Starlink has become the largest commercial LEO satellite network, yet little is known about its internal queue management and bandwidth allocation mechanisms. Prior measurement studies have documented performance variations but lack the granularity to explain the underlying causes. We present the first microscopic characterization of Starlink's transmission behavior, using controlled measurements from multiple terminals to capture per-packet dynamics at microsecond precision. Our analysis uncovers several previously undocumented mechanisms. Starlink employs head-drop queuing rather than tail-drop, with capacities of approximately 1500 and 4000 packets on downlink and uplink, respectively. Bandwidth allocation is demand-driven, starting from a baseline of 100/30 Mbps on the downlink and uplink that ramps up by 3.4×/2× over 400 ms when flows sustain queue pressure. Active queue management aggressively induces packet loss to control queue occupancy, especially on the uplink. These mechanisms reset every 15 seconds during Starlink's reconfiguration cycle. We also find flow-level queuing that isolates latency between concurrent flows while coupling their loss on the downlink. These findings reveal that Starlink's queue management creates fundamentally different operating conditions than terrestrial networks.

Edge-cloud inference collaborations are often designed with a routing estimator that decides whether to offload each frame from weak models at the edge to stronger models in the cloud. Existing systems place the routing estimator after the weak detector, so the weak forward pass still runs even on frames that are later offloaded. In this paper, we argue that this weak-conditioned design can be suboptimal when the offload budget varies. First, we present a competitive weak-skipping estimator (0.153 GFLOPs, about 29x lighter than the weak detector at 4.49 GFLOPs) that extracts routing signal from raw pixels, outperforming the common after-weak placement weak-conditioned baselines. Second, we show that neither weak-skipping nor weak-conditioned placement dominates across the full operating curve, and we propose budget-adaptive routing, which selects between them by offload budget via two offline-tuned thresholds. On PASCAL VOC, our budget-adaptive router traces the upper accuracy envelope of both fixed placements across the operating range. Our method reduces per-frame latency by up to 19.1 ms (about 30% lower at rho = 0.9). Besides outperforming SOTA methods, it is surprisingly stronger than the strong model (+1.7 pp over the strong model's peak mAP) at some operating points with far less compute. Artifacts are available at https://github.com/ViGeng/bgt-ada

In 2026, we saw rising numbers of proposals for orbital data centers to facilitate AI, e.g., by SpaceX (up to one million satellites), Blue Origin, and Google—yet none include rigorous lifecycle sustainability analysis. We present ESpaS-ODC, a lifecycle carbon model that accounts for the three subsystems physically unavoidable at GPU-class power densities but absent from prior work: a thermal radiator sized from ISS data, solar-array degradation and eclipse margin, and cold-standby spares for no-repair access. Applying the model to an edge data center in a 510 km orbit reveals that the service-overhead-scaled radiator weighs about eleven times the GPU it serves and that modeled power and thermal infrastructure dominate launched component mass—not compute. Using mission duration T as the denominator to amortize launch, re-entry, and manufacturing carbon, model-boundary parity with a global-average terrestrial DC occurs within the first two mission years in our parameter sweep. With respect to a renewables-powered green-energy DC (Finland grid intensity: 68 gCO2e/kWh, PUE: 1.2), parity requires multi-year missions with cold-standby spares compensating the shorter component lifetimes. Idealized dawn-dusk SSO (no-eclipse regime, β > βcrit) eliminates the eclipse battery, cutting modeled component mass significantly (e.g., 25% on the 1 kW-ODC for a 3 yr mission), but leaves the radiator unchanged: high-beta orbits solve the battery problem, not the thermal problem. Finally, carrying one full cold spare (r = 2) increases amortized carbon per GPU hour by 40% on Starship and 34% on Falcon-9 at T = 3 yr, while its dependability benefit remains to be quantified.

The DNS is a key component of the Internet. Originally designed to facilitate the resolution of host names to IP addresses, its scope has continuously expanded over the years, today covering use cases such as load balancing or service discovery. While DNS was initially conceived as a rather static directory service in which resource records (RR) only change rarely, we have seen a number of use cases over the years where a DNS flavor that isn't purely based upon requesting and caching RRs, but rather on an active distribution of updates for all resolvers that showed interest in the respective records in the past, would be preferable. In this paper, we thus explore a publish-subscribe variant of DNS based on the Media-over-QUIC architecture, where we devise a strawman system and protocol proposal to enable pushing RR updates. We provide a prototype implementation, finding that DNS can benefit from a publish-subscribe variant: next to limiting update traffic, it can considerably reduce the time it takes for a resolver to receive the latest version of a record, thereby supporting use cases such as load balancing in content distribution networks. The publish-subscribe architecture also brings new challenges to the DNS, including a higher overhead for endpoints due to additional state management, and increased query latencies on first lookup, due to session establishment latencies.

The widespread use of smartphones and Wi-Fi-enabled devices offers a promising alternative to vision-based crowd estimation methods by passively capturing Wi-Fi probe requests. However, recent techniques in MAC address randomization effectively prevent device tracking, ensuring better privacy protection while complicating traditional counting methods. This paper presents a machine learning-based approach for estimating crowd sizes using only the number of unique randomized MAC addresses observed over time, without any form of individual tracking. We treat the count of observed addresses as a time-series signal and apply MiniRocket to extract robust temporal features. These features, combined with statistical attributes of the signal, are used to train a machine learning model to classify crowd sizes. We validate our method across static, stable, and dynamic environments, achieving a Mean Absolute Percentage Error (MAPE) below 9% for dynamic crowds, 1% for static crowds, and 0.8% for stable real-world crowds, demonstrating accurate, scalable, and privacy-preserving crowd size estimation in near real-time.

As smartphones and other Wi-Fi-Enabled devices become increasingly common, they offer a practical way to estimate crowd size by collecting the probe request frames these devices periodically transmit. While this passive approach avoids relying on cameras or other intrusive sensors, the adoption of MAC address randomization—designed to protect user privacy—makes it difficult to reliably count how many unique devices are present. This paper presents a machine learning–based approach for device counting that leverages Channel State Information (CSI) features extracted from probe frames. Our method enables accurate device population estimation while preserving MAC-level privacy and avoiding persistent tracking or behavioral profiling. A Siamese neural network is trained to learn a discriminative similarity function between packet pairs, allowing the grouping of temporally co-occurring packets likely originating from the same device. To address the challenge of the limited number of packets available from each randomized MAC address, we use a lightweight augmentation strategy that interpolates between CSI samples to increase training density. We evaluate our approach in indoor and outdoor settings with varying device probing behaviors under static conditions. By leveraging packet-level voting and device-counting strategies, our model leverages packet-level voting and device-counting strategies to consistently achieve high counting accuracy—averaging over 98% in indoor environments and reaching 100% outdoors—while preserving device privacy by avoiding behavioral tracking and re-identification.

Modern web applications demand increasingly sophisticated network protocols. Deploying applications that integrate custom protocols and algorithms is particularly challenging in container, browser, or shared kernel environments, where untrusted code execution must be restricted. While the host-provided network stack is usually highly efficient, it often lacks adaptability. With QUIC emerging as an extensible foundation for networked applications and WebAssembly (WASM) offering a secure, lightweight container runtime, we propose a modular network stack architecture composable of host and isolated guest components. This paper investigates the performance cost of implementing a complete network stack-including HTTP/3, QUIC, and TLS-as isolated WASM components. We evaluate our architecture, built on the WebAssembly System Interface (WASI) 0.2 and the WASM Component Model, through a comprehensive comparison with a native implementation. Our results demonstrate that while building a modular QUIC stack with WASM is technically feasible, its current overhead limits its practical use. The composed WASM implementation achieves a maximum goodput of 0.9 Gbps, compared to 6.1 Gbps with a native implementation. We identified the lack of send-offloading capabilities, missing cryptographic acceleration, and additional memory copies as the major performance bottlenecks. We discuss these limitations and propose potential improvements to bridge the performance gap.

