The measurement of network speed, specifically broadband speed/throughput as measured by tools like iPerf, has long been used as a key performance indicator for home broadband. Unfortunately, home users rarely have the capability to conduct reliable wired tests, instead being only able to measure using Wi-Fi. Hence, home wireless is often viewed as an unreliable indicator of network speed, leaving home users with little recourse to challenge the quality of broadband speed being delivered. To that end, we seek to answer the extent to which such tests are unreliable and, more importantly, to understand if one can accurately determine if the result was indicative of broadband as a bottleneck or if the measurement was limited by Wi-Fi. In our paper, we demonstrate that such a determination is eminently possible and, moreover, such a determination can be done drawing only on features and groups of features already reported by iPerf. We show through extensive experiments that one can capture the goodness (test was indicative of broadband speeds) or badness (test was not indicative of broadband speeds) with a 92. 5% accuracy, drawing only on the median throughput and interquartile range with second-by-second windowing reported by iPerf. Finally, we show that there is a negative correlation between the ratio of Wi-Fi throughput to Ethernet (broadband) throughput and the interquartile range normalized with its corresponding Wi-Fi throughput.

Swiping the screen to switch videos is a unique browsing behavior for short videos, intended to facilitate viewers in quickly searching for content of interest. However, frequent video switching can result in nearly half of the data being used to transmit never-watched video data, leading to unnecessary network load via resource wastage. To tackle this problem, recent studies have utilized historical viewing data to predict the necessary length of videos to download based on viewing probability. Critically, the precision of the predictions plays a pivotal role in shaping both data consumption and user experience. This paper addresses issues that emerge with inaccurate predictions by proposing KBL (Kettle-style Buffer Loading), a novel algorithm to balance waste with a high-quality video experience, without requiring extensive training or prior knowledge. Inspired by tea kettle service, KBL reduces waste by setting the boundary of the respective video buffers, current and pre-loaded videos, based on an evaluation of the network conditions. Through extensive evaluation, KBL is demonstrated to reduce waste by up to 58% of data usage compared to state of the art short video strategies without incurring significant QoE degradation, even in the face of shifting user behavior.

Understanding the real-world end-to-end performance of residential Wi-Fi and broadband networks is essential for consumers, service providers, and policy makers in an increasingly connected society to determine how connectivity can be improved, through better technology or appropriate spectrum decisions. While previous research has studied such performance, the focus has primarily been on the wired Internet, pointing to Wi-Fi as a bottleneck without investigating the reasons thereof. We believe that the preliminary results presented in this paper, from in-depth studies of residential Wi-Fi deployments in a small Midwestern town, offer a first look into how the Wi-Fi environment may impact the user experience. Over a period of six months, we deployed single-board computers to measure the throughput and latency of Wi-Fi and the backhaul broadband network in a number of different residential environments, as well as capture the Wi-Fi signal environment. Our analyses show: (i) interoperability issues between AP and client supporting different Wi-Fi amendments (e.g., 802.11ac, 802.11ax); (ii) a lack of smart interference management and Dynamic Frequency Selection (DFS) support, resulting in over-usage of channels in the U-NII-1 and U-NII-3 bands when other, less congested bands may be available.

Network throughput tests, commonly known as “speed tests” are widely used by consumers, regulators, and ISPs to measure and diagnose network performance. However, the tools used to conduct these tests are often costly in terms of data consumption. Moreover, the speed tests rely on data that is transferred to clients for the sole purpose of measuring throughput with the data being discarded and serving no other purpose. In this paper, we present rePurpose, a system that moves useful content (ads) to enable periodic speed tests by significantly offsetting the cost of network measurement, thereby avoiding harming user QoE. rePurpose can work within the existing ad ecosystem to pre-stage ads needed by users ahead of time. We evaluate the efficacy of rePurpose by emulating a common scenario where users watch videos and ads. Our evaluation shows that rePurpose can reduce the data cost of periodic speed tests by up to 90%. Moreover, by virtue of the time-shifted delivery courtesy of the periodic speed tests moving useful data, rePurpose improves video and ad QoE by reducing or eliminating start-up delay by up to five seconds.

Active network probing, commonly known as a speed test, is the prevalent network speed measurement and diagnostic method. Speed tests primarily measure achievable throughput by conducting bulk downloads that saturate the bottleneck link. However, the impact of speed tests on user Quality of Experience (QoE) has not been thoroughly explored. In this paper, we investigate the effects of active network probing on user QoE during two common activities: file downloading and video streaming, focusing on key QoE metrics such as download time, video bitrate, and buffering. Our analysis reveals that the standard speed test significantly extends download times (by up to 88% in WiFi and 46% in cellular networks) and adversely affects various video QoE metrics, particularly bitrate, resulting in an average bitrate reduction ranging from 46% to 60%. Moreover, we assess the outcomes of typical speed test scenarios, such as single and double tests, and establish that both variants impair QoE, with double tests causing greater disruptions. Our findings offer a comprehensive insight into the ramifications of active network probing on user applications and emphasize the necessity for approaches to alleviate its detrimental effects on QoE.

Much emphasis has been placed on how the affordances and layouts of an office setting can influence co-worker interactions and perceived team outcomes. Little is known, however, whether perceptions of teamwork and team conflict are affected when the location of work changes from the office to the home. To address this gap, we present findings from a ten-week,in situ study of 91 information workers from 27 US-based teams. We compare three distinct work locations---private and shared workspaces at home as well at the office---and explore how each location may impact individual perceptions of teamwork. While there was no significant association with participants' perceptions of teamwork, results revealed associations of work location with team conflict: participants who worked in a private room at home reported significantly lower team conflict compared to those working in the office. No difference was found for the office and the shared workspace. We further found that the influence of work location on team conflict interacted with job decision latitude and the level of task interdependence among co-workers. We discuss practical implications for full-time work from home (WFH) on teams. Our study adds an important environmental dimension to the literature on remote teaming, which in turn may help organizations as they consider, prepare, or implement more permanent WFH and/or hybrid work policies in the future.

Short videos have recently emerged as a popular form of short-duration User Generated Content (UGC) within modern social media. Short video content is generally less than a minute long and predominantly produced in vertical orientation on smartphones. While still fundamentally being streaming, short video delivery is distinctly characterized by the deployment of a mechanism that pre-loads ahead of user request. Background pre-loading aims to eliminate start-up time, which is now prioritized higher in Quality of Experience (QoE) objectives, given that the application design facilitates instant 'swiping' to the next video in a recommended sequence. In this work, we provide a comprehensive comparison of four popular short video services. In particular, we explore content characteristics and evaluate the video quality across resolutions for each service. We next characterize the pre-loading policy adopted by each service. Last, we conduct an experimental study to investigate data consumption and evaluate achieved QoE under different network scenarios and application configurations.

