting that has been scarcely addressed so far: how to form broadcasting areas and assign content to them so that radio resources are efficiently exploited and user requests satisfied. Due to its high complexity, we solve the problem through an original clustering heuristics, named Single-Content Fusion (SCF), that initially aggregates cells into single-content areas by maximizing cell similarity in content interests. Such areas are then merged into multiple-content areas leveraging similarity in spatial coverage. The validity of our solution is shown by the excellent match with the optimum in a toy scenario and by the remarkable advantages SCF provides in large-scale, real-world scenarios, in comparison to other heuristic approaches.

The added value of Device-to-Device (D2D) communication amounts to an efficient content discovery mechanism that enables users to steer their requests toward the node most likely to satisfy them. In this paper, we address the implementation of content-centric routing in a D2D architecture for Android devices based on WiFi Direct, a protocol recently standardised by the Wi-Fi Alliance. After discussing the creation of multiple D2D groups, we introduce novel paradigms featuring intra- and inter-group bidirectional communication. We then present the primitives involved in content advertising and requesting among members of the multi-group network. Finally, we evaluate the performance of our architecture in a real testbed involving Android devices in different group configurations. We also compare the results against the ones achievable exploiting Bluetooth technologies.

In our companion WoWMoM 2015 paper [1], we propose a content-centric routing mechanism for Wi-Fi Direct networks. In that paper, we showed how to implement a Wi-Fi Direct multi-group network with unrooted Android devices that supports bidirectional communication between different groups. We devised on it a content-centric application, denoted as Multi-Group Content (MGC), that enables content request and delivery in a distributed and cooperative way. Finally, we demonstrated our application with Android devices to fully validate our approach and assess its performance. Aim of the current demonstration is to allow demo session attendees to experiment MSG and assess its behavior and performance in real time.

We consider a network of electric vehicles (EVs) and its components: vehicles, charging stations, and coalitions of stations. For such a setting, we propose a model in which individual stations, coalitions of stations, and vehicles interact in a market revolving around the energy for battery recharge. We start by separately studying 1) how autonomously operated charging stations form coalitions; 2) the price policy enacted by such coalitions; and 3) how vehicles select the charging station to use, working toward a time/price tradeoff. Our main goal is to investigate how equilibrium in such a market can be reached. We also address the issue of computational complexity, showing that, through our model, equilibria can be found in polynomial time. We evaluate our model in a realistic scenario, focusing on its ability to capture the advantages of the availability of an intelligent transportation system supporting the EV drivers. The model also mimics the anticompetitive behavior that charging stations are likely to follow, and it highlights the effect of possible countermeasures to such a behavior.

The proliferation of overlapping, always-on IEEE 802.11 access points (APs) in urban areas, can cause inefficient bandwidth usage and energy waste. Cooperation among APs could address these problems by allowing underused devices to hand over their wireless stations to nearby APs and temporarily switch off, while avoiding to overload a BSS and thus offloading congested APs. The federated house model provides an appealing backdrop to implement cooperation among APs. In this paper, we outline a distributed framework that assumes the presence of a multipurpose gateway with AP capabilities in every household. Our framework allows cooperation through the monitoring of local wireless resources and the triggering of offloading requests toward other federated gateways. Our simulation results show that, in realistic residential settings, the proposed framework yields an energy saving between 45 and 86 percent under typical usage patterns, while avoiding congestion and meeting user expectations in terms of throughput. Furthermore, we show the feasibility and the benefits of our framework with a real test-bed deployed on commodity hardware.

We address the resource allocation problem in an LTE-based 2-tier heterogeneous network where in-band D2D communications are supported under network control. The different communication paradigms share the same radio resources, thus they may interfere. We devise a dynamic programming approach to efficiently schedule download and upload traffic, by 1) efficiently matching communicating endpoints and 2) assigning radio resources in an interference-aware manner while accounting for the characteristics of the content to be delivered. To this end, we develop an accurate model of the system and apply approximate dynamic programming to solve it. Our solution allows us to deal with realistic large-scale scenarios. In such scenarios, we compare our approach to today's networks where eICIC techniques and proportional fairness scheduling are implemented. Results highlight that our solution increases the system throughput while greatly reducing energy consumption. We also show that D2D mode, established either in the downlink or uplink, can effectively support delivery of highly popular content without significantly harming macrocell or microcell traffic, leading to increased system capacity. Interestingly, we find that D2D mode can also be a low-cost alternative to microcells.

Resource allocation in LTE networks is known to be an NP-hard problem. In this paper, we address an even more complex scenario: an LTE-based, 2-tier heterogeneous network where D2D mode is supported under the network control. All communications (macrocell, microcell and D2D-based) share the same frequency bands, hence they may interfere. We then determine (i) the network node that should serve each user and (ii) the radio resources to be scheduled for such communication. To this end, we develop an accurate model of the system and apply approximate dynamic programming to solve it. Our algorithms allow us to deal with realistic, large-scale scenarios. In such scenarios, we compare our approach to today's networks where eICIC techniques and proportional fairness scheduling are implemented. Results highlight that our solution increases the system throughput while greatly reducing energy consumption. We also show that D2D mode can effectively support content delivery without significantly harming macrocells or microcells traffic, leading to an increased system capacity. Interestingly, we find that D2D mode can be a low-cost alternative to microcells.

Broadcasting capabilities are one of the most promising features of upcoming LTE-Advanced networks. However, the task of scheduling broadcasting sessions is far from trivial, since it affects the available resources of several contiguous cells as well as the amount of resources that can be devoted to unicast traffic. In this paper, we present a compact, convenient model for broadcasting in LTE, as well as a set of efficient algorithms to define broadcasting areas and to actually perform content scheduling. We study the performance of our algorithms in a realistic scenario, deriving interesting insights on the possible trade-offs between effectiveness and computational efficiency.