Content Delivery Networks (CDNs) have been pivotal in the dramatic evolution of the Internet, handling the majority of data traffic for billions of connected users. Low-Earth-Orbit (LEO) satellite networks, such as Starlink, aim to revolutionize global connectivity by providing high-speed, low-latency Internet to remote regions. However, LEO satellite networks (LSNs) face challenges integrating with traditional CDNs, which rely on geographical proximity for efficient content delivery - a method that clashes with the operational dynamics of LSNs. In this paper, we scrutinize the operation of CDNs in the context of LSNs, using Starlink as a case study. We develop a browser extension NetMet that performs extensive web browsing experiments from controlled nodes using both Starlink and terrestrial Internet access. Additionally, we analyse crowdsourced speed tests from Starlink users to Cloudflare CDN servers globally. Our results indicate significant performance issues for Starlink users, stemming from the misalignment between terrestrial and satellite infrastructures. We then investigate the potential for SpaceCDNs which integrate CDN infrastructure directly within the LSNs, and show that this approach offers a promising alternative that decreases latencies by over 50%, making them comparable with the CDN experience of users behind terrestrial ISPs. Our aim is to stimulate further research and discussion on overcoming the challenges of effective content delivery with growing LSN offerings.

In distributed consensus systems (DCSs), a single peer exposes functionality to other peers to agree on a shared state for a computational problem, such as for cryptocurrencies and distributed file systems. We observe, however, that this original, peer-centric model has evolved towards deploying several peers at a single network location, thus exposing the same DCS services many times, driven by the fees and rewards that can be gained by doing so. We refer to this trend as the service-centric model and provide in our paper evidence for this trend, its growth, and its impact on DCS operations, using empirical observations in the Ethereum system. Specifically, we shed light on the opposing observations of increasing reliance on highly available cloud infrastructures and large numbers of non-reachability events in the DCS. We provide recommendations on how to tackle this impact through changes to the Ethereum platform and identifier generation, believing that those recommendations and our empirical observations provide useful insights for building resilient and bias-free DePIN platforms.

Low-Earth-Orbit satellite networks (LSNs) are enabling low-latency high-bandwidth internet connectivity at a global scale. However, majority of the traffic on the Internet is currently handled by Content Delivery Networks (CDNs), which rely on geographical proximity to deliver content. In this work, we examine CDN performance for the commercial largest LSN, i.e. Starlink, by performing active measurements through our web browser plugin and passive analysis of Cloudflare speed tests globally. Comparing this to terrestrial networks, we highlight significant performance degradation for Starlink users due to the asymmetries between satellite and terrestrial infrastructure.

The emerging nature of low-earth-orbiting satellite constellations has brought forth an era of global connectivity. Star-link has the potential to offer a truly global, high-performant service. An obvious question arising from this is whether Starlink can replace existing terrestrial services. In this work we describe the key findings from a detailed study of the performance of Starlink, investigating both its potential and shortcomings to act as a "global ISP".

The Starlink network from SpaceX stands out as the only commercial LEO network with over 2M+ customers and more than 4000 operational satellites. In this paper, we conduct a first-of-its-kind extensive multi-faceted analysis of Starlink performance leveraging several measurement sources. First, based on 19.2M crowdsourced M-Lab speed tests from 34 countries since 2021, we analyze Starlink global performance relative to terrestrial cellular networks. Second, we examine Starlink's ability to support real-time latency and bandwidth-critical applications by analyzing the performance of (i) Zoom conferencing, and (ii) Luna cloud gaming, comparing it to 5G and fiber. Third, we perform measurements from Starlink-enabled RIPE Atlas probes to shed light on the last-mile access and other factors affecting its performance.Finally, we conduct controlled experiments from Starlink dishes in two countries and analyze the impact of globally synchronized "15-second reconfiguration intervals'' of the satellite links that cause substantial latency and throughput variations. Our unique analysis paints the most comprehensive picture of Starlink's global and last-mile performance to date.

Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications depends on their smoothness and immersiveness. Modern AR applications applying computationally intensive computer vision algorithms can burden today's mobile devices, and cause high energy consumption and/or poor performance. To tackle this challenge, it is possible to offload part of the computation to nearby devices at the edge. However, this calls for smart task placement strategies in order to efficiently use the resources of the edge infrastructure. In this paper, we introduce Nimbus — a task placement and offloading solution for a multi-tier, edge-cloud infrastructure where deep learning tasks are extracted from the AR application pipeline and offloaded to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. Our multifaceted evaluation, based on benchmarked performance of AR tasks, shows the efficacy of our solution. Overall, Nimbus reduces the task latency by$\sim 4\times$and the energy consumption by$\sim$77% for real-time object detection in AR applications. We also benchmark three variants of our offloading algorithm, disclosing the trade-off of centralized versus distributed execution.

Future automated vehicles will be able to negotiate cooperative maneuvers with each other via vehicle-to-everything communication, increasing safety, driving comfort, and traffic flow. Researchers have proposed several enabling cooperation protocols, but only a few have already been implemented in real-world vehicles. While simulations can be helpful for the development of a cooperation protocol, in the end, it is necessary to validate functionality in the target system. This paper implements one explicit general-purpose protocol for cooperative maneuvers in an automated vehicle, evaluating the overall cooperative, connected, and automated system in-depth. We show its feasibility, suitability for an example use case, and improvement potentials.

Emerging Remote Piloting (RP) operations of electrified Unmanned Aerial Vehicles (UAVs) demand low-latency and high-quality video delivery to conduct safe operations in the low-altitude airspace. Although cellular networks are one of the prominent candidates to provide connectivity for such operations, their ground-centric nature limits their capabilities in achieving seamless and reliable aerial connectivity. In this paper, we study the feasibility of supporting RP operations with low latency and high-quality video delivery over commercial cellular networks. By setting up an adaptive bitrate video transmission pipeline with the Google Congestion Control (GCC) and Self-Clocked Rate Adaptation for Multimedia (SCReAM) Congestion Control (CC) algorithms, we analyze the video delivery performance for the RP application requirements and compare the performance of GCC and SCReAM against constant bitrate video delivery. Our results show that low-latency video delivery with < 300 ms playback latency between full-HD and 4K resolution can be maintained up to about 95% of the time in the air. While static bitrate video delivery outperforms adaptive streaming in urban location with abundant link capacity, the latter becomes advantageous in rural locations, where the link capacity is affected by fluctuations. Although the study's findings highlight the capabilities of cellular networks in delivering low-latency video for a safety-critical aerial service, we also discuss the potential improvements and future research challenges for enabling safe operations and meeting the service requirements using cellular networks. We release our collected traces and the video transmission pipeline as open-source to facilitate research in this field.

Smartphones and the signaling messages they emit allow third parties to learn about the owners' mobility. While Wi-Fi and Bluetooth signaling messages have been (mis)used for tracking individuals, there are also privacy-respecting uses: crowd sensing for estimating the number of people in an area and their dynamics, is one such example. However, the very useful countermeasures against individual tracking, most prominently MAC address randomization, also complicate crowd size estimation. In this paper, we present an online estimation algorithm that operates only on ephemeral MAC addresses and, if desired, signal strength information to distinguish relevant signals from background noise. We use measurements and simulations to calibrate our counting algorithm and collect numerous data sets which we use to explore the algorithm's performance in different scenarios.

Connected and automated vehicles will not only autonomously plan their motions but also use vehicle-to-vehicle communication to negotiate cooperative maneuvers. Via intent-sharing and joint decision-making, those vehicles will perform coordinated actions together. Up to now, studies on cooperative maneuvers mostly assumed participants always cooperate. This scenario is fair but unrealistic. In our contribution, we investigate how vehicles can assess cooperative maneuver requests to decide whether or not to participate in them. Our decision algorithm ensures safety and fairness while being independent of the underlying cooperative maneuver protocol. We show the algorithm’s feasibility in simulations. This paper is thus another step towards realizing cooperative maneuvers.