Working remotely from home during the COVID-19 pandemic has resulted in significant shifts and disruptions in the personal and work lives of millions of information workers and their teams. We examined how sleep patterns---an important component of mental and physical health---relates to teamwork. We used wearable sensing and daily questionnaires to examine sleep patterns, affect, and perceptions of teamwork in 71 information workers from 22 teams over a ten-week period. Participants reported delays in sleep onset and offset as well as longer sleep duration during the pandemic. A similar shift was found in work schedules, though total work hours did not change significantly. Surprisingly, we found that more sleep was negatively related to positive affect, perceptions of teamwork, and perceptions of team productivity. However, a greater misalignment in the sleep patterns of members in a team predicted positive affect and teamwork after accounting for individual differences in sleep preferences. A follow-up analysis of exit interviews with participants revealed team-working conventions and collaborative mindsets as prominent themes that might help explain some of the ways that misalignment in sleep can affect teamwork. We discuss implications of sleep and sleep misalignment in work-from-home contexts with an eye towards leveraging sleep data to facilitate remote teamwork.

During the COVID-19 pandemic, millions of previously co-located information workers had to work from home, a trend expected to become much more commonplace in the future. We interviewed 53 information workers from 17 U.S. teams to understand how this unique extended work-from-home setting influenced teamwork and how they adapted to it. Using a grounded theory approach, we discovered that extended remote work highlighted diversity in team members' home-lives and daily work rhythms. Whereas these types of diversity played only marginal roles for teams in the co-located office, they had a more tangible impact in the work-from-home setting, from coordination delays and interruptions to conflicts related to workload fairness, miscommunication, and trust. Importantly, workers reported that their teams adapted to these challenges by setting explicit norms and standards for online communication and asynchronous collaboration and by promoting general social and situational awareness. We discuss computer-supported designs to help teams manage these latent diversities in an extended remote teamwork setting.

Histograms have a large variety of useful applications in data analysis, e.g., tracking the spread of diseases and analyzing public health issues. However, most data analysis techniques used in practice operate over plaintext data, putting the privacy of users’ data at risk. We consider the problem of allowing an untrusted aggregator to privately compute a histogram over multiple users’ private inputs (e.g., number of contacts at a place) without learning anything other than the final histogram. This is a challenging problem to solve when the aggregators and the users may be malicious and collude with each other to infer others’ private inputs, as existing black box techniques incur high communication and computational overhead that limit scalability. We address these concerns by building a novel, efficient, and scalable protocol that intelligently combines a Trusted Execution Environment (TEE) and the Durstenfeld-Knuth uniformly random shuffling algorithm to update a mapping between buckets and keys by using a deterministic cryptographically secure pseudorandom number generator. In addition to being provably secure, experimental evaluations of our technique indicate that it generally outperforms existing work by several orders of magnitude, and can achieve performance that is within one order of magnitude of protocols operating over plaintexts that do not offer any security.

In the past few years, unmanned aerial vehicles (UAVs) have drastically increased in popularity both from consumer and industry perspectives. A key component towards enabling the widespread usage of UAVs is the ability to stay in near-constant communication with the drone for command and control and conveying relevant instrumentation. The usage of cellular technology, namely LTE, seems to be a natural fit for addressing coverage and Line of Sight (LoS) issues. However, there is a relative dearth of data, specifically open source data that explores key performance aspects of cellular at altitudes typically envisioned for commercial UAV operation. The key contribution of this paper is to analyze data taken from numerous drone flights that include varying altitudes, locations, and multiple cellular carriers as recorded in a medium-sized Midwestern city. Further, we offer our data as an open-source repository for the community offering multiple vantage points for the various runs including the operating system, chipset (through MobileInsight), drone instrumentation, and server-side packet captures as part of the recorded data streams.

Cellular networks provide an essential connectivity foundation for a sizable number of mobile devices and applications, making it compelling to measure their performance in regard to user experience. Although cellular infrastructure provides low-level mechanisms for network-specific performance measurements, there is still a distinct gap in discerning the actual application-level or user-perceivable performance from such methods. Put simply, there is little substitute for direct sampling and testing to measure end-to-end performance. Unfortunately, most existing technologies often fall quite short. Achievable Throughput tests use bulk TCP downloads to provide an accurate but costly (time, bandwidth, energy) view of network performance. Conversely, Available Bandwidth techniques offer improved speed and low cost but are woefully inaccurate when faced with the typical dynamics of cellular networks. In this paper, we propose CUP, a novel approach for Cellular Ultra-light Probe-based available bandwidth estimation that seeks to operate at the cost point of Available Bandwidth techniques while correcting accuracy issues by leveraging the intrinsic aggregation properties of cellular scheduling, coupled with intelligent packet timing trains and the application of Bayesian probabilistic analysis. By keeping the costs low with reasonable accuracy, our approach enables scaling both with respect to time (longitude) and space (user device density). We construct a CUP prototype to evaluate our approach under various demanding real-world cellular environments (longitudinal, driving, multiple vendors) to demonstrate the efficacy of our approach.

The advancement of the Internet of Things (IoT) is bringing unprecedented convenience into our daily life. However, with the relentlessly increasing number of mobile devices connected to the Internet, the wireless network environment is becoming more crowded than ever before. Particularly, WiFi, with its evolving role in IoT, is shouldering a tremendous amount of traffic from IoT and other mobile devices. As a result, exploding numbers of competing devices, encroachment by cellular technology, and dramatic increases in content richness deliver a more variable Quality of Experience (QoE) on WiFi than desired. Moreover, such variance tends to occur both across time and space making it an extremely difficult problem to debug. Existing active approaches tend to be expensive or impractical while existing passive approaches tend to be too narrow. To conduct efficient and nonobtrusive WiFi traffic characterization, in this article, we propose a novel passive client-side approach that delivers efficient and accurate characterization by taking advantage of the properties of frame aggregation (FA) and block acknowledgment (BA). The devised approach requires only capturing and analyzing certain types of control packets thus making it feasible to deploy on IoT devices that have limited computation power. We show in this article that we can accurately derive important characterization metrics, such as airtime, queuing information, and transmission rates with only a minimal amount of observed BAs. We show through extensive experiments the validity of our approach and conduct validation studies in the dense environment of a campus tailgate.

Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich heterogeneous dataset from the University of Notre Dame’s NetHealth study that collected individuals’ (student participants’) social interaction data via smartphones, health-related behavioral data via wearables (Fitbit), and trait data from surveys. To integrate the different types of information, we model the NetHealth data as a heterogeneous information network (HIN). Then, we redefine the problem of predicting individuals’ mental health conditions (depression or anxiety) in a novel manner, as applying to our HIN a popular paradigm of a recommender system (RS), which is typically used to predict the preference that a person would give to an item (e.g., a movie or book). In our case, the items are the individuals’ different mental health states. We evaluate four state-of-the-art RS approaches. Also, we model the prediction of individuals’ mental health as another problem type—that of node classification (NC) in our HIN, evaluating in the process four node features under logistic regression as a proof-of-concept classifier. We find that our RS and NC network methods produce more accurate predictions than a logistic regression model using the same NetHealth data in the traditional non-network fashion as well as a random-approach. Also, we find that the best of the considered RS approaches outperforms all considered NC approaches. This is the first study to integrate smartphone, wearable sensor, and survey data in a HIN manner and use RS or NC on the HIN to predict individuals’ mental health conditions.

WiFi has emerged as a pivotal technology for delivering Quality of Experience (QoE) to mobile devices. Unfortunately, exploding numbers of competing devices, potential encroachment by cellular technology, and dramatic increases in content richness deliver a more variable QoE than desired. Moreover, such variance tends to occur both across time and space making it a difficult problem to debug. Existing active approaches tend to be expensive or impractical while existing passive approaches tend to suffer from accuracy issues. In our paper, we propose a novel passive client-side approach that provides an efficient and accurate characterization by taking advantage of the properties of Frame Aggregation (FA) and Block Acknowledgements (BA). We show in the paper that one can accurately derive important metrics such as airtime and throughput with only a minimal amount of observed BAs. We show through extensive experiments the validity of our approach and conduct validation studies in the dense environment of a campus tailgate.

Video streaming using WiFi networks poses the challenge of variable network performance when multiple clients are present. Hence, it is important to continuously monitor and predict the network changes in order to ensure a higher user quality of experience (QoE) for video streaming. Existing approaches that aim to detect such network changes have several disadvantages. For example, active probing approaches are expensive so that generate more additional traffic flow during the testing. To overcome its shortcomings, we propose a passive, lightweight approach, CP-DASH, whereby queuing effects present in frame aggregation are leveraged to predict link congestion in the WiFi network. This approach allows the early detection which can be used to adapt our video appropriately. We conduct experiments simulating a WiFi network with multiple clients and compare CP-DASH with five contemporary rate selection mechanisms. We found that our proposed method significantly reduces the switch rates and stall rates from 22% to 5% and from 38% to 25% compared with an existing throughput-based algorithm, respectively.

The ubiquitous use of social media enables researchers to obtain self-recorded longitudinal data of individuals in real-time. Because this data can be collected in an inexpensive and unobtrusive way at scale, social media has been adopted as a “passive sensor” to study human behavior. However, such research is impacted by the lack of homogeneity in the use of social media, and the engineering challenges in obtaining such data. This paper proposes a statistical framework to leverage the potential of social media in sensing studies of human behavior, while navigating the challenges associated with its sparsity. Our framework is situated in a large-scale in-situ study concerning the passive assessment of psychological constructs of 757 information workers wherein of four sensing streams was deployed - bluetooth beacons, wearable, smartphone, and social media. Our framework includes principled feature transformation and machine learning models that predict latent social media features from the other passive sensors. We demonstrate the efficacy of this imputation framework via a high correlation of 0.78 between actual and imputed social media features. With the imputed features we test and validate predictions on psychological constructs like personality traits and affect. We find that adding the social media data streams, in their imputed form, improves the prediction of these measures. We discuss how our framework can be valuable in multimodal sensing studies that aim to gather comprehensive signals about an individual's state or situation.

The age of the Internet of things has brought about the field of smartphone sensing, which has found numerous uses for the growing population of smartphones throughout the world. Among these uses are longitudinal studies that have studied social networks, health behaviors, productivity, and other topics with large participant bases and detailed data. In this paper we demonstrate another way to study social behavior during one of these longitudinal studies, the NetHealth study, by predicting whether a pair of students are friends or not through visit data patterns of a social hub of any college campus - the dining hall. We use Bluetooth Low Energy (BLE) beacons deployed throughout the dining hall to detect when students enter and leave, and create features from this visit data that can cope with varying amounts of data among study participants. We then demonstrate the predictive power of these features, calculated over a semester of data, against ground truth friendship labels given by the participants themselves, obtaining an AUROC of 0.716.

WiFi serves as one of the key mechanisms for wireless access for mobile devices whether at home, on travel, or during normal day-to- day activities. Unfortunately, the perceived high bandwidth and low cost of WiFi is often tempered with varying degrees of quality. Compounding this further, existing techniques for assessing network performance are often expensive in terms of time, bandwidth, and energy making them ill-suited for widespread, longitudinal deployment. In this paper, we propose Fast Mobile Network Characterization (FMNC) to address this shortcoming. FMNC uses sliced, structured, and reordered packet sequences along with an awareness of frame aggregation to rapidly characterize available bandwidth. FMNC does this within the context of a single HTTP GET, consuming less than 100 KB on the downlink with resolution of the path characteristics typically occurring in under 250 ms. We demonstrate the performance of FMNC through extensive lab experiments under a variety of configuration scenarios.

The rise of video streaming has placed significant demands on network infrastructure. These demands are most acutely felt in the wireless space where limited resources are available. Compounding the matter, most techniques for adapting to network dynamics have been developed with wired networks in mind thus making performance in congested wireless networks, especially WiFi, quite problematic. In this paper, we propose a novel cross-layer design to improve video bitrate selection by incorporating MAC layer information. We design a lightweight channel characterization method that can provide an accurate airtime estimation based on the observation of WiFi control packets. We then devise a bitrate adaptation algorithm that can judiciously avoid faulty bitrate increases whenever severe channel competition is detected. Through extensive lab experiments, we show that our proposed method can significantly reduce video stall rates by up to 30x (from 65% to 2%) compared to existing methods.

The past decade has seen incredible growth in wireless consumption with newly emergent technologies such as the Internet of Things (IoT) slated to radically increase wireless network demands. A significant body of research has operated from the perspective of viewing the Quality of Experience (QoE) of the user as paramount with the network and protocols viewed as a service to accomplish those goals. While we concur that QoE is paramount, we flip the role of the provider and posit that interesting architectures can be composed if the user is willing to relinquish control of their storage to the provider. In this paper, we describe PASS, Provider Accessible Storage Service, whereby network providers are allowed to manage, write, and even sell spare space on the user device for the purpose of improving network efficiency. We describe PASS from a high level vision and then walk through a realization of PASS focused on HTTP content fetches.