A number of vehicular networking applications require continuous knowledge of the location of vehicles and tracking of the routes they follow, including, e.g., real-time traffic monitoring, e-tolling, and liability attribution in case of accidents. Locating and tracking vehicles has however strong implications in terms of security and user privacy. On the one hand, there should be a mean for an authority to verify the correctness of positioning information announced by a vehicle, so as to identify potentially misbehaving cars. On the other, public disclosure of identity and position of drivers should be avoided, so as not to jeopardize user privacy. In this paper, we address such issues by introducing A-VIP, a secure, privacy-preserving framework for continuous tracking of vehicles. A-VIP leverages anonymous position beacons from vehicles, and the cooperation of nearby cars collecting and reporting the beacons they hear. Such information allows a location authority to verify the positions announced by vehicles, or to infer the actual ones if needed, without resorting to computationally expensive asymmetric cryptography. We assess the effectiveness of A-VIP via realistic simulation and experimental testbeds.

Bandwidth availability in the cellular backhaul is challenged by ever-increasing demand by mobile users. Vehicular users, in particular, are likely to retrieve large quantities of data, choking the cellular infrastructure along major thoroughfares and in urban areas. It is envisioned that alternative roadside network connectivity can play an important role in offloading the cellular infrastructure. We investigate the effectiveness of vehicular networks in this task, considering that roadside units can exploit mobility prediction to decide which data they should fetch from the Internet and to schedule transmissions to vehicles. Rather than adopting a specific prediction scheme, we propose a fog-of-war model that allows us to express and account for different degrees of prediction accuracy in a simple, yet effective, manner. We show that our fog-of-war model can closely reproduce the prediction accuracy of Markovian techniques. We then provide a probabilistic graph-based representation of the system that includes the prediction information and lets us optimize content prefetching and transmission scheduling. Analytical and simulation results show that our approach to content downloading through vehicular networks can achieve a 70% offload of the cellular network.

Knowledge of the location of vehicles and tracking of the routes they follow are a requirement for a number of applications. However, public disclosure of the identity and position of drivers jeopardizes user privacy, and securing the tracking through asymmetric cryptography may have an exceedingly high computational cost. In this paper, we address all of the issues above by introducing A-VIP, a lightweight privacy-preserving framework for tracking of vehicles. A-VIP leverages anonymous position beacons from vehicles, and the cooperation of nearby cars collecting and reporting the beacons they hear. Such information allows an authority to verify the locations announced by vehicles, or to infer the actual ones if needed. We assess the effectiveness of A-VIP through testbed implementation results.

Efficient schemes for warning message dissemination in vehicular ad hoc networks (VANETs) use context information collected by vehicles about their neighbor nodes to guide the dissemination process. These schemes maximize their performance when all the vehicles advertise correct information about their positions, and hence position errors may drastically reduce the performance of the dissemination process. We present a proactive Cooperative Neighbor Position and Verification (CNPV) protocol that detects nodes advertising false locations so as to mitigate the impact of adversarial users. We combine our mechanism with two warning dissemination schemes for VANETs, and demonstrate how these algorithms can benefit from the use of our security scheme in the presence of malicious nodes trying to exploit the inherent vulnerabilities of each algorithm.

We envision a next-generation cellular network, where base stations allow Internet connectivity through different wireless interfaces (e.g., LTE and WiFi), and licensed cellular frequencies can be used also for device-to-device communications. With this scenario in mind, we develop a model that synthetically and consistently describes the diverse communications opportunities offered by the above network system. Then, we propose a fix-and-relax approach that makes the model solvable in real time. As one of its possible applications, our numerical results show how the model can be effectively used to design and analyze policies for dynamic frequency allocation.

Cooperation among federated APs in dense urban areas can yield energy saving by allowing under-used devices to hand over their wireless stations (WS) to nearby APs and temporarily switch off while meeting user expectations in terms of throughput. We demonstrate the effectiveness and the benefits of our energy-efficient cooperative protocol through a real deployment emulating a residential scenario. The demo we propose is highly interactive, as users can generate traffic within a BSS through a wireless station, like a smartphone or a notebook, and observe, through a web interface, the protocol behavior and the network topology changes caused by the new traffic scenario.

We present a communication protocol, called LINGER, for persistent dissemination of delay-tolerant information to vehicular users, within a geographical area of interest. The goal of LINGER is to dispatch and confine information in localized areas of a mobile network with minimal protocol overhead and without requiring knowledge of the vehicles' routes or destinations. LINGER does not require roadside infrastructure support: it selects mobile nodes in a distributed, cooperative way and lets them act as "information bearers", providing uninterrupted information availability within a desired region. We analyze the performance of our dissemination mechanism through extensive simulations, in complex vehicular scenarios with realistic node mobility. The results demonstrate that LINGER represents a viable, appealing alternative to infrastructure-based solutions, as it can successfully drive the information toward a region of interest from a far away source and keep it local with negligible overhead. We show the effectiveness of such an approach in the support of localized broadcasting, in terms of both percentage of informed vehicles and information delivery delay, and we compare its performance to that of a dedicated, state-of-the-art protocol.

A growing number of ad hoc networking protocols and location-aware services require that mobile nodes learn the position of their neighbors. However, such a process can be easily abused or disrupted by adversarial nodes. In absence of a priori trusted nodes, the discovery and verification of neighbor positions presents challenges that have been scarcely investigated in the literature. In this paper, we address this open issue by proposing a fully distributed cooperative solution that is robust against independent and colluding adversaries, and can be impaired only by an overwhelming presence of adversaries. Results show that our protocol can thwart more than 99 percent of the attacks under the best possible conditions for the adversaries, with minimal false positive rates.

We consider a system where users aboard communication-enabled vehicles are interested in downloading different contents from Internet-based servers. This scenario captures many of the infotainment services that vehicular communication is envisioned to enable, including news reporting, navigation maps, and software updating, or multimedia file downloading. In this paper, we outline the performance limits of such a vehicular content downloading system by modeling the downloading process as an optimization problem, and maximizing the overall system throughput. Our approach allows us to investigate the impact of different factors, such as the roadside infrastructure deployment, the vehicle-to-vehicle relaying, and the penetration rate of the communication technology, even in presence of large instances of the problem. Results highlight the existence of two operational regimes at different penetration rates and the importance of an efficient, yet 2-hop constrained, vehicle-to-vehicle relaying.