Bluetooth trackers, or tags, have quickly become ubiquitous and widely supported by multiple vendors. Beyond their original design of finding lost objects, these devices have the ability to extend the capabilities of current wireless smart devices. Since its launch in 2019, Apple’s FindMy enables any devices from their brand to be easily tracked by more than 1 billion active iPhones and iPads on the market. While convenient, these systems may even serve further uses, including as a result of this work, crowd sensing and a side channel for mobile communication. But they also raise privacy concerns for their users. In this paper, we demonstrate how Apple FindMy can be used as a privacy-friendly tool for crowd monitoring, and how it may inadvertently leak information on a person’s location in case of deliberate tracking. Additionally, we design and evaluate a proof of concept protocol, using the Apple FindMy and a crafted tag using a simple microcontroller. We show how such system could be used to transmit information at very low bit rates, while the devices transporting the information remain unaware of this covert channel, yielding an out of band communication channel.

With the pioneering introduction of autonomous vehicles, system failures while driving from A to B are more likely to occur. In such scenarios one option is to hand back the control to the human driver, if someone suitable is inside the vehicle. Teleoperated Driving, the remote control of vehicles by human operators, can be a solution to scenarios without suitable drivers inside. A video stream is used to provide operators with an overview of the vehicle’s environment and support for a safe remote control. By utilizing cellular networks as wireless communication medium for Teleoperated Driving, the available bandwidth is a limiting factor. This paper introduces a multi-step approach to lower the bandwidth requirements, which is achieved by initially splitting the single video stream into two parts: One part conveying the original video information restricted to important objects and the remainder, to which various filters are applied. Results show that this approach can lead to a decreased bandwidth consumption. These results are validated with a user study, where participants had to rate the perceived video quality and the driveability for the different combinations. This user study shows that, for every investigated scenario, at least one combination of parameters (applied filters) was rated driveable. Finally, the results are used to sketch a system that infers specific combinations of parameters based on the environmental conditions and the available bitrate.

“Roam like Home” is the initiative of the European Commission (EC) to end the levy of extra charges when roaming within the European region. As a result, people can use data services more freely across Europe. However, the implications of roaming solutions on network performance have not been carefully examined yet. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in six countries across Europe. Using this platform, we measure different aspects of international roaming in 4G networks in Europe, including mobile network configuration, performance characteristics, and quality of experience. We find that operators adopt a common approach to implement roaming called Home-routed roaming (HR). This results in additional latency penalties of 60 ms or more, depending on geographical distance. This leads to worse browsing performance, with an increase in the metrics related to Quality of Experience (QoE) of users (Page Load time and Speed Index) in the order of 15-20 percent. We further analyze in isolation the impact of latency on QoE metrics and find that the penalty imposed by HR leads to a degradation on QoE metrics up to 150 percent in case of intercontinental roaming.

Driver assistance, self-driving, and multimedia systems have two common implications: increasing demand on network bandwidth and the need for more powerful computation nodes. As a result, intra-vehicular networks (IVNs) change their layout. They are built around central nodes connected to the rest of the vehicle via Ethernet. The usage of Ethernet presents a challenge, as it lacks support for deterministic behavior by design. The solution is found within the IEEE Time-Sensitive Networking (TSN) standards, introducing real-time, low-latency, and deterministic communication into the Ethernet ecosystem. These new networked systems need to be thoroughly evaluated with IVN requirements in mind. To assess numerous configurations of IVN setups, in this work, we introduce a novel Environment for Generic In-vehicular Networking Experiments — EnGINE. It allows, among many others, repeatable, reproducible, and replicable TSN experiments with high precision and flexibility, which is not possible to run using proprietary solutions. EnGINE is based exclusively on commercial off-the-shelf components and is orchestrated by a flexible Ansible framework. This approach allows us to configure various topologies emulating realistic IVNs behavior, which is challenging using simulations. Based on available related work, we further address the challenges found in the IVNs. We derive additional requirements for experiments in the TSN domain and present our approach to fulfill them in an experimental setting. We believe that EnGINE provides the ideal environment for TSN network experiments.

Cloud computing has seen continuous growth over the last decade. The recent rise in popularity of next-generation applications brings forth the question: "Can current cloud infrastructure support the low latency requirements of such apps?" Specifically, the interplay of wireless last-mile and investments of cloud operators in setting up direct peering agreements with ISPs globally to current cloud reachability and latency has remained largely unexplored.

Network support for Unmanned Aerial Vehicles (UAVs) is raising an interest among researchers due to the strong potential applications. However, current knowledge on UAV data traffic is mainly based on conceptual studies and does not provide an in-depth insight on the data traffic properties. To close this gap, we present a measurement-based study analyzing in detail the Control and Non-payload Communication (CNPC) traffic produced by three different UAVs when communicating with their remote controller over 802.11 protocol. We analyze the traffic in terms of data rate, inter-packet interval and packet length distributions, and identify their main influencing factors. The data traffic appears neither deterministic nor periodic but bursty, with a tendency towards Poisson traffic. We further create an understanding on how the traffic of the investigated UAVs are internally generated and propose a model to analytically capture their traffic processes, which provides an explanation for the observed behavior. We implemented a publicly available UAV traffic generator "AVIATOR" based on the proposed traffic model and verified the model by comparing the simulated traces with the experimental results.

Automated vehicles will be able to drive autonomously in various environments. An essential part of that is to predict other vehicles' intents and to coordinate maneuvers jointly. Such cooperative maneuvers have the ability to make driving safer and traffic more efficient. However, among the various communication protocols proposed for maneuver coordination, no single one satisfies all requirements. This paper assesses failure risks and mitigation strategies for cooperative maneuvers, including an analysis of popular protocols regarding this aspect. Next, we evaluate one particular cooperation protocol, the complex vehicular interactions protocol (CVIP), concerning performance of mitigation mechanisms and their influence on maneuver success rates or times to reach consensus among maneuver participants. Via simulation, we show that CVIP is suitable for cooperative maneuvers in realistic scenarios and investigate the trade-offs individual mitigation mechanisms face. These results are well-suited as guidelines and benchmark for other researchers developing cooperative maneuver protocols.

Cooperative maneuvers will enable automated vehicles to optimize traffic flow and increase safety via vehicle-to-vehicle communication. Different approaches and protocols exist, but no study has investigated how to generate intelligent suggestions for cooperative maneuvers. We use machine learning to propose safe and suitable overtake maneuvers. To this end, we train a classifier for maneuver success as well as regression models on an extensive data set of randomized initial situations. In addition, we show that changing objective functions allows optimizing for different goals like smoothness or driven distance. Our evaluation shows that machine learning is well-suited to suggest cooperative maneuvers while also facing some trade-offs. This work may thus provide a benchmark for advanced studies on cooperative maneuver proposals.

Multipath TCP (MPTCP) extends traditional TCP to enable simultaneous use of multiple connection endpoints at the source and destination. MPTCP has been under active development since its standardization in 2013, and more recently in February 2020, MPTCP was upstreamed to the Linux kernel. In this paper, we provide the first broad analysis of MPTCPv0 in the Internet. We probe the entire IPv4 address space and an IPv6 hitlist to detect MPTCP-enabled systems operational on port 80 and 443. Our scans reveal a steady increase in MPTCP-capable IPs, reaching 9k+ on IPv4 and a few dozen on IPv6. We also discover a significant share of seemingly MPTCP-capable hosts, an artifact of middleboxes mirroring TCP options. We conduct targeted HTTP(S) measurements towards select hosts and find that middleboxes can aggressively impact the perceived quality of applications utilizing MPTCP. Finally, we analyze two complementary traffic traces from CAIDA and MAWI to shed light on the real-world usage of MPTCP. We find that while MPTCP usage has increased by a factor of 20 over the past few years, its traffic share is still quite low.