WiFi has emerged as a pivotal technology for mobile devices offering the potential for exceptional connectivity speeds. Unfortunately, the performance of WiFi may vary significantly making WiFi link characterization (and more broadly the path characterization) an essential element of the user Quality of Experience (QoE). The key challenge that emerges with respect to link characterization is how to characterize performance in an efficient manner. In this paper, we explore how the existing frame aggregation mechanisms introduced by 802.11e can be leveraged to achieve such a goal. We show not only how frame aggregation breaks existing lightweight mechanisms for link characterization but also how to carefully construct packet sequences that induce frame aggregation to capture the WiFi available bandwidth. We construct a proof of concept system, AIWC (Aggregation Intensity based Wifi Characterization), to demonstrate the aforementioned concepts with significant improvements versus prior work.

During the one minute it takes to read this abstract, two billion smartphones worldwide will perform billions of Wifi channel scans recording the signal strength of nearby Wifi Access Points (APs). Yet despite this ongoing planetary-scale wireless network measurement, few systematic efforts are made today to recover this potentially valuable data. In this paper we ask the question: “Are the smartphone channel scans useful in monitoring enterprise Wifi networks?” More specifically, can these client-side measurements provide new insights compared to the AP-side measurements that enterprise Wifi networks already perform? Beginning with two Wifi scan datasets collected on two large scale smartphone testbeds, we conduct case studies that show how smartphone channel scans can be used to (1) improve AP spectrum management, and (2) predict the impact of AP failure or overload. In each case, a walk on the client side yields valuable insights for network operators that are otherwise impossible to gain from AP-side measurements, and together our results demonstrate the value of smartphone channel scans.

A key part of achieving a reasonable Quality of Experience (QoE) for the mobile user is the ability to properly assess the quality of the available network capacity. Unfortunately, most existing techniques are ill-suited to wireless characterization, either providing fast but grossly inaccurate results or providing accurate results at the cost of time and bandwidth. To meet this need, we developed Fast Mobile Network Characterization (FMNC). FMNC operates within a web fetch and provides an accurate available bandwidth assessment within 250 ms while consuming less than 100 KB of data. In our demo, we will show the mobile clients for FMNC including apps for Android / iOS, the server back end, and a REST API. We will also show visualization of our ongoing longitudinal dataset of nearly one hundred users.

MOTIVATION: Network alignment (NA) aims to find regions of similarities between species' molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. GNA finds large conserved regions and produces a one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category. However, both NA categories have the same goal: to allow for transferring functional knowledge from well- to poorly-studied species between conserved network regions. So, which one to choose, LNA or GNA? To answer this, we introduce the first systematic evaluation of the two NA categories. RESULTS: We introduce new measures of alignment quality that allow for fair comparison of the different LNA and GNA outputs, as such measures do not exist. We provide user-friendly software for efficient alignment evaluation that implements the new and existing measures. We evaluate prominent LNA and GNA methods on synthetic and real-world biological networks. We study the effect on alignment quality of using different interaction types and confidence levels. We find that the superiority of one NA category over the other is context-dependent. Further, when we contrast LNA and GNA in the application of learning novel protein functional knowledge, the two produce very different predictions, indicating their complementarity. Our results and software provide guidelines for future NA method development and evaluation. AVAILABILITY AND IMPLEMENTATION: Software: http://www.nd.edu/~cone/LNA_GNA CONTACT: : [email protected] information: Supplementary data are available at Bioinformatics online.

WiFi offloading has emerged as a key component of cellular operator strategy to meet the rich data needs of modern mobile devices. Hence, mobile devices tend to aggressively seek out WiFi in order to provide improved user Quality of Experience (QoE) and cellular capacity relief. For home and work environments, aggressive WiFi scans can significantly improve the speed at which mobile nodes join the WiFi network. Unfortunately, the same aggressive behavior that excels in the home environment incurs considerable side effects in crowded wireless environments. In this paper, we analyze empirical data collected from large (stadium) and medium (classroom) venues, and show through controlled experiments (laboratory) how aggressive WiFi scans can have significant implications for energy and throughput for mobile nodes. We close with several thoughts on the disjoint incentives for properly balancing WiFi discovery speed and crowded network interactions.

The ability to sense acoustic emotion from a smartphoneis advantageous for two main reasons. First, smartphonesensing is unobtrusive compared to wearing a microphone, and second, a smartphone is nearly always with the user. When sensing emotion over a long period of time, these two reasons become increasingly more important. We demonstrate the challenges of building a system for longitudinal emotion sensing on a smartphone, as well as our design approach to these challenges. Current emotion sensing systems perform all sensing and computation on the phone, but this design can lead to significant battery life constraints. We show that in terms of energy consumption, offloading feature and classification computation to a remote server is the most feasible design choice without excessive battery draining, and discuss how we address the privacy, energy, and storage concerns of such an approach.

The rise in motion-based gaming peripherals has afforded intriguing opportunities for low-cost instrumentation of health-oriented activities. One particular activity, that of physical therapy, is of considerable interest as traditional systems in the area cost on the order of tens of thousands of dollars. However, while recent research has shown that gaming peripherals can deliver high quality instrumentation, non-expert programmers face considerable challenges in delivering robust and accurate instrumentation outside of the lab environment. Furthermore, when one considers how to fuse data across multiple peripherals, the heterogeneity of peripheral performance significantly complicates recording useful data. To that end, this paper seeks to describe our approach for delivering a robust, accurate, and scalable framework for motion-based gaming peripherals, specifically targeted at physical therapy in the clinical and research settings. We describe the principles of our framework and composition of data flow through a variety of illustrative examples. Finally, we conclude with several experimental setups designed to demonstrate the efficacy of the framework drawn directly from our experience in live clinical settings.

Internet Service Providers (ISPs) face increasing bandwidth pressure from rising access demand by users, especially P2P and VoD applications. Traditionally, P2P has been viewed as tremendously negative from the perspective of the ISP. In this paper, we question this assumption and study the impact of P2P applications on the effective ISP functionality. We perform an economic analysis to show that a higher P2P penetration rate does not necessarily lead to increased ISP bottleneck link bandwidth pressure. Our results show that the local serving rate is critical for the sustainability of the ISP business model as well as for the benefit of P2P users.