ABSTRACT We address the problem of content discovery and provision in vehicular networks with infrastructure, when a publish/subscribe paradigm is applied. In the scenario we consider, vehicular users can be providers and consumers of generic information content, which is available through the vehicular network itself. Special infrastructure nodes act as information brokers and aid vehicles in content retrieval and dissemination. We study the performance of such a scheme by evaluating the probability that a content requested by a vehicle can be found and successfully delivered to the querying vehicle. To ensure high success probability, we design a credit‐based scheme integrated with a feedback‐based mechanism. The combined use of credit and feedback entices rational users to provide their content when requested by other users (thus discouraging free‐riding behavior), and guarantees that no user is unduly burdened by too many requests for the same content (thus guaranteeing users a fair treatment). Also, through a banning mechanism that temporarily inhibits service to misbehaving users, we effectively counter malicious users whose sole objective is to disrupt the system. Using a simple game‐theoretic formulation, we prove that these mechanisms ensure that cooperation is the best choice for rational users. The performance of our scheme for discovery and provision of content is shown through ns‐3 simulations, by using a real‐world road topology and realistic vehicular traces. Copyright © 2012 John Wiley & Sons, Ltd.

The proliferation of electric vehicles is envisaged to become a reality, as they represent one of the major solutions to fossil fuel shortage and polluting emissions. One aspect of primary importance is the point of view of drivers, who aim at minimizing their trip time, hence the overall time that battery recharging may take. New generation of vehicles will be always connected, either using cellular or vehicular communications and they will be part of a widespread Intelligent Transportation System (ITS). Thus, dissemination of information on charging stations (with time-related parameters), or about which charging station to use, will significantly alleviate drivers' concern on the time needed to reach a destination. In this work, we investigate the scenario outlined above, by taking a game-theoretic approach. We account for the travel time towards charging stations, the waiting time there and the time for battery recharge, and analyze the role of ITS, as well as of the information it can distribute, in reducing the trip time of electric vehicles. Our study highlights the importance of a Central Controller that can not only inform drivers on the current scenario, but also give specific advice on the charging station to use. Interestingly, we show that drivers, being rational, will conform to such advice even if suboptimal.

Content access and downloading in vehicular environments is expected to heavily rely on the availability of roadside infrastructure. Although vehicle-to-vehicle communication is foreseen, data will mostly flow through roadside access points, or RSUs (RoadSide Units), which suffer from less connectivity problems. However, at least in the early stages of deployment, the RSU coverage will be spotty, or limited to main avenues in urban areas. In this paper, we try to address such shortcomings by investigating the possibility of exploiting parked vehicles to extend the RSU service coverage. Our approach leverages optimization models aiming at maximizing both the freshness of the content that downloaders retrieve and the efficiency in the utilization of radio resources. Performance evaluation highlights that the use of parked vehicles enhances the benefits of the content downloading process and leads to a significant offload of the RSUs, with respect to the case where only mobile relays are used.

Content downloading by mobile users is expected to significantly increase the cellular network load. Vehicular users, in particular, are likely to engage in information retrieval on the move: in this context, Intelligent Transportation Systems (ITS) can play an important role in offloading the cellular infrastructure. We investigate the effectiveness of ITS in this task, considering that roadside units (RSUs) can exploit mobility prediction to decide which data they should fetch from the Internet and schedule transmissions to vehicles, either potential relays or downloaders. Rather than presenting a specific prediction scheme, we propose a model that allows us to express and account for any prediction technique in a simple, yet effective, manner. We then provide a probabilistic graph-based representation of the system that accounts for the prediction uncertainty. We use such a representation to study the network dynamics by efficiently solving a (non-integer) LP problem. Our results show that the above approach to content downloading through ITS can achieve an 80% offload of the cellular network. Also, we investigate the dependency of the system performance on the accuracy of the mobility prediction, and which prediction errors have the largest impact.

The proliferation of overlapping, always-on IEEE 802.11 Access Points (APs) in urban areas can cause inefficient bandwidth usage and energy waste. Cooperation among federated APs could address these problems (i) by allowing under-used devices to hand over their wireless stations to nearby APs and temporarily switch off, (ii) by balancing the load of stations among APs and thus offloading congested APs. We outline a framework that allows such cooperation, yielding a 60% energy saving in realistic residential settings, while providing load balancing and meeting the user expectations in terms of throughput.

Performance and reliability of content access in mobile networks is conditioned by the number and location of content replicas deployed at the network nodes. In this work, we design a practical, distributed solution to content replication that is suitable for dynamic environments and achieves load balancing. Simulation results show that our mechanism, which uses local measurements only, approximates well an optimal solution while being robust against network and demand dynamics. Also, our scheme outperforms alternative approaches in terms of both content access delay and access congestion.

We address content discovery in wireless networks with infrastructure, where mobile nodes store, advertise, and consume content while Broker entities running on infrastructure devices let demand and offer meet. We refer to this paradigm as match-making, highlighting its features within the confines of the standard publish-and-subscribe paradigm. We study its performance in terms of success probability of a content query, a parameter that we strive to increase by acting as follows: 1) We design a credit-based scheme that makes it convenient for rational users to provide their content (thus discouraging free-riding behavior), and it guarantees them a fair treatment. 2) We increase the availability of either popular or rare content, through an efficient caching scheme. 3) We counter malicious nodes whose objective is to disrupt the system performance by not providing the content they advertise. To counter the latter as well as free riders, we introduce a feedback mechanism that enables a Broker to tell apart well- and misbehaving nodes in a very reliable manner, and to ban the latter. The properties of our match-making scheme are analyzed through game theory. Furthermore, via ns-3 simulations, we show its resilience to different attacks by malicious users and its good performance with respect to other existing solutions.