In the early days of cloud computing, datacenters were sparsely deployed at distant locations far from end-users with high end-to-end communication latency. However, today’s cloud datacenters have become more geographically spread, the bandwidth of the networks keeps increasing, pushing the end-users latency down. In this paper, we provide a comprehensive cloud reachability study as we perform extensive global client-to-cloud latency measurements towards 189 datacenters from all major cloud providers. We leverage the well-known measurement platform RIPE Atlas, involving up to 8500 probes deployed in heterogeneous environments, e.g., home and offices. Our goal is to evaluate the suitability of modern cloud environments for various current and predicted applications. We achieve this by comparing our latency measurements against known human perception thresholds and are able to draw inferences on the suitability of current clouds for novel applications, such as augmented reality. Our results indicate that the current cloud coverage can easily support several latency-critical applications, like cloud gaming, for the majority of the world’s population.

Telecommunication (Telco) outdoor position recovery aims to localize outdoor mobile devices by leveraging measurement report (MR) data. Unfortunately, Telco position recovery requires sufficient amount of MR samples across different areas and suffers from high data collection cost. For an area with scarce MR samples, it is hard to achieve good accuracy. In this paper, by leveraging the recently developed transfer learning techniques, we design a novel Telco position recovery framework, called TLoc, to transfer good models in the carefully selected source domains (those fine-grained small subareas) to a target one which originally suffers from poor localization accuracy. Specifically, TLoc introduces three dedicated components: 1) a new coordinate space to divide an area of interest into smaller domains, 2) a similarity measurement to select best source domains, and 3) an adaptation of an existing transfer learning approach. To the best of our knowledge, TLoc is the first framework that demonstrates the efficacy of applying transfer learning in the Telco outdoor position recovery. To exemplify, on the 2G GSM and 4G LTE MR datasets in Shanghai, TLoc outperforms a non-transfer approach by 27.58 and 26.12 percent less median errors, and further leads to 47.77 and 49.22 percent less median errors than a recent fingerprinting approach NBL.

The increasing trend of the traffic demand from mobile users and the presence of limited resources creates a challenge for network resource management. Understanding the data usage pattern and traffic demand of mobile users is a way forward to enable data-driven network resource management. However, due to the complex nature of mobile networks, understanding and characterizing data usage pattern of mobile users is a daunting task. In this work, we investigate and characterize data usage patterns and behavior of users in mobile networks. We leverage a dataset (~340 M records) collected through a crowd-based mobile network measurement platform - Netradar - across six countries. We elucidate different network factors and study how they affect the data usage patterns by taking mobile users in Finland as a use case. We perform a comparison on data usage patterns of mobile users across six countries by considering total data consumption, network access, the number of sessions created per user, throughput, and user satisfaction level on services. We show that data usage behavior of users over a mobile network is primarily driven by user mobility, the type of data subscription plan marketed by Mobile Network Operators (MNOs), network congestion, and network coverage. Besides, the data usage patterns over different network technologies (e.g., preferring cellular over WiFi) and the percentage of users accessing congested networks vary by country; mostly due to the market pricing strategy and radio coverage. However, the overall data consumption (cellular and WiFi) is comparatively similar in most of the countries we studied.

Automated vehicles need to interact: to create mutual awareness and to coordinate maneuvers. How this interaction shall be achieved is still an open issue. Several new protocols are discussed for cooperative services such as changing lanes or overtaking, e.g., within the European Telecommunications Standards Institute (ETSI) and Society of Automotive Engineers (SAE). These communication protocols are, however, usually specific to individual maneuvers or based on implicit assumptions on other vehicles' intentions. To enable reuse and support extensibility towards future maneuvers, we propose CVIP, a protocol framework for complex vehicular interactions. CVIP supports explicitly negotiating maneuvers between the involved vehicles and allows monitoring maneuver progress via status updates. We present our design in detail and demonstrate via simulations that it enables complex inter-vehicle interactions in a flexible, efficient and robust manner. We also discuss open questions to be answered before complex interactions among automated vehicles can become a reality.

The number of deployed Internet of Things (IoT) devices is steadily increasing to manage and interact with community assets of smart cities, such as transportation systems and power plants. This may lead to degraded network performance due to the growing amount of network traffic and connections generated by various IoT devices. To tackle these issues, one promising direction is to leverage the physical proximity of communicating devices and inter-device communication to achieve low latency, bandwidth efficiency, and resilient services. In this work, we aim at enhancing the performance of indoor IoT communication (e.g., smart homes, SOHO) by taking advantage of emerging technologies such as visible light and ultrasound. This approach increases the network capacity, robustness of network connections across IoT devices, and provides efficient means to enable distance-bounding services. We have developed communication modules using off-the-shelf components for visible light and ultrasound and evaluate their network performance and energy consumption. In addition, we show the efficacy of our communication modules by applying them in a practical indoor IoT scenario to realize secure IoT group communication.

Understanding and predicting mobility are essential for the design and evaluation of future mobile edge caching and networking. Consequently, research on human mobility prediction has drawn significant attention in the last decade. Employing information-theoretic concepts and machine learning methods, earlier research has shown evidence that human behavior can be highly predictable. Whether high predictability manifests itself for different modes of device usage, across spatial and temporal dimensions is still debatable. Despite existing studies, more investigations are needed to capture intrinsic mobility characteristics constraining predictability, to explore more dimensions (e.g. device types) and spatiotemporal granularities, especially with the change in human behavior and technology. We investigate practical predictability of next location visitation across three different dimensions: device type, spatial granularity and temporal spans using an extensive longitudinal dataset, with fine spatial granularity (AP level) covering 16 months. The study reveals device type as an important factor affecting predictability. Ultra-portable devices such as smartphones have "on-the-go" mode of usage (and hence dubbed "Flutes"), whereas laptops are "sit-to-use" (dubbed "Cellos"). The goal of this study is to investigate practical prediction mechanisms to quantify predictability as an aspect of human mobility modeling, across time, space and device types. We apply our systematic analysis to wireless traces from a large university campus. We compare several algorithms using varying degrees of temporal and spatial granularity for the two modes of devices; Flutes vs. Cellos. Through our analysis, we quantify how the mobility of Flutes is less predictable than the mobility of Cellos. In addition, this pattern is consistent across various spatio-temporal granularities, and for different methods (Markov chains, neural networks/deep learning, entropy-based estimators). This work substantiates the importance of predictability as an essential aspect of human mobility, with direct application in predictive caching, user behavior modeling and mobility simulations.

Towards private proximity services we realized a set of proximity services at different spatial resolutions. For small-scale (~0.5 m) securing remote access to smart homes and for mid-scale (10-20 m) to manage nearby Internet of Things (IoT) devices and offer fine-grained service discovery in indoor environments. Regarding large-scale services (100 m) we implemented a device grouping via similarity of light patterns ambient sound Wi-Fi signals and ultrasound communication which is naturally restricted by spatial barriers. To improve user's privacy from a system point of view we analyzed different security mechanisms in the domain of device-to-device (D2D) communication such as access control location privacy. Based on visible light communication (VLC) we are implemented and tested a system for private indoor service discovery and distance-bounding authorization. Furthermore we examined the feasibility of homomorphic encryption for time-series data like visible light patterns.