The incredible rise in popularity of mobile smart devices has placed tremendous pressure on wireless service providers. While much of the pressure arises from increasingly rich multimedia and social offerings, a sizable portion of the traffic originates when the user is not actively interacting with the device. The focus of this paper is to explore the prevalence and utility of smartphone background traffic through a pool of over one hundred campus smartphone users over a seven-week period from the Spring of 2013. Notably, our work shows that background traffic constitutes a non-trivial portion of wireless traffic ranging between one-third to two-fifths of traffic across the wireless interfaces. Our work breaks down background traffic with respect to diurnal behavior, wireless interface, mobile application, and latency until screen activation to further characterize the data.

The availability of “always-on” communications has tremendous implications for how people interact socially. In particular, sociologists are interested in the question if such pervasive access increases or decreases face-to-face interactions. Unlike triangulation which seeks to precisely define position, the question of face-to-face interaction reduces to one of proximity, i.e., are the individuals within a certain distance? Moreover, the problem of proximity estimation is complicated by the fact that the measurement must be quite precise (1-1.5 m) and can cover a wide variety of environments. Existing approaches such as GPS and Wi-Fi triangulation are insufficient to meet the requirements of accuracy and flexibility. In contrast, Bluetooth, which is commonly available on most smartphones, provides a compelling alternative for proximity estimation. In this paper, we demonstrate through experimental studies the efficacy of Bluetooth for this exact purpose. We propose a proximity estimation model to determine the distance based on the RSSI values of Bluetooth and light sensor data in different environments. We present several real world scenarios and explore Bluetooth proximity estimation on Android with respect to accuracy and power consumption.

Mobility models play an important role in wireless network simulation. While being widely used due to simplicity, synthetic models usually suffer from the inadequate semantics to characterize real-world movements. In contrast, traces are highly desirable for simulation since they are extracted from realistic movements. However, even releasing anonymized traces could potentially cause privacy exposure. To tackle this dilemma, our paper proposes a novel approach to produce mobility traces while still preserving location privacy. Our algorithm depends only on certain wireless relationships observed in a large-scale mobile dataset collected in campus, instead of using any of the actual location information for trace generation. We argue that wireless relationships rather than geo-locations are the critical aspects to preserve in mobility patterns. A set of metrics are applied to evaluate the performance of the proposed approach in terms of preserving the original wireless relationships in the output traces, demonstrating promising initial results.

The use of bandwidth intensive applications in the limited 802.11 wireless spectrum has lead to increased congestion and interference in the mobile space. Sending more data in a timely manner requires a fast transmission speed. The transmission speeds in turn are governed by rate adaptation algorithms that seek to optimize the delivery probability of packets. However, optimizing for an individual node also has a significant impact on other nodes in the wireless network. Despite this impact of rate selection on the larger wireless network, individual nodes tend to be non-altruistic resulting in multi-rate WLANs, thus incurring the "rate anomaly'' problem. In this paper, we posit that rate changes must be justified, i.e. goodput improvement must be commensurate with effective channel cost. We present the concept of rate zones to challenge when rate adaptation is justified with regards to the performance of the larger wireless network. Our experimental, simulation, and real-world results show that on average rate adaptation negatively impacts performance.

Due to fading and node mobility, packet loss in wireless networks is prevalent and unavoidable. Significant research has been conducted to improve the quality of wireless networks. Compared to other wireless networks, the backbone of a wireless mesh network (WMN) tends to be a relatively static topology and thus link error is likely to be transient rather than permanent. This paper proposes a technique, Save For Later (SFL), that leverages such transience to improve the performance of WMNs. SFL is a link-layer enhancement that allows an intermediate mesh router to temporarily save failed transmissions due to the high probability that a later successful transmission will convey that the failure is transient in nature. The paper develops a stochastic model to analyze the steady state throughput of a TCP bulk data transfer under SFL. Finally, the paper shows through simulation and analytical results that SFL elegantly improves the performance of WMNs in terms of throughput and flow fairness.

Wireless network providers are under tremendous pressure to deliver unprecedented amounts of data to a variety of mobile devices. A powerful concept that has only gained limited traction in practice has been the concept of opportunistic networks whereby nodes opportunistically communicate with each other when in range to augment or overcome existing wireless systems. One of the key impediments towards the adoption of opportunistic communications has been the inability to demonstrate viability at scale, namely showing that sufficient opportunities exist and more importantly exist when needed to offer significant network performance gains. We demonstrate through a large-scale, longitudinal study of smartphone users that significant opportunities are indeed prevalent, are indeed stable, and end up being reasonably reciprocal both on short and long-term timescales. In this paper, we propose a framework dubbed PSR (Prevalence, Stability, Reciprocity) to capture key aspects that characterize the net potential for opportunistic networks which we feel merit significantly increased attention.

With an increasing number of organizations allowing personal smart phones onto their networks, considerable security risk is introduced. The security risk is exacerbated by the tremendous heterogeneity of the personal mobile devices and their respective installed pool of applications. Furthermore, by virtue of the devices not being owned by the organization, the ability to authoritatively enforce organizational security polices is challenging. As a result, a critical part of organizational security is the ability to drive user security behavior through either on-device mechanisms or security awareness programs. In this paper, we establish a baseline for user security behavior from a population of over one hundred fifty smart phone users. We then systematically evaluate the ability to drive behavioral change via messaging centered on morality, deterrence, and incentives. Our findings suggest that appeals to morality are most effective over time, whereas deterrence produces the most immediate reaction. Additionally, our findings show that while a significant portion of users are securing their devices without prior intervention, it is difficult to influence change in those who do not.

Managing large-scale networks involving users and applications is challenging due to the complexity and dynamic nature of the heterogeneous graphs. How to quickly identify the meaningful changes and hidden anomalous activities in the spatiotemporally dynamic network graphs is essential in many aspects of network management, such as security, performance and troubleshooting. In this paper, we explore the viability and efficacy of a novel graph differential anomaly visualization (DAV) model in the area of network management. Our approach combines algorithmic graph analysis methods and visualization technologies by taking advantages from both computer and human intelligence. We focus on DAV at various levels, i.e., nodes, links and communities. Specifically, a novel community-based DAV scheme is proposed that can help understand the managed networks with a right balance of granularity and complexity. More importantly, the community-based DAV algorithm is less susceptible to network dynamics and high churn. The developed visual analytic tool can not only detect but more importantly find the root causes of anomalies in a time efficient manner.