We present a lightweight algorithm for the estimation of the node achievable throughput and available bandwidth in IEEE 802.11 wireless networks. We consider a multirate WLAN with access point (AP), where there may be both elastic and inelastic traffic flows. Through our algorithm and leveraging previous theoretical results, the AP can estimate: (i) the available bandwidth that a new station wishing to associate with the AP can use, (ii) the impact on the system performance of admitting the new station, (iii) the bandwidth still available (if any) for inelastic traffic. The above quantities can be effectively used for admission control in WLANs and load balancing among APs with overlapping coverages. Indeed, simulation results show that the estimates yielded by our algorithm accurately reflect the system throughput behavior when there are both elastic and inelastic flows, in the uplink and downlink directions.

Content downloading in vehicular networks is a topic of increasing interest: services based upon it are expected to be hugely popular and investments are planned for wireless roadside infrastructure to support it. We focus on a content downloading system leveraging both infrastructure-to-vehicle and vehicle-to-vehicle communication. With the goal to maximize the system throughput, we formulate a max-flow problem that accounts for several practical aspects, including channel contention and the data transfer paradigm. Through our study, we identify the factors that have the largest impact on the performance and derive guidelines for the design of the vehicular network and of the roadside infrastructure supporting it.

Increasing the performance of wireless mesh networks is an important topic, which has been addressed in both theory and practice. Indeed, the availability of low-cost hardware justifies hands-on investigations and comparisons of protocols used in mesh networks deployments. In this work, we describe a real-life testbed that we have developed in order to investigate traffic routing in multihop, multiradio meshed networks with mobile users. We consider an environment where a user connects to different infrastructure mesh nodes as it moves, and we compare the performance of state-of-the-art routing protocols, namely, OLSR, OLSR-ETX, B.A.T.M.A.N and our modified version of this protocol, called sw-B.A.T.M.A.N. Our experimental measurements show that sw-B.A.T.M.A.N outperforms the other protocols, ensuring a seamless connectivity in presence of MPEG video streaming, with and without background traffic.

We propose a fully-distributed approach to the on line estimation of vehicle traffic density. Our approach envisions vehicles communicating within a VANET and cooperating to collect density measurements through a uniform sampling of the road sections of interest. The proposed scheme does not require the presence of any network infrastructure, central controller or devices triggered by the passage of vehicles, and it is suitable for both highway and urban environments. Results derived through ns-2 simulations in realistic mobility scenarios show that our solution is very effective, providing accurate, on-line estimates of the traffic density with minimal protocol overhead.

The provision of UDP-based multimedia streams to vehicular users through a roadside wireless mesh network requires a fast-switching, robust protocol architecture. We consider vehicles (e.g., cars, buses or streetcars) that connect to different roadside mesh nodes as they move in an urban environment, and study the joint problem of traffic delivery and connectivity management in such scenario. We identify BATMAN as a candidate layer-2 implementation of a routing protocol for vehicular networks, and we use simulation to compare its performance with other routing protocols for wireless ad hoc and mesh networks. Since BATMAN shows some inconsistencies in its behavior, we propose an improved version of the protocol, named smart-window BATMAN (sw-BATMAN). Then, we design two testbeds that include both roadside and vehicular mesh nodes. There, we implement the selected routing solution along with a handover mechanism that, by leveraging a channel selection scheme, allows vehicles to connect to the different roadside mesh nodes in a seamless manner. The performance assessment on our testbeds shows the efficiency of the proposed solution and highlights that our traffic routing and connectivity management are suitable for sustaining the handover of UDP streams in a vehicular environment, in a seamless manner.

Streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. A problem, scarcely addressed so far, is how to distribute video streaming traffic from one source to all nodes in an urban vehicular network. This problem significantly differs from previous work on broadcast and multicast in ad hoc networks because of the highly dynamic topology of vehicular networks and the strict delay requirements of streaming applications. We present a solution for intervehicular communications, called Streaming Urban Video (SUV), that 1) is fully distributed and dynamically adapts to topology changes, and 2) leverages the characteristics of streaming applications to yield a highly efficient, cross-layer solution.

We consider content discovery in wireless networks with infrastructure, where mobile nodes store, advertise and consume contents while Broker entities running on infrastructure devices let demand and offer meet. We refer to this paradigm as match-making, highlighting its features within the confines of the standard publish-and-subscribe paradigm. We combine such content discovery scheme with an efficient caching strategy that ensures a high hit probability. Furthermore, through a reputation-based mechanism, we counter the nodes that deceive the Broker into believing they cached a content whereas they did not. The performance of our match-making scheme is derived through ns-3 emulation/simulation.

Performance and reliability of content access in mobile networks is conditioned jointly by the number and location of content replicas deployed at the network nodes. The endeavour of this work is to address such an optimization problem with a distributed, lightweight solution that handles network dynamics. We devise a mechanism that lets nodes share the burden of storing and providing content, so as to achieve load balancing, and decide whether to replicate or drop the information so as to adapt to a dynamic content demand and time-varying topology. Simulation results show that our mechanism, which uses local measurements only, is: (i) extremely precise in approximating an optimal solution to content placement and replication; (ii) robust against network mobility; (iii) flexible in accommodating variation in time and space of the content demand.

We address cooperative caching in mobile ad hoc networks where information is exchanged in a peer-to-peer fashion among the network nodes. Our objective is to devise a fully-distributed caching strategy whereby nodes, independently of each other, decide whether to cache or not some content, and for how long. Each node takes this decision according to its perception of what nearby users may be storing in their caches and with the aim to differentiate its own cache content from the others'. We aptly named such algorithm "Hamlet". The result is the creation of a content diversity within the nodes neighborhood, so that a requesting user likely finds the desired information nearby. We simulate our caching algorithm in an urban scenario, featuring vehicular mobility, as well as in a mall scenario with pedestrians carrying mobile devices. Comparison with other caching schemes under different forwarding strategies confirms that Hamlet succeeds in creating the desired content diversity thus leading to a resource-efficient information access.