Visible Light Communication (VLC)emerges as a communication technology for Internet of Things (IoT)services with appealing benefits not present in existing radio-based communication. However, current VLC designs commonly require dedicated LED lights to emit modulated light beams which entail high energy overhead and unpleasant visual experiences due to the perceptible light blinking effects for end users. This greatly limits the deployment and applicable scenarios of VLC. In this paper, we design and develop LocalVLC, a practical and low-cost VLC system that can be used as a standard light source to augment smart IoT services. LocalVLC introduces a novel Morse-code inspired modulation scheme that can operate on off-the-shelf LEDs with low energy overhead. It can effectively overcome the light flickering by encoding data into high frequency light pulses without requiring extra processing hardware such as FPGA or micro-controller. We have implemented and evaluated a full-fledged system prototype based on LocalVLC design. Under practical settings, our LocalVLC prototype can support up to 10 meters of range, and attain reasonable throughput (up to 1.4 Kbps)with low error rate and energy consumption. Comparing with the widely adopted Manchester encoding, LocalVLC yields 8x improvement on both throughput and energy consumption. In addition, we demonstrate the practicality of LocalVLC through two IoT use cases where we developed two lightweight LocalVLC-based solutions using low-cost off-the-shelf hardware to exemplify the usage of LocalVLC for indoor service discovery and smart home key management.

Opportunistic networking is one way to realize pervasive applications while placing little demand on network infrastructure, especially for operating in less well connected environments. In contrast to the ubiquitous network access model inherent to many cloud-based applications, for which the web browser forms the user front end, opportunistic applications require installing software on mobile devices. Even though app stores (when accessible)offer scalable distribution mechanisms for applications, a designer needs to support multiple OS platforms and only some of those are suitable for opportunistic operation to begin with. In this paper, we present a web-based framework that allows users to interact with opportunistic application content without installing the respective app and thus also includes users whose mobile OSes do not support opportunistic networking at all via minimal stand-alone infrastructure. We describe our system and protocol design, validate its operation using simulations and a testbed. We implement web versions of two existing native mobile opportunistic applications: PeopleFinder and Here & Now.

This paper presents Webget, a measurement tool that measures Web quality of service (QoS) metrics, including the DNS lookup time, time to first byte (TTFB), and the download time. Webget also captures Web complexity metrics, such as the number and the size of objects that make up the website. We deploy the Webget test to measure the Web performance of Google, YouTube, and Facebook from 182 SamKnows probes. Using a 3.5-year-long (January 2014-July 2017) dataset, we show that the DNS lookup time of these popular content delivery networks (CDNs) and the download time of Google have improved over time. We also show that the TTFB toward Facebook exhibits worse performance than the Google CDN. Moreover, we show that the number and the size of objects are not the only factors that affect the Web download time. We observe that these webpages perform differently across regions and service providers. We also developed a Web measurement system, Web performance and rendering (WePR) that measures the same Web QoS and complexity metrics as Webget, but it also captures the Web quality of experience metrics, such as rendering time. WePR has a distributed architecture where the component that measures the Web QoS and complexity metrics is deployed on the SamKnows probe, while the rendering time is calculated on a central server. We measured the rendering performance of four websites. We show that in 80% of the cases, the rendering time of the websites is faster than the downloading time. The source code of the WePR system and the dataset is made publicly available.

Two major factors affecting mobile network performance are mobility and traffic patterns. Simulations and analytical-based performance evaluations rely on models to approximate factors affecting the network. Hence, the understanding of mobility and traffic is imperative to the effective evaluation and efficient design of future mobile networks. Current models target either mobility or traffic, but do not capture their interplay. Many trace-based mobility models have largely used pre-smartphone datasets (e.g., AP-logs), or much coarser granularity (e.g., cell-towers) traces. This raises questions regarding the relevance of existing models, and motivates our study to revisit this area. In this study, we conduct a multidimensional analysis, to quantitatively characterize mobility and traffic spatio-temporal patterns, for laptops and smartphones, leading to a detailed integrated mobility-traffic analysis. Our study is data-driven, as we collect and mine capacious datasets (with 30TB, 300k devices) that capture all of these dimensions. The investigation is performed using our systematic (FLAMeS) framework. Overall, dozens of mobility and traffic features have been analyzed. The insights and lessons learnt serve as guidelines and a first step towards future integrated mobility-traffic models. In addition, our work acts as a stepping-stone towards a richer, morerealistic suite of mobile test scenarios and benchmarks.

Adaptive streaming over HTTP has become the de-facto standard for video streaming over the Internet, partly due to its ease of deployment in a heavily ossified Internet. Though performant in most on-demand scenarios, it is bound by the semantics of TCP, with reliability prioritised over timeliness, even for live video where the reverse may be desired. In this paper, we present an implementation of MPEG-DASH over TCP Hollywood, a widely deployable TCP variant for latency sensitive applications. Out-of-order delivery in TCP Hollywood allows the client to measure, adapt and request the next video chunk even when the current one is only partially downloaded. Furthermore, the ability to skip frames, enabled by multi-streaming and out-of-order delivery, adds resilience against stalling for any delayed messages. We observed that in high latency and high loss networks, TCP Hollywood significantly lowers the possibility of stall events and also supports better quality downloads in comparison to standard TCP, with minimal changes to current adaptation algorithms.

"Roam like Home" is the initiative of the European Commission (EC) to end the levy of extra charges when roaming within the European region. As a result, people are able to use data services more freely across Europe. However, the implications roaming solutions have on performance have not been carefully examined. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in six countries across Europe. Using this platform, we measure different aspects of international roaming in 3G and 4G networks, including mobile network configuration, performance characteristics, and content discrimination. We find that operators adopt common approaches to implementing roaming, resulting in additional latency penalties of ∼60 ms or more, depending on geographical distance. Considering content accessibility, roaming poses additional constraints that leads to only minimal deviations when accessing content in the original country. However, geographical restrictions in the visited country make the picture more complicated and less intuitive.

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The throughput of a cellular network depends on a number of factors such as radio technology, limitations of device hardware (e.g., chipsets, antennae), physical layer effects (interference, fading, etc.), node density and demand, user mobility, and the infrastructure of Mobile Network Operators (MNO). Therefore, understanding and identifying the key factors of cellular network performance that affect end-users experience is a challenging task. We use a dataset collected using netradar, a platform that measures cellular network performance crowd- sourced from mobile user devices. Using this dataset we develop a methodology (a classifier using a machine learning approach) for understanding cellular network performance. We examine key characteristics of cellular networks related to throughput from the perspective of mobile user activity, MNO, smartphone models, link stability, location and time of day. We perform a network-wide correlation and statistical analysis to obtain a basic understanding of the influence of individual factors. We use a machine learning approach to identify the important features and to understand the relationship between different ones. These features are then used to build a model to classify the stability of cellular network based on the data reception characteristics of the user. We show that it is possible to classify reasons for network instability using minimal cellular network metrics with up to 90% of accuracy.

Advances in computational devices, wireless networking, cyber-physical integration and novel user interfaces are creating a world where we are continuously surrounded by a wealth of computational resources and new ways of interacting with them. However, we currently lack means of composing applications and services that can take full advantage of this environment. In this paper we present a system design that allows application developers to treat the future environment as a generic execution environment, which will automatically distribute and execute the components of their applications. This has the potential to unlock a wealth of currently unused resources and enable new classes of more immersive and useful applications and service that execute directly in the surrounding environment.

Scoped-flooding is used for content discovery in a broad networking context and it has significant impact on the design of caching algorithms in a communication network. Despite its wide usage, a thorough analysis on how scoped-flooding affects a network's performance, e.g., caching and content discovery efficiency, is missing. To develop a better understanding, we first model the behavior of scoped-flooding by the help of a theoretical model on network growth and utility. Next, we investigate the effects of scoped-flooding on various topologies in information-centric networks (ICNs). Using the proposed ring model, we show that flooding can be constrained within a small neighborhood to achieve most of the gains which come from areas with relatively low growth rate, i.e., the network edge. We also study two flooding strategies and compare their behaviors. Given that caching schemes favor more popular items in competition for cache space, popular items are expected to be stored in diverse parts of the network compared to the less popular items. We propose to exploit the resulting divergence in availability along with the routers' topological properties to fine tune the flooding radius. Our results shed light on designing both efficient content discovery mechanism and effective caching algorithms for future ICN.