Abstract Firewalls are among the most important components in network security. Traditionally, the rules of the firewall are kept private under the assumption that privacy of the ruleset makes attacks on the network more difficult. We posit that this assumption is no longer valid in the Internet of today due to two factors: the emergence of botnets reducing probing difficulty and second, the emergence of distributed applications where private rules increase the difficulty of troubleshooting. We argue that the enforcement of the policy is the key, not the secrecy of the policy itself. In this paper, we demonstrate through the application of game theory thatpublicfirewall rules when coupled with false information (lying) are actually better than keeping firewall rules private, especially when taken in the larger group context of the Internet. Interesting scenarios arise when honest, public firewalls are socially insured by other lying firewalls and networks adopting public firewalls become mutually beneficial to each other. The equilibrium under multiple‐network game is socially optimal because the percentage of required lying firewalls in social optimum is much smaller than the percentage in single‐network equilibrium and the chance of attacking through firewalls is further reduced to zero. Copyright © 2011 John Wiley & Sons, Ltd.

The availability of "always-on" communications has tremendous implications for how people interact socially. In particular, sociologists are interested in the question if such pervasive access increases or decreases face-to-face interactions. Unlike triangulation which seeks to define precise position, the question of face-to-face interactions reduces to one of proximity, i.e. are the individuals within a certain distance? Moreover, the problem of proximity estimation is complicated by the fact that the measurement must be quite precise (1-1.5m) and can cover a wide variety of environments. Existing approaches such as GPS and WiFi triangulation are insufficient due to those constraints. In contrast, Bluetooth, which is commonly available on most smartphones, provides a compelling alternative for proximity estimation. In this paper, we demonstrate through experimental studies the efficacy of Bluetooth for this exact purpose. We present several real world scenarios and explore Bluetooth proximity estimation on Android with respect to accuracy and power consumption.

Diagnosing a large-scale sensor network is a crucial but challenging task due to the spatiotemporally dynamic network behaviors of sensor nodes. In this demo, we present Sensor Anomaly Visualization Engine (SAVE), an integrated system that tackles the sensor network diagnosis problem using both visualization and anomaly detection analytics to guide the user quickly and accurately diagnose sensor network failures. Temporal expansion model, correlation graphs and dynamic projection views are proposed to effectively interpret the topological, correlational and dimensional sensor data dynamics and their anomalies. Through a real-world large-scale wireless sensor network deployment (GreenOrbs), we demonstrate that SAVE is able to help better locate the problem and further identify the root cause of major sensor network failures.

Abstract Botnets have become an increasing security concern in today's Internet. Since current technological defenses against botnets have failed to produce results, it has become necessary to think about different strategies. Given that money is perhaps the single determining force driving the growth in botnet attacks, we propose an interesting economic approach to take away the root cause of botnet, i.e., the financial incentives. In this paper, we model botnet‐related cyber crimes as a result of profit‐maximizing decision‐making optimization problem from the perspective of botmasters. By introducing theuncertaintylevel created by thevirtual bots, we make determining the optimal botnet size infeasible for the botnet operators, and consequently the botnet profitability can fall dramatically. The theoretical model presented here has a large potential to fight off botnet‐related attacks of varying revenue patterns. Copyright © 2010 John Wiley & Sons, Ltd.

Managing complex enterprise networks requires an understanding at a finer granularity than traditional network monitoring. The ability to correlate and visualize the dynamics and inter-relationships among various network components such as hosts, users, and applications is non-trivial. In this paper, we propose a visualization approach based on the hierarchical structure of similarity/difference visualization in the context of heterogeneous graphs. The concept of hierarchical visualization starts with the evolution of inter-graph states, adapts to the visualization of intra-graph clustering, and concludes with the visualization of similarity between individual nodes. Our visualization tool, ENAVis (Enterprise Network Activities Visualization), quantifies and presents these important changes and dynamics essential to network operators through a visually appealing and highly interactive manner. Through novel graph construction and transformation, such as network connectivity graphs, MDS graphs, bipartite graphs, and similarity graphs, we demonstrate how similarity/dynamics can be effectively visualized to provide insight with regards to network understanding.

Over the last decade, numerous admission control schemes have been studied to allocate network resources. Although per-flow control schemes can provide guaranteed QoS, such schemes face scalability issues in large networks due to the tremendous number of flows present. While aggregation-based approaches such as Differentiated Services relieve the storage of state in the core, admission control of flows, especially short-lived flows, is still a serious bottleneck. To that end, we propose an admission control scheme, Fast Admission for Short Flows (FASF), that enables accelerated admission control at the edge rather than via centralized or in-path mechanisms. FASF not only reduces the burden on admission control by largely distributing the dominant resource requests (i.e. short-lived flows), but also improves flow completion time and hence network goodput.

DRM, Digital Rights Management, has become prolific. It is used on both physical mediums (including CDs, DVDs, Blu-Ray discs) and digitally distributed content. DRM controls how, when, where and by whom content gets used. However, perfect DRM remains elusive with each variation often being cracked or circumvented by various hacking groups shortly after (or before) its release. The use of DRM grew out of the need to protect the distribution of content with file-sharing networks, which grew in size due to the proliferation of broadband internet services. However, for file-sharing to be effective, users need to have sufficient upload bandwidth relative to the memory size of the content being shared. With compressed audio and video content, file-sharing has been relatively successful, but for content that requires a memory footprint a magnitude larger than typically shared before, distribution becomes significantly harder. With the proliferation of high-definition content we argue that DRM in the traditional sense no longer appears necessary. Specifically, we posit that using the natural file size of true high- definition content essentially acts as its own form of DRM due to the extreme asymmetry of broadband speeds and vested economic incentives of ISPs with regards to enforcement.

Despite the significant body of research that has been conducted on quality of service (QoS), the notion of a dominant approach to end-to-end (E2E) QoS remains elusive. Beyond the ever present issue of deployment, the complexity and limited speed of resource negotiation arising from per-hop or per-domain interactions limit the general utility of the existing approaches. In this paper, we propose a novel approach to make significant strides in practical end-to-end QoS by leveraging the over-provisioned nature of the core and cooperative interactions amongst end autonomous systems. Our approach, E3AC (End-wise delegation for End-to-End Admission Control), provides a framework for catalyzing QoS amongst end ISPs while dramatically reducing the setup latency for QoS negotiations

Abstract Developing a modular system that properly supports a range of security models is challenging. The work presented here details our experiences with the modularLinuxsecurity framework called Linux Security Modules, or LSMs. Throughout our experiences we discovered that the developers of the LSM framework made certain tradeoffs for speed and simplicity during implementation, and consequently leaving the framework incomplete. Our experiences show at which points the theory of the LSM differs from reality, and details how these differences play out when developing and using a custom LSM. Copyright © 2009 John Wiley & Sons, Ltd.