We investigate the problem of spreading information contents in a wireless ad hoc network. In our vision, information dissemination should satisfy the following requirements: (i) it should result in a desirable distribution of information replicas in the network and (ii) the information should be evenly and fairly carried by all nodes in their turn. In this paper, we show that these goals can be achieved by simple cache-and-forward mechanisms inspired by well-known node mobility models, provided that a sufficient number of information replicas are injected into the network. The proposed approach works under different network scenarios, is fully distributed and comes at a very low cost in terms of protocol overhead.

We propose a localized channel assignment scheme called LOCA for multi-channel multi-radio (MCMR) wireless mesh networks. The scheme combines the advantage of using multiple channels with random assignment, typical of the dynamic/hybrid approach, with the advantage of using all node interfaces for both transmission and reception, as done in static assignment. Since optimal channel assignment in MCMR networks is an NP-hard problem, we resort to a heuristic, which uses only localized (single-hop) information to perform channel assignment. Also, we consider that the BATMAN routing protocol is implemented in the mesh network and we exploit the local information that nodes can collect through BATMAN to implement our strategy. Simulation results obtained through ns2 show that the proposed scheme ensures a high network connectivity level and that a low number of reassignment procedures is needed to adapt the channel usage to the changes in the network topology or in the interference level. Furthermore, when compared against a static approach, LOCA provides significantly better performance in terms of throughput and packet delivery ratio, for networks with low-medium node density.

We present Figaro, a content discovery solution for mobile environments. Our main focus is on urban networks, in which high densities of users coexist in relatively narrow, circumscribed areas reached by an infrastructure (e.g., bus stops integrating an AP).

We propose a framework, called LINGER, for the support of cooperative creation and distribution of information contents in vehicular networks. The goal of LINGER is to dispatch and confine information in localized areas of a mobile network with no infrastructure availability and minimal protocol overhead. LINGER selects mobile nodes in a distributed, cooperative way and lets them act as "information bearers", ensuring uninterrupted information availability to as many nodes as possible in a desired region. Simulation results in vehicular scenarios with realistic node mobility prove that LINGER successfully drives information toward a target area from a far away source and keeps it local with negligible overhead. Further tests with a beaconing application leveraging the LINGER framework show that a mobile information bearer may be as reliable as an infrastructure-based access point in providing service to users. Finally, in a large-scale scenario, LINGER is proven to be effective for delay-tolerant broadcast applications.

In this work we define and study a new model for the caching problem in a heterogeneous wireless network under a flash-crowd scenario. Using non-cooperative game theory, we cast the caching problem as an anti-coordination game. We start by defining the social optimum in the general case and then focus on a two-player game to obtain insights into the design of efficient caching strategies. Based the theoretical findings, our current work focuses on the development of strategies to be implemented in a practical network setting.

Broadcast/multicast is a service of paramount importance for wireless users, which poses several serious challenges. Many multicast applications, such as multicasting streaming or alarm signalling, require a reliable and efficient medium access control (MAC) layer. However, the current IEEE 802.11 protocol does not offer any MAC layer recovery mechanisms on unsuccessfully delivered broadcast/multicast frames. Consequently, lost frames cannot be detected, hence retransmitted, and this may deteriorate the quality of broadcast/multicast services. To address these issues, we propose a reliable multicast MAC protocol for wireless multihop networks, which aims at providing high packet delivery ratio as well as low control overhead and multicast delivery delay.

We consider that a given number of dissemination points (DPs) have to be deployed for disseminating information to vehicles travelling in an urban area. We formulate our problem as a maximum coverage problem (MCP) so as to maximize the number of vehicles that get in contact with the DPs and as a second step with a sufficient amount of time. Since the MCP is NP-hard, we solve it though heuristic algorithms. Evaluation of the proposed solutions in a realistic urban environment shows how knowledge of vehicular mobility plays a major role in achieving an optimal coverage of mobile users, and that simple heuristics provide near-optimal results even in large-scale scenarios.

The authors focus on a wireless mesh network, that is, an ad hoc IEEE 802.11-based network whose nodes are either user devices or Access Points providing access to the mesh network or to the Internet. By relying on some work done within the IEEE 802.11s TG, the network nodes can use one control channel and one or more data channels, each on separate frequencies. Then, some problems related to channel access are identified and a MAC scheme is proposed that specifically addresses the problem of hidden terminals and the problem of coexisting control and data traffic on different frequency channels. An analytical model of the MAC scheme is presented and validated by using the Omnet++ simulator. Through the developed model, we show that our solution achieves very good performance both in regular and in very fragmented mesh topologies, and it significantly outperforms the standard 802.11 solution.

The paper focuses on a cooperative environment in wireless ad hoc networks, where mobile nodes share information in a peer-to-peer fashion. Nodes follow a pure peer-to-peer approach (i.e., without the intervention of servers), thus requiring an efficient query/response propagation algorithm to prevent network congestion. The main contribution of the paper is the proposal of a novel solution, called Eureka, that identifies the regions of the network where the required information is more likely to be stored and steers the queries toward those regions. To discriminate among regions, the concept of information density is introduced, along with a procedure that allows nodes its estimation. Eureka does not require the use of satellite positioning systems, and proves to be very effective in both vehicular and pedestrian environments.

Streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. A problem, scarcely addressed so far, is how to support streaming traffic in vehicular networks (VANETs). This problem significantly differs from previous work on broadcast and multicast in ad hoc networks, because of the highly-dynamic topology of VANETs and the strict delay requirements of streaming applications. Our solution, completely relies on inter-vehicular communication for the distribution of multimedia content in a urban environment and it has the following appealing features: (i) it is fully distributed and dynamically adapts to topology changes; (ii) it leverages the characteristics of streaming applications to yield a highly-efficient, cross-layer solution. After optimally dimensioning the network system, we compare the performance of our solution against theoretical results for broadcast capacity in multihop networks.