We propose a new rate control algorithm for interactive real-time multimedia traffic, the FRACTaL algorithm. In our approach, the endpoint sends Forward Error Correction (FEC) packets not only for better error-resilience but also to probe for available bandwidth. The sender varies the amount of FEC to meet the sending rate calculated by the congestion control without changing the media rate. We evaluate our proposal in an emulated networking environment, using a set of reference test scenarios and compare it to the SCReAM congestion control algorithm. We find that FRACTaL performs better than SCReAM when competing with TCP flows, i.e., it is able to obtain its "fair" share. In other (non-TCP) scenarios, it achieves lower loss rates at comparable path utilization and queuing delay, i.e., FRACTaL delivers better and consistent media quality.

Mobile and wearable devices like smartphones or tablets are data hubs of our digital life and contain a high amount of sensitive data, which makes them a potential target for attackers. The aim of our P2Hub approach is to consider the privacy-by-architecture principle directly during the system design phase. We enhance the isolation of sensitive private information through a privacy-preserving module supported by novel, lightweight virtualization techniques. Thus, we inherently improve the system's security and privacy.

There is a growing interest in video performance measurements with emphasis on user experience and several initiatives have been taken to conduct active testing of real video services. A deeper understanding of the variations in media bit rate and its influence on the performance of video playback is needed in order to design better measurements. In this paper, we analyze a dataset of YouTube videos from various genres. We show statistically that most YouTube videos can be represented sufficiently well by the first 1 to 3 minutes of the video. This eliminates the need for running longer tests when network conditions are stable as in the case of fixed networks. We test our observation in an active testing environment that measures video metrics, and recommend based on the results that such tests should run at least for one minute, however, a duration of 3 minutes will help achieve better and more stable results.

Hop count limitation helps controlling the spread of messages as well as the protocol complexity and overhead in a distributed network. For a mobile opportunistic network, we examine how the paths between any two nodes change with increasing number of hops a message can follow. Using the all hops optimal path (AHOP) problem, we represent the total delay of a route from a source node to a destination node as additive weight and use the number of encounters as a representation of bottleneck weight. First, we construct a static (contact) graph from the meetings recorded in a human contact trace and then analyze the change in these two weights with increasing hop count. Alternatively, we aggregate all the contact events in a time interval and construct several time-aggregated graphs over which we calculate the capacity metrics. Although, we observe differences in the properties of the static and the time-aggregated graphs (e.g., higher connectivity and average degree in static graph), our analysis shows that second hop brings most of the benefits of multi-hop routing for the studied networks. However, the optimal paths —path that provides the most desirable bottleneck/additive weight— are achieved at further hops, e.g, hop count ≈ 4. Our finding, which is also verified by simulations, is paramount as it puts an upper bound on the hop count for the hop-limited routing schemes by discovering the optimal hop count for both additive and bottleneck weights.

While there is a drastic shift from host-centric networking to content-centric networking, how to locate and retrieve the relevant content efficiently, especially in a mobile network, is still an open question. Mobile devices host increasing volume of data which could be shared with the nearby nodes in a multi-hop fashion. However, searching for content in this resource-restricted setting is not trivial due to the lack of a content index, as well as, desire for keeping the search cost low. In this paper, we analyze a lightweight search scheme, hop-limited search, that forwards the search messages only till a maximum number of hops, and requires no prior knowledge about the network. We highlight the effect of the hop limit on both search performance (i.e., success ratio and delay) and associated cost along with the interplay between content availability, tolerated waiting time, network density, and mobility. Our analysis, using the real mobility traces, as well as synthetic models, shows that the most substantial benefit is achieved at the first few hops and that after several hops the extra gain diminishes as a function of content availability and tolerated delay. We also observe that the return path taken by a response is on average longer than the forward path of the query and that the search cost increases only marginally after several hops due to the small network diameter.

Social relationships are a natural basis on which humans make trust decisions. Online Social Networks (OSNs) are increasingly often used to let users base trust decisions on the existence and the strength of social relationships. While most OSNs allow users to discover the length of the social path to other users, they do so in a centralized way, thus requiring them to rely on the service provider and reveal their interest in each other.

Imagine if there is a privacy-preserving mechanism for two mobile devices to determine if their owners have common friends. It can be useful for access control in applications like ride-sharing, sharing Internet access or even just a simple "people radar" app for showing nearby friends and friends-of-friends. Current mechanisms for doing this come at the cost of revealing these interactions and the users' locations to central servers. In a paper that appears in the WiSec 2015 proceedings, we describe Social Pal [2], a framework that allows privacy-preserving discovery of the distance between two users in a social network. Social Pal was implemented as a general purpose software framework that can be easily used by application developers who wish to incorporate such functionality into their applications.

We consider a dynamic offloading problem arising in the context of mobile cloud computing (MCC). In MCC, three types of tasks can be identified: (i) those which can be processed only locally in a mobile device, (ii) those which are processed in the cloud, and (iii) those which can be processed either in the mobile or in the cloud. For type (iii) tasks, it is of interest to consider when they should be processed locally and when in the cloud. Furthermore, for both type (ii) and (iii) tasks, there is typically two ways to access the cloud: via a (costly) cellular connection or via intermittently available WLAN hotspots. The optimal strategy involves multi-dimensional considerations such as the availability of WLAN hotspots, energy consumption, communication costs and the expected delays. We approach this challenging problem in the framework of Markov decision processes and derive a near-optimal offloading policy.

In this paper, we propose a new rate control algorithm for conversational multimedia flows. In our approach, along with Real-time Transport Protocol (RTP) media packets, we propose sending redundant packets to probe for available bandwidth. These redundant packets are Forward Error Correction (FEC) encoded RTP packets. A straightforward interpretation is that if no losses occur, the sender can increase the sending rate to include the FEC bit rate, and in the case of losses due to congestion the redundant packets help in recovering the lost packets. We also show that by varying the FEC bit rate, the sender is able to conservatively or aggressively probe for available bandwidth. We evaluate our FEC-based Rate Adaptation (FBRA) algorithm in a network simulator and in the real-world and compare it to other congestion control algorithms.

Searching content in mobile opportunistic networks is a difficult problem due to the dynamically changing topology and intermittent connections. Moreover, due to the lack of global view of the network, it is arduous to determine whether the best response is discovered or search should be spread to other nodes. A node that has received a search query has to take two decisions: (i) whether to continue the search further or stop it at the current node (current search depth) and, independently of that, (ii) whether to send a response back or not. As each transmission and extra hop costs in terms of energy, bandwidth and time, a balance between the expected value of the response and the costs incurred must be sought. In order to better understand this inherent trade-off, we assume a simplified setting where both the query and response follow the same path. We formulate the problem of optimal search for the following two cases: a node holds (i) exactly matching content with some probability, and (ii) some content partially matching the query. We design static search in which the search depth is set at query initiation, dynamic search in which search depth is determined locally during query forwarding, and learning dynamic search which leverages the observations to estimate suitability of content for the query. Additionally, we show how unreliable response paths affect the optimal search depth and the corresponding search performance. Finally, we investigate the principal factors affecting the optimal search strategy.

We study delay tolerant networking (DTN) and in particular, its capacity to store, carry and forward messages to their final destination(s). We approach this broad question in the framework of percolation theory. To this end, we assume an elementary mobility model, where nodes arrive to an infinite plane according to a Poisson point process, move a certain distance ℓ, and then depart. In this setting, we characterize the mean density of nodes required to support DTN style networking. Under the given assumptions, we show that DTN communication is feasible when the mean node degree v is greater than 4 ηc(γ), where parameter γ= ℓ /d is the ratio of the distance ℓ to the transmission range d, and ηc(γ) is the critical reduced number density of tilted cylinders in a directed continuum percolation model. By means of Monte Carlo simulations, we give numerical values for ηc(γ). The asymptotic behavior of ηc(γ) when γ tends to ∞ is also derived from a fluid flow analysis.