Real-time media servers are becoming increasingly important as the Internet supports more and more multimedia applications. In order to meet these ever increasing demands, real-time media servers will be responsible for supporting a large number of clients with a wide range of QoS requirements. While techniques to aggregate state information for scalability have been proposed in the literature such as with Differentiated Services; the per-stream effects of such aggregation are poorly understood. Based on the (m,k)-firm model to schedule loss-tolerant streams, we explore the effects of aggregated state information in this paper and describe our scheme, called granularity aware (m,k) queue management (GAQM). GAQM improves control over the tradeoff between scalability and per-stream QoS performance. Specifically, we identify the necessity of balancing aggregation groups according to characteristics such as relative deadlines. Another key finding of this work is that with proper biasing, the inaccuracy of aggregate state lends itself to burst scheduling rather than simply extending traditional scheduling mechanisms. This finding is profound in that the result is counterintuitive: less frequent scheduling leads to increased per-stream performance. We present detailed examples of GAQM and evaluate our work through simulation studies and Markov chain analysis.

Maintaining synchronization of clocks between wireless systems is a well known problem of which significant research has been performed. This has lead to a variety of methods introduced to maintain clock synchronization. Typically, works are heavy weight in that they require constant communication between systems in order to maintain clock synchronization on the order of microseconds. Unfortunately, for many applications the cost of constant communication to ensure clock synchronization is neither desirable nor acceptable. Additionally, for applications such as link state routing, delay measurements and quality of service measurements, clock synchronization on the order of microseconds is not necessary, and synchronization on the order of milliseconds is sufficient. Thus, in this work we present a light weight technique for correcting for clock drift between systems that will allow for millisecond accuracy during long periods of time while requiring no special hardware nor constant communication between systems. Experimental studies of the measured delay between two systems are performed, showing that with a training period, clocks can remained synchronized within a few milliseconds over long periods of time.

The demand for rich multimedia content is continuously increasing as exemplified by the success of sites such as YouTube, Google Video, and others. Critically, the richness of multimedia content places significant demands on the limited bandwidth available in wireless networks. To that end, this paper proposes a novel mechanism, opportunistic wireless broadcast (OWB), that opportunistically aggregates redundant content over short timescales into unified broadcasts in order to dramatically improve the efficiency of streaming media. Unlike end-to-end techniques such as application-layer multicast (ALM) or native IP multicast, OWB does not require modifications to the server or client applications, thus offering a practical transition for deployment. Experimental studies are presented showing that even with a minimal amount of redundancy, OWB can significantly improve network throughput and quality of service in terms of end-to-end delay.

Even though their capacities are still orders of magnitude lower than those of hard disks, flash storage systems are rapidly gaining importance in energy-constrained systems. This paper focuses on USB flash drives, which can provide portable storage to mobile systems or storage to systems that otherwise do not have persistent storage opportunities (e.g., low-power sensor devices). The paper presents studies relating to power consumption, energy overheads and benefits, and performance impacts of USB flash drives. The key insights obtained from these experiments are that (i) read/write costs are not significantly greater than idle costs and (ii) the size of the flash itself has only limited bearing on energy consumption.

Wireless enabled devices are ubiquitous in todaypsilas computing environment. Businesses, universities, and home users alike are taking advantage of the easy deployment of wireless devices to provide network connectivity without the expense associated with wired connections. Unfortunately, the wireless medium is inherently unreliable resulting in significant work having been performed to better understand the characteristics of the wireless environment. Notably, many works attribute the primary source of wireless losses to errors in the physical medium. In contrast, our work shows that the wireless device itself plays a significant role in 802.11 packet loss. In our experiments, we found that the correlation of loss between multiple closely located (within one lambda) receivers is low with the majority of loss instances only occurring at one of the receivers. We conducted extensive experiments on the individual loss characteristics of five common wireless cards, showing that while the cards behave similarly on the macro-level (e.g. similar overall loss rates), the cards perform quite differently on the micro-level (e.g. burstiness, correlation, and consistency).

Abstract Distributed denial of service (DDoS) attacks originating from botnets can quickly bring normally effective web services to a screeching halt. This paper presents SESRAA (selective short‐term randomized acceptance algorithms), an adaptive scheme for maintaining web service despite the presence of multifaceted attacks in a noisy environment. In contrast to existing solutions that rely upon ‘clean’ training data, we presume that a live web service environment makes finding such training data difficult if not impossible. SESRAA functions much like a battlefield surgeon's triage: focusing on quickly and efficiently salvaging good connections with the realization that the chaotic nature of the live environment implicitly limits the accuracy of such detections. SESRAA employs an adaptivek‐means clustering approach using short‐term extraction and limited centroid evolution to defend the legitimate connections in a mixed attack environment. We present the SESRAA approach and evaluate its performance through experimental studies in a diverse attack environment. The results show significant improvements against a wide variety of DDoS configurations and input traffic patterns. Copyright © 2008 John Wiley & Sons, Ltd.

Wireless network access has become an integral part of computing both at home and at the workplace. The convenience of wireless network access at work may be extremely beneficial to employees, but can be a burden to network security personnel. This burden is magnified by the threat of inexpensive wireless access points being installed in a network without the knowledge of network administrators. These devices, termed Rogue Wireless Access Points , may allow a malicious outsider to access valuable network resources, including confidential communication and other stored data. For this reason, wireless connectivity detection is an essential capability, but remains a difficult problem. We present a method of detecting wireless hosts using a local RTT metric and a novel packet payload slicing technique. The local RTT metric provides the means to identify physical transmission media while packet payload slicing conditions network traffic to enhance the accuracy of the detections. Most importantly, the packet payload slicing method is transparent to both clients and servers and does not require direct communication between the monitoring system and monitored hosts.

High speed modern networks are tasked with moving large amounts of data to diverse groups of interested parties. Often under heavy loads, a significant portion of the data exhibits large amounts of redundancy on short and/or long-term time scales. As a result, a large body of work has emerged offering bandwidth conservation exemplified by the work in caching and multicast. The majority of the techniques that have experienced widespread adoption rely on parsing / reacting to application-specific data. With the advent of simplified end-to-end security, as introduced by IPv6, these techniques will no longer have access to the plaintext data. We present a novel technique for preserving security while allowing in-network devices to identify redundant data flows in order to apply bandwidth conservation techniques. Our communication protocol does not require modifications to existing applications nor does it inflict a significant amount of overhead to the existing network infrastructure.