We consider a cooperative environment in wireless mobile networks where information is exchanged among nodes in a peer-to-peer fashion. We apply a pure peer-to-peer approach (i.e., without the intervention of servers) and we seek to devise an efficient query/response propagation algorithm. Our approach, calledEureka, identifies the regions of the network where the required information is more likely to be stored and steers the queries toward those regions. To discriminate among regions, we introduce the concept ofinformation densityand a procedure that allows nodes its estimation. The effectiveness of our scheme is evaluated through simulation in a vehicular environment with realistic mobility models.

The growing availability of multiple network interfaces on mobile devices makes concurrent multipath transfer an appealing candidate to satisfy the ever increasing user demand for bandwidth. As of today, the only transport protocol supporting multihoming is the stream control transmission protocol (SCTP); yet, SCTP can only exploit at most one of the available paths at any given time. This paper introduces sender-based packet pair SCTP (SBPP-SCTP) and westwood SCTP (W-SCTP), which add concurrent multipath support to the original SCTP. The two protocols are implemented on Linux and their performance is assessed on an experimental testbed emulating different wireless-channel bandwidth and delay conditions.

This paper introduces WiSE, a sender-side, transport-layer protocol that modifies the standard SCTP protocol. WiSE aims at exploiting SCTP's multihoming capabilities by selecting in real time the best choice among available, alternate paths to the same destination. Through the use of bandwidth estimation techniques, WiSE tries to infer whether losses are due to congestion or to radio channel errors. At the same time, the available bandwidth of the current path used for transmission is matched to that of an alternate path, also probed for available bandwidth. If the current path is severely congested and the alternate path is lightly loaded, WiSE switches the transmission onto the alternate path using SCTP's flexible path management capabilities. Extensive simulations under different scenarios highlight the superiority of the proposed solution with respect to TCP and the standard SCTP implementation.

Multiple description coding (MDC) combined with multi-path transmission is a viable solution to the growing demand of reliable multimedia video communication over the Internet. Despite the progress in MDC techniques, transport protocols based on TCP cannot efficiently handle multipath transmissions of this kind of traffic. In this paper we present MD-SCTP, a full-multipath partially-reliable SCTP-based protocol, providing different scheduling schemes and featuring a selective retransmission within time-to-delivery constraints of multimedia traffic. Performance analysis proves the validity of our scheduler and of our selective retransmission scheme.

This paper presents a peer-to-peer (P2P) service for the transmission of real-time video content, exploiting the contemporary usage of multiple network paths over the current Internet. Before starting the transmission, the sender probes the available paths for their round trip time and other parameters using simple ICMP packets. Then it chooses the set of paths to be used in order to maximize the expected signal to noise ratio (SNR) while meeting the delay requirements of the streaming content. The video is encoded using a multiple description coding (MDC) technique. Simulations show that the proposed algorithm always chooses one of the group of paths that yields the best SNR

We address the problem of wireless stations self-configuration in a WLAN environment with overlapping access point coverages. We propose and investigate a transport-layer solution called AISLE (autonomic interface selection) that builds on top of the SCTP protocol and exploits its multihoming features. Through simulation, we evaluate AISLE's capability to maximize the throughput of multi-interface stations and to achieve an optimal partition of the stations across overlapping WLANs. Although we focus on a WLAN scenario, AISLE is independent of the technology used at the physical and MAC layers

A consequence of the increasing availability of multiple network interfaces on the same computer system is the request for new technologies capable of fully exploiting the available resources. As a matter of fact, today's most common transport and network layer protocols are unable to use more than one interface at a time, thus wasting part of the available network resources. However, the growing demand for bandwidth and quality of service introduced by multimedia applications in particular could find an answer in the correct utilization of multipath connections. In this paper, we present Westwood SCTP-PR, a partially-reliable SCTP-based protocol, providing full multipath support and featuring an original adaptive load balancing technique, characteristics which make it especially suitable to real-time traffic.

Lists the Autonomic Networks Program Committee ((in alphabetical order).

This paper presents WiSE, a transport-layer protocol that modifies the standard SCTP protocol. WiSE aims at exploiting SCTP's multihoming capabilities by selecting in real time the best choice among available, alternate paths to the same destination. Through the use of bandwidth estimation techniques, WiSE tries to infer whether losses are due to congestion or radio channel errors. At the same time, the available bandwidth on the current path used for transmission is matched to that of an alternate path, also probed for available bandwidth; if the current path is severely congested, and the alternate path is lightly loaded, WiSE switches the transmission onto the alternate path, using SCTP's flexible path management capabilities. Extensive simulations under different scenarios highlight the superiority of the proposed solution with respect to the standard SCTP implementation.

We propose an information-sharing application for wireless intervehicular networks (IVNs), called Infoshare. The Infoshare application leverages the broadcast nature of the wireless medium to achieve maximum spreading of information queries among vehicles, while a smart caching policy limits the overhead resulting from useless queries and duplicated replies. Simulation by ns2 is used to investigate the performance of Infoshare, highlighting the impact of various system parameters on spreading dynamics. A network scenario featuring one- and two-lane traffic traveling either at the same or at different speeds is considered. Simulation results come in handy for the development of analytical models, identifying critical parameters and justifying assumptions.

TCP Smart Framing, or TCP-SF for short, enables the Fast Retransmit/Recovery algorithms even when the congestion window is small. Without modifying the TCP congestion control based on the additive-increase/multiplicative-decrease paradigm, TCP-SF adopts a novel segmentation algorithm: while Classic TCP always tries to send full-sized segments, a TCP-SF source adopts a more flexible segmentation algorithm to try and always have a number of in-flight segments larger than 3 so as to enable Fast Recovery. We motivate this choice by real traffic measurements, which indicate that today's traffic is populated by short-lived flows, whose only means to recover from a packet loss is by triggering a Retransmission Timeout. The key idea of TCP-SF can be implemented on top of any TCP flavor, from Tahoe to SACK, and requires modifications to the server TCP stack only, and can be easily coupled with recent TCP enhancements. The performance of the proposed TCP modification were studied by means of simulations, live measurements and an analytical model. In addition, the analytical model we have devised has a general scope, making it a valid tool for TCP performance evaluation in the small window region. Improvements are remarkable under several buffer management schemes, and maximized by byte-oriented schemes.