The Internet infrastructure often supports multiple routes between two communicating hosts and, today, especially mobile hosts usually offer multiple network interfaces, so that disjoint paths between the hosts can be constructed. Having a number of (partly or fully) disjoint paths available may allow applications to distribute their traffic, aggregate capacity of different paths, choose the most suitable subset of paths, and support failover if a path fails. Exploiting multipath characteristics has been explored for TCP, but the requirements for real-time traffic differs notably. In this paper, we devise a multipath communication model for Real-time Transport Protocol (RTP); present minimal set of required protocol extensions; develop algorithms for scheduling RTP traffic across multiple paths at the sender and a corresponding de-jittering algorithm at the receiver side; and evaluate our proposal in varying scenarios using media traffic across different emulated mobile access network setups.

Mobile devices may often use WLAN hot-spots to communicate directly with one another in a peer-to-peer fashion without having to authenticate (and pay) for Internet access. This ad-hoc-style communication may impede the performance of regular Internet users' hot-spot experience. We present how LEDBAT (Low Extra Delay Background Transport) and its modified version, fLEDBAT, can be used as transport protocol between mobile devices in an infrastructure mode WLAN to reduce the impact on regular traffic. Although promising, the use of (f)LEDBAT may not solve all the problems arising from high-volume ad-hoc data traffic. Active queue management at the access point would do this but this would require controlling it. To overcome this, we complement LEDBAT by bundle admission control mechanisms based on the queueing delay and/or the number of active bundle transfers as alternative approaches. Both combined make device-to-device traffic yield to regular traffic, which we validate through extensive ns-2 simulations.

TCP and other Internet transport protocols rely on series of hard-coded initial values for their connection state, particularly for the congestion control parameters. For example, recently the initial value of congestion window has been under much debate, as there is a desire to make TCP more efficient for common use cases, while not endangering its performance on scenarios with limited network bandwidth. Our take on this discussion is that there is no clear single set of initial values that would work for all cases. Earlier research has proposed sharing connection and congestion control state among multiple connections over time, but that approach is limited to sharing connections to a particular host, which is not sufficient, because services are often distributed across multiple hosts, and opening multiple connections to the same host is a rather rare use case. We aim to solve this problem by proposing the Pathlet Transport Architecture that models the network paths as a series of pathlets, and uses those as the basis of initializing and maintaining the various transport parameters, particularly those related to congestion control. We analyze our initial instantiation of the PTA architecture using ns-3 simulations for TCP congestion control parameters, and show how it improves the communication performance in various different network scenarios, where single common set of magic values would fail.

We investigate the problem of estimating the transmission time of fragmented messages over multiple disrupted links. We build a system model for the case where a single message is sent over a chain of links and the disruptions in these links are identically and independently distributed. For this case, we derive an approximation formula for the mean transmission time, based on number of links, length of fragments and distributions of disruptions. The formula is verified against simulation experiments in the cases of uniform and exponential distributions for disruptions.

This paper presents a multimedia streaming service in a mobile (3G) environment that, in addition to in-band congestion signals such as packet losses and delay variations, receives congestion cues from a Network Coverage Map Service (NCMS) to make rate-control decisions. The streaming client routinely queries the NCMS to assess the network conditions at future locations along its expected path. The streaming client may ask the streaming server for short-term transmission bursts to increase pre-buffering when it is approaching areas with bad network performance to maintain media quality. If needed, the client may also switch to a different encoding rate (rate-switching) depending on the severity of expected congestion. These notifications are scheduled as late as possible, so that any changes in network conditions and/or changes in user's movements can be taken into account (late scheduling). Using this type of geo-predictive media streaming service we show that the streaming client can provide pause-less playback and better quality of experience to the user.

Heterogeneous networks are made up of wireless and wired links. The wireless link quality may vary due to interference and fading phenomena and, peered with radio and link layer reconfigurations lead to varying error rates, latencies, and, most importantly, changes in the available bit rate. In both fixed and wireless networks, varying amounts of cross traffic from other nodes may lead to fluctuations in queue size (reflected again in a path latency) and to congestion (reflected in packet drops from router queues). We propose a rate-adaptation algorithm for conversational video communication in a heterogeneous environment and use the standardized RTCP extensions as a starting point. We use metrics such as PSNR, loss rate, bandwidth utilization and fairness to evaluate the proposed algorithm.

We consider an opportunistic content sharing system designed to store and distribute local spatio-temporal “floating” information in uncoordinated P2P fashion relying solely on the mobile nodes passing through the area of interest, referred to as the anchor zone. Nodes within the anchor zone exchange the information in opportunistic manner, i.e., whenever two nodes come within each others' transmission range. Outside the anchor zone, the nodes are free to delete the information, since it is deemed relevant only for the nodes residing inside the anchor zone. Due to the random nature of the operation, there are no guarantees, e.g., for the information availability. By means of analytical models, we show that such a system, without any supporting infrastructure, can be a viable and surprisingly reliable option for content sharing as long as a certain criterion, referred to as the criticality condition, is met. The important quantity is the average number of encounters a randomly chosen node experiences during its sojourn time in the anchor zone, which again depends on the communication range and the mobility pattern. The theoretical studies are complemented with simulation experiments with various mobility models showing good agreement with the analytical results.

Several recently proposed multicast protocols use in-packet Bloom filters to encode multicast trees. These mechanisms are in principle highly scalable because no per-flow state is required in the routers and because routing decisions can be made efficiently by simply checking for the presence of outbound links in the filter. Yet, the viability of previous approaches is limited by the possibility of forwarding anomalies caused by false positives inherent in Bloom filters. This paper explores such anomalies, namely (1) packets storms, (2) forwarding loops and (3) flow duplication. We propose stateless solutions that increase the robustness and the scalability of Bloom filter-based multicast protocols. In particular, we show that the parameters of the filter need to be varied to guarantee the stability of the packet forwarding, and we present a bit permutation technique that effectively prevents both accidental and maliciously created anomalies. We evaluate our solutions in the context of BloomCast, a source-specific inter-domain multicast protocol, using analytical methods and simulations.

A “best-effort” authentication method, which is easier to attack than generic authentication methods (but requires fewer computations for benign nodes), may be sufficient for certain networking scenarios. We illustrate this point by examining the case of fragment authentication by intermediaries in an opportunistic network. We describe mechanisms for implementing best-effort authentication, with the caveat that an authentication strength sufficient in one scenario may be unfit for another.

Content sharing using personal web pages, blogs, or online social networks is a common means for people to maintain contact with their friends, colleagues, and acquaintances. While such means are essential to overcome distances, using infrastructure services for location-based services may not be desirable. In this paper, we analyze a fully distributed variant of an ephemeral content sharing service, solely dependent on the mobile devices in the vicinity using principles of opportunistic networking. The net result is a best effort service for floating content in which: 1) information dissemination is geographically limited; 2) the lifetime and spreading of information depends on interested nodes being available; 3) content can only be created and distributed locally; and 4) content can only be added, but not explicitly deleted. First we present our system design and summarize its analytical modeling. Then we perform extensive evaluation for a map-based mobility model in downtown Helsinki to assess the operational range for floating content, which, at the same time also validate the analytical results obtained for a more abstract model of the system.

When applying delay-tolerant networking concepts to communication in mobile ad-hoc networks formed between mobile users, a general assumption is that users are willing to share own resources to support communication between others. However, we cannot assume that all users are altruistic in their behavior; instead, we have to deal with users who only make a limited or no contribution to the mobile community. Nodes not participating in communication only reduce the effective node density, but do not consume resources. Others act as sources and sinks but perform only limited or no forwarding and thus may impact the overall network performance. When considering routing in mobile DTNs, such selfish nodes have to be considered. We introduce two types of selfish nodes and evaluate their impact on message delivery performance for different routing protocols by means of simulation in different synthetic mobility models and with real-world traces. We find that their impact can be surprisingly low in our scenarios, suggesting that DTN communication can be quite robust against selfishness and that controlled non-cooperative behavior may be a suitable way to overcome resource limitations, such as battery depletion.