The emergence of popular video sharing sites such as YouTube has created a tremendous content shift towards timely, medium-sized media together with placing significant demands on the network. While techniques such as Application Layer Multicast and P2P streaming offer the potential to reduce the impact of general streaming content, the difficulty in imposing synchronization and the heavy asymmetry of clients nullify the majority of the respective benefits of the techniques. In this paper, we propose a method, transparent tail synchronization, that discovers latent opportunities for synchronization from the tail of the content to take advantage of efficient distribution techniques. Our approach maximizes savings at the content provider while operating in a straightforward and easily deployable manner. We describe how tail synchronized media distribution can offer savings across a wide range of asymmetry at the end clients.

Computationally complex and data intensive atomic scale biomolecular simulation is enabled via processing in network storage (PINS): a novel distributed system framework to overcome bandwidth, compute, storage, and security challenges inherent to the wide area computation and storage grid. High throughput data generation requirements for our scientific target are overcome through novel aggregate bandwidth capabilities. Biomolecular simulation methods are correlated with the client tools, hybrid database/file server (GEMS), computation engine (Condor), virtual file system adapter (Parrot), and local file servers (Chirp). PINS performance is reported for the path sampling of a solvated protein domain requiring over 1000 simulations with total output data generation on the order of 1TB.

Streaming real-time applications require the timely distribution of information in mobile ad-hoc and sensor networks. At the same time, such networks must operate energy-efficiently to maximize the lifetime of mobile devices and applications. In multi-hop networks, multiple communication paths between a single sender and receiver can be established, with varying real-time and energy characteristics of each path. This paper introduces the DETOUR (Delay- and Energy- aware mulTi- cOUrse Routing) protocol that applies feedback-driven path diversification, where traffic load is balanced across two or more paths to ensure both timeliness and energy-efficiency. We apply the (m,k) model for firm real-time communication to wireless networks, i.e., the protocol aims to meet at least m end-to- end deadlines out of k packet transmissions, thereby sacrificing additional improvement in latency in order to maximize the lifetime of the network by minimizing energy consumption. The experimental results of this paper show the protocols ability to reduce energy consumptions (up to 35%) while meeting the data streams firm real-time constraints.

Wi-Fi protected access (WPA) is currently the most commonly used mechanism for protecting users of wireless networks. Protection is afforded by authenticating users of the network and encrypting communication which travels through the wireless medium. However, WPA is limited in the amount protection offered in networks which use a pre-shared key (WPA-PSK) for authentication, as anyone holding the PSK may eavesdrop on other authorized users. We present a lightweight enhancement to the WPA four-way handshake which removes this limitation, providing confidentiality even in a shared-key environment. In addition, we apply the enhancement to a non-authenticated open public WLAN environment thereby providing protection from sniffing attacks without requiring additional configuration or setup modifications to be made by the user

Biomolecular simulations produce more output data than can be managed effectively by traditional computing systems. Researchers need distributed systems that allow the pooling of resources, the sharing of simulation data, and the reliable publication of both tentative and final results. To address this need, we have designed GEMS, a system that enables biomolecular researchers to store, search, and share large scale simulation data. The primary design problem is striking a balance between generosity and gluttony. On one hand, storage providers wish to be generous and share resources with their collaborators. On the other hand, an unchecked data producer can be gluttonous and easily replicate data unnecessarily until it fills all available space. To balance generosity and gluttony, GEMS allows both storage providers and data producers to state and enforce policies on the consumption of storage and the replication of data. By taking advantage of known properties of simulation data, the system is able to distinguish between high value final results that must be preserved and low value intermediate results that can be deleted and regenerated if necessary. We have built a prototype of GEMS on a cluster of workstations and demonstrate its ability to store new data, to replicate within policy limits, and to recover from failures.

Sharing data and storage space in a distributed system remains a difficult task for ordinary users, who are constrained to the fixed abstractions and resources provided by administrators. To remedy this situation, we introduce the concept of a tactical storage system (TSS) that separates storage abstractions from storage resources, leaving users free to create, reconfigure, and destroy abstractions as their needs change. In this paper, we describe how a TSS can provide a variety of filesystem and database abstractions for unmodified applications without requiring special privileges or kernel changes. A TSS provides performance competitive with NFS for single clients and also scales well for multiple servers and multiple clients. A prototype TSS of 120 disks and 6 TB of storage has been deployed at the University of Notre Dame and used for applications in high energy physics and bioinformatics.

The phenomenal growth of QoS-aware applications over the Internet has accelerated the development of key technologies such as differentiated services (DiffServ). Although QoS is provided through class-based service differentiation, the aspect of fault tolerance is not addressed in the DiffServ architecture. For traditional IP networks, the underlying link state protocol provides fault detection and recovery. However for QoS sensitive flows, the recovery times of such protocols may not be adequate. Although such a problem may be solved through fine grain HELLO timers, the underlying core routers may not be able to tolerate the additional CPU and bandwidth burden. The edge-based intelligence of the DiffServ domain represents a unique opportunity to improve the fault detection capability of the link state protocol. We propose a hybrid scheme whereby heartbeat packets are used to detect possible faults coupled with a temporary fine grain HELLO interval for fault location and possible recovery. We analyze our scheme through extensive simulation studies and we examine the tradeoffs and benefits of our scheme.

The significant growths of group communications and QoS-aware applications over the Internet have accelerated the development of two key technologies, namely, multicasting and Differentiated Services (DiffServ). Although both are complementary technologies, their integration is a non-trivial task due to several architectural conflicts between them. The inherent heterogeneous nature of QoS multicasting further complicates this problem with the sender-driven nature of DiffServ. Thus, in this paper, we propose a method for providing heterogeneous QoS to multicast groups via dynamic DSCPs without per-group state information in the DiffServ core. We detail our approach as well as examine implications for an adaptive method for both multicast and unicast connections. Finally, we present simulation studies regarding the performance benefits of dynamic DSCPs in multicasting.

The phenomenal growths of group communications and QoS-aware applications over the Internet have respectively accelerated the development of two key technologies, namely, multicasting and differentiated services (DiffServ). Although both are complementary technologies, the integration of the two technologies is a non-trivial task due to architectural conflicts between multicasting and DiffServ. We propose an approach for providing multicast support across a DiffServ domain that is scalable in terms of group size, network size, and number of groups. We analyze our approach in a detailed manner for feasibility, adaptiveness, and deployment considerations.

The issue of providing efficient media retrieval services has been an important problem in recent years. Among them, video-on-demand (VoD) is one of the most popular and challenging ones. An efficient solution to the VoD service problem should address the twin issues of scalability and client heterogeneity. In this paper, we propose a VoD server model, that is based on the parallel video server architecture and layered coding, to satisfy these twin issues. The proposed model dynamically adapts to