Wireless local area networks (WLANs) based on the IEEE 802.11 technology are becoming increasingly popular and widely deployed. However, the growing need for quality of service (QoS) guarantees is difficult to implement in distributed systems like WLANs, where the random access protocol and the unpredictability of the wireless channel hamper their interaction with well-established architectures like DiffServ. Even without trying to provide deterministic QoS guarantees, simpler requirements are hard to get by. For example, the basic requirement of providing fair access to all users is conflicting with the nature of higher-layer protocols: TCP is fair only under certain conditions, hardly met by 802.11b WLANs. Another basic requirement is the protection for short-lived TCP flows, that are sensitive to losses during the early stages of the TCP window growth. The main contribution of this paper is the proposal of an LLC-layer algorithm that can be implemented at both access point (AP) and wireless stations (WSs). The algorithm aims at guaranteeing fair access to the medium to every user, by awarding longer transmission opportunities to WSs that experienced short channel failures. At the same time, such award mechanism can protect short-lived flows while they strive to get past the critical "small window regime". We outline the proposed solution and present a simulation study that shows the effectiveness of the new algorithm in comparison to the standard 802.11b implementation.

Wireless local area networks (WLAN) using IEEE 802.11 technology are expected to become a widespread networking solution to provide both real-time services and data applications. In this paper, we consider a WLAN with infrastructure using the IEEE 802.11e distributed contention-based access scheme, the so-called EDCA. We investigate the EDCA performance in presence of voice and data integrated traffic, and observe the inefficiencies that arise when access differentiation is performed on the basis of traffic type only We then present a solution that improves both fairness and throughput of real-time traffic in WLAN employing EDCA, and we show the benefits of our proposal through some simulation results obtained with ns-2.

We present a study of Web user behavior when network performance decreases causing an increase of page transfer times. Real traffic measurements are analyzed to infer whether worsening network conditions translate into greater impatience by the user, which translates in early interruption of TCP connections. Several parameters are studied in order to gather their impact on the interruption probability on Web transfers: time of day, file size, throughput and time elapsed since the beginning of the download. From the results presented, we try to paint a picture of the complex interactions between user perception of the Web and network-level events.

This paper presents the adaptive video over IP (AViP) approach to transmit video sequences. A rate-selection algorithm based on both delay and loss indication is presented and its performance measured using actual MPEG-2 video sequences, network simulations and objective measures of perceptual quality. The results show that the AViP approach leads to efficient use of available network resources, reactiveness to congestions and TCP-friendliness. Moreover, AViP delivers significantly higher perceptual levels of quality than traditional constant-bit-rate systems operating at the same average level.

We define an analytical approach and a methodology to determine the set of service level agreements (SLA) that can be effectively supported by a DiffServ IP network. We consider the assured forwarding (AF) per hop behavior (PHB), and, based on the SLA probabilistic description, we derive a worst-case mathematical formulation for the overbooking probability, i.e., the probability that the traffic crossing any link of a source-destination path exceeds the link capacity. We then compare analytical results with simulation of different traffic models.

We present a simulation study of HTTP traffic crossing a DiffServ domain. We consider both the cases where the reserved bandwidth is not exceeded by the offered traffic (overprovisioning) and where the assured traffic competes with the classic best effort class (underprovisioning). The reported simulation shows that the DiffServ approach is able to protect the assured flows in the first case, while the performance benefits are tighter in the second case, in which fairness issues arise between long and short-lived flows.

This paper discusses a new approach to QoS routing, introducing the notion of algorithm resilience (i.e., its capability to adapt to network and load modifications) as the performance index of the algorithm itself, for a given network topology, load and traffic pattern. The new approach can be summarized as network graph reduction, i.e., a modification of the graph describing the network before the routing path is computed, in order to exclude from the path selection over-congested portions of the network. This solution leads to a class of two-step routing algorithms, where both steps are simple, hence allowing efficient implementation. Simulation experiments, run on randomly-generated topologies and traffic patterns, show that these routing algorithms outperform both the standard minimum hop algorithm and those QoS-based algorithms based on the same metrics but not using the notion of network graph reduction.

This paper addresses the problem of evaluating routing algorithms via simulation in packet-switched networks when elastic traffic is involved. It highlights some deficiencies of classical approaches that fail to capture both the complex interactions of connections traversing multiple bottlenecks and common user behaviors. The paper describes an approach devised to overcome these limitations which is particularly suited for the evaluation of routing algorithms in presence of best-effort traffic. The simulation results presented offer a deeper insight into well-known routing algorithms. Through this analysis it is clear that quantitative and also qualitative behaviors of dynamic routing algorithms based on traffic measurements may be fairly different depending on the nature of the traffic loading the network, as well as depending on its interactions with the network parameters and behavior.

We study the performance of TCP Westwood (TCPW), a new TCP protocol with a sender-side modification of the window congestion control scheme. TCP Westwood controls the window using end-to-end rate estimation in a way that is totally transparent to routers and to the destination. Thus, it is compatible with any network and TCP implementation. The key innovative idea is to continuously estimate, at the TCP sender, the packet rate of the connection by monitoring the ACK reception rate. The estimated connection rate is then used to compute congestion window and slow start threshold settings after a congestion episode. Resetting the window to match available bandwidth makes TCPW more robust to sporadic losses due to wireless channel problems. These often cause conventional TCP to overreact, leading to unnecessary window reduction. Experimental studies of TCPW show significant improvements in throughput performance over Reno and SACK, particularly in mixed wired/wireless networks over high-speed links. The contributions of this paper include a model for fair and friendly sharing of the bottleneck link and a Markov Chain performance model in presence of link errors/loss. TCPW performance is compared to that of TCP Reno, and analytic results are validated against simulation results. Internet and laboratory measurements using a Linux TCPW implementation are also reported, providing further evidence of the gains achievable via TCPW.