Quality of experience (QoE) is an important, and admittedly overloaded, concept for the emerging IPTV services. Service providers are continuously working towards delivering a better TV experience. To this effect, cost-effective and scalable tools are highly desirable for QoE monitoring, diagnostics and reporting. In this paper, we demonstrate that the RTP control protocol and its network tomography extensions can satisfy the needs of the providers in collecting and reporting both detailed and summarized information using several numerical examples based on real-life scenarios.

In this paper, we study how to adapt the routing according to dynamic network conditions in wireless ad hoc networks. We present a method that dynamically chooses routing agent between ad hoc on-demand distance vector routing protocol (AODV) with TCP (end-to-end transport) and delay-tolerant networking (DTN) routing and bundle protocol (hop-by-hop transport). We use simulations to confirm that DTN routing and the bundle protocol leads to significantly shorter end-to-end delays and higher message delivery ratios than AODV and end-to-end TCP when the wireless node density is low. However, with high node density, DTN routing, especially epidemic routing, suffers from multiple bundle copies and simultaneous transmissions that lead into collisions and retransmissions at the wireless link (MAC) layer. Thus, we propose a simple adaptive scheme that uses only local information to transmit the messages from source to destination using either AODV or DTN routing, depending on current node density, message size, and path length to destination.

Mobile phones are becoming commonplace for consuming Internet content and services. However, availability, affordability, and quality of the supposedly ubiquitous cellular network infrastructure may be limited, so that delay-tolerant web access via WLAN hotspots becomes an interesting alternative, even in urban areas. In this paper we explore mobile web access using asynchronous messaging via WLAN hotspots: for nodes directly connected to an access point and nodes relying on others for message forwarding. We investigate different routing and caching approaches using real-world access point locations in Helsinki. We find that a significant number of requests can be satisfied without requiring an always-on infrastructure, provided that users are willing to tolerate some response delay; this allows offloading traffic from the cellular network. We also report on our prototype implementation of mobile DTN-based web browsing.

Delay-tolerant networking (DTN) provides a platform for applications in environments where end-to-end paths may be highly unreliable or do not exist at all. In many applications such as distributed wikis or photo sharing, users need to be able to find content even when they do not know an unambiguous identifier. In order do bring these applications to the domain of DTNs, a search scheme is required that works despite the unreliable network conditions. In this paper, we introduce a search scheme that makes no assumptions about the underlying routing protocols and the format of search requests. We evaluate different algorithms for forwarding and terminating search queries, using simulations with different classes of DTN routing protocols for different mobility scenarios.

The quality of Internet access for mobile users may suffer from highly variable communication characteristics (packet loss, delay, throughput) and from temporary disconnections. The former occur as a result of changing radio properties, handovers, or variable system load, the latter whenever wireless coverage is insufficient. While quite some research has tackled improving radio coverage to keep users always best connected and numerous approaches pursue improving wireless performance and robustness, these often assume a greenfield deployment or a tight integration with operators. We present the mobility support system architecture developed in the CHIANTI project. While building on related work in various technical respects, the CHIANTI architecture is specifically designed to be instantly and incrementally deployable in today's Internet landscape, considering real-world legal and deployment constraints, and supports roles of different (independent) players. We also report on our proof-of-concept implementation.

Communication in 3G networks may experience packet losses due to transmission errors on the wireless link(s) which may severely impact the quality of video services, with conversational video being most challenging to repair due to tighter delay constraints. Many error resilience mechanisms have been developed that can be applied at the source (codec) level and transport/application layer to address these challenges. Their respective performance varies depending on the network conditions. This paper analyzes and compares the performance of four error resilience mechanisms under different realistic wireless link conditions: selective retransmissions, slice size adaptation, reference picture selection, and unequal error protection using packet-based forward error correction. We derive suggestions for the applicability of the individual mechanisms.

Delay-tolerant networking is used for communication in challenged environments such as MANETs, in which links are unstable and end-to-end paths between communicating nodes may not exist. Messages may be significantly larger than packets in IP networks. Large messages lead to longer transfer times rendering it more likely that a link breaks in the middle of a message transfer. This motivates investigating how to support partial message transfers through fragmentation. In this paper, we formulate fragmentation independent of routing algorithms, introduce several fragmentation strategies, and evaluate these by simulations to derive recommendations for using fragmentation in DTNs.

Mobile users may experience short disruptions and premature call terminations (not just) when moving while engaged in a (multimedia) conversation. While much emphasis is put on enabling seamless mobility, recovery in case of (temporary) service failures has received little attention. In this paper, we explore the technical aspects of disconnection tolerance mechanisms for SIP-based mobile communications. We present standards-compliant detection and recovery mechanisms, provide a brief experimental evaluation based upon packet traces, and report on our prototype implementation.

Delay-tolerant networking (DTN) enables nodes to communicate by means of asynchronous messaging witout the need for an end-to-end path. Suitably designed application protocols may operate in DTNs by minimizing end-to-end interactions and using self-contained messages for communication. The store-carry-and-forward operation and message replication of many DTN routing protocols may yield multiple copies of messages spread across many nodes for an axtended period of time. We leverage these properties for application support in (mobile) intermediate DTN nodes which act as ad-hoc routers. We add explicit application hints to messages that are visible to each node, allowing them, e.g., to cache content, act as distributed storage, or perform application-specific forwarding.

Future heterogeneous wireless networks will encompass different link layer technologies and allow selecting the most appropriate network depending on different criteria. To support mobile nodes in the selection process, network information services are developed that provide the mobile node with sufficient information about its network neighborhood, typically focusing on the optimization of handover processes. In this paper, we take a more general approach towards network information services, which is needed to support mobile communications in the existing environments of WLAN hotspots and wide area mobile communications networks. We introduce the notion of "service maps", a mobile data management approach allowing a mobile user to obtain a detailed view of available networks and the services they offer depending on the user context such as geographic position, mobility paths, and application requirements. We review our experiences with networking in highly mobile scenarios from which we derive a set of requirements for a general information service and present our system design and data model that also accounts for business and (incremental) deployment considerations

Today's mobile, wireless, and ad-hoc communications often exhibit extreme characteristics challenging assumptions underlying the traditional way of end-to-end communication protocol design in the Internet. One specific scenario is Internet access from moving vehicles on the road as we are researching in the drive-thru Internet project. Using wireless LAN as a broadly available access technology leads to intermittent - largely unpredictable and usually short-lived - connectivity, yet providing high performance while available. To allow Internet applications to deal reasonably well with such intermittent connectivity patterns, we have introduced a supportive drive-thru architecture. A key component is a "session" protocol offering persistent end-to-end communications even in the presence of interruptions. In this paper, we present the design of the persistent connectivity management protocol (PCMP) and report on findings from our implementation.

This paper reports on measurement results for the use of IEEE 802.11 networks in drive-thru scenarios: we have measured transmission characteristics for sending and receiving high data volumes using UDP and TCP in vehicles moving at different speeds that pass one or more IEEE 802.11 access points at the roadside. We discuss possibilities and limitations for the use of scattered WLAN cells by devices in fast moving vehicles and provide an analysis of the performance that can he expected for the communication in such scenarios. Based on these observations, we discuss implications for higher-layer protocols and applications.

Version 2 of ITU Recommendation H.263, better known as H.263+, includes a number of new mechanisms to improve coding efficiency and support various types of networks more efficiently. This paper provides an overview of the error resilience optional modes of H.263+ and describes the use of such modes in various multimedia network scenarios.