In this paper we propose an enhancement to the TCP protocol, called TCP Smart-Framing(TCP-SF), that enables the Fast Recovery algorithm for short lived flows, as most of the current Internet traffic is. Without modifying the TCP congestion control based on the additive-increase/multiplicative-decrease paradigm, TCP Smart-Framing adopts a novel segmentation algorithm: while classic TCP starts sending one segment, a TCP-SF source is allowed to send an initial window of 4 smaller segments, whose aggregate payload is equal to the connection's MSS. This key idea can be implemented on top of any TCP flavor, from Tahoe to SACK, and requires modifications to the server behavior only.

The standard end-to-end flow control implemented by the TCP protocol is ill-suited when it comes to achieving fair bandwidth allocation among competing TCP flows. Indeed, the lack of feedback from intermediate nodes does not allow a TCP source to regulate its throughput so that it is not sending more than its fair share, thus penalizing other, less aggressive flows. We propose a novel scheme that, building on existing work on network-layer stateless fair queueing, extends the approach to the TCP layer. Also, we discuss a possible implementation of both the network-layer and the transport-layer architecture. We test our solution under different traffic scenarios and show that not only is a fair bandwidth allocation achieved, but the overall network utilization for TCP flows is also increased.

TCP Westwood (TCPW) is a sender-side modification of the TCP congestion window algorithm that improves upon the performance of TCP Reno in wired as well as wireless networks. The improvement is most significant in wireless networks with lossy links, since TCP Westwood relies on end-to-end bandwidth estimation to discriminate the cause of packet loss (congestion or wireless channel effect) which is a major problem in TCP Reno. An important distinguishing feature of TCP Westwood with respect to previous wireless TCP “extensions” is that it does not require inspection and/or interception of TCP packets at intermediate (proxy) nodes. Rather, it fully complies with the end-to-end TCP design principle. The key innovative idea is to continuously measure at the TCP source the rate of the connection by monitoring the rate of returning ACKs. The estimate is then used to compute congestion window and slow start threshold after a congestion episode, that is, after three duplicate acknowledgments or after a timeout. The rationale of this strategy is simple: in contrast with TCP Reno, which “blindly” halves the congestion window after three duplicate ACKs, TCP Westwood attempts to select a slow start threshold and a congestion window which are consistent with the effective bandwidth used at the time congestion is experienced. We call this mechanism faster recovery. The proposed mechanism is particularly effective over wireless links where sporadic losses due to radio channel problems are often misinterpreted as a symptom of congestion by current TCP schemes and thus lead to an unnecessary window reduction. Experimental studies reveal improvements in throughput performance, as well as in fairness. In addition, friendliness with TCP Reno was observed in a set of experiments showing that TCP Reno connections are not starved by TCPW connections. Most importantly, TCPW is extremely effective in mixed wired and wireless networks where throughput improvements of up to 550% are observed. Finally, TCPW performs almost as well as localized link layer approaches such as the popular Snoop scheme, without incurring the O/H of a specialized link layer protocol.

We present a framework for the analysis of a set of adaptive variable-bit-rate voice sources in a packet network. The instantaneous bit rate of each source is determined by an end-to-end control mechanism that, based on measurements of packet delay and loss rate, selects the rate that best matches current network conditions. Several such algorithms can be analyzed with the proposed framework, which consists of a detailed Markovian model of the source behavior and of an approximate description of the interaction between the sources and the underlying network. The model of the source takes into account time intervals during which a connection is active as well as intervals of inactivity; within a given conversation, it also models on/off (speech/silence) periods. The interaction of a source with the rest of the system is derived through an iterative procedure that evaluates the feedback that a source receives from the network. A case study presenting the results relative to an adaptive system transmitting at bit rates typical of widely used speech coding standards (64 kb/s, 13 kb/s and 8 kb/s) illustrates the proposed framework.

In this paper, we outline a methodology that can be applied to model the behavior of TCP flows. The proposed methodology stems from a Markovian model of a single TCP source, and eventually considers the superposition and interaction of several such sources using standard queueing analysis techniques. Our approach allows the evaluation of such performance indices as throughput, queueing delay and packet loss of TCP flows. The results obtained through our model are validated by means of simulation, under several topology and traffic settings.

This paper presents a load-balancing method to improve network utilization when static routing algorithms are employed. Static routing algorithms can generally be reduced to a path assignment problem with the aim of minimizing a cost function: i.e., maximizing network revenues. Unfortunately the problem, when multiple constraints such as different QoS traffic classes are present, is computationally intractable. Load-balancing methods are heuristics that allow the identification of local minima of the cost function that hopefully are close to the real optimum. The load-balancing method we propose is based only on static information about the average traffic load of each traffic relation: a piece of information which can easily be attained in telecommunication networks. The path-selection strategy is defined in terms of set theory and its performance is evaluated via simulation by comparison against standard minimum-hop path selections. Simulation results show that low priority traffic benefits significantly from the more balanced load obtained with our solution.

We address the problem of providing transparent, reliable, and efficient network-level multicasting in wormhole LANs. We describe some alternatives for achieving deadlock-free multicasting using fast buffer reservation techniques at the host interface level. Tradeoffs involving complexity and performance of various solutions are discussed, and are illustrated using simulation results. Our results show that in most cases the straightforward approach of sending multiple unicast copies of the multicast message at the originator host turns out to be the most simple and effective approach.

Most of the medium-access control (MAC) sublayer protocols recently proposed for application in very high-speed local and metropolitan area networks (LANs and MANs) are based on a slotted transmission scheme. Slotting guarantees very good throughput efficiencies, but further gains are possible if slots can be freed after reaching their destination, thus being available for repeated use as they propagate in the network. The authors describe a simulation-based quantitative analysis of the performance gains obtained by introducing slot reuse in distributed-queue dual-bus (DQDB) and cyclic reservation multiaccess (CRMA) MANs. CRMA-II, the latest evolution of CRMA, naturally incorporates slot reuse in the MAC protocol operations and is considered in the study for comparison purposes. In the case of the standard DQDB protocol, some existing proposals are considered. In the case of CRMA, both a previous IBM proposal and a novel approach, leading to very good performances, are studied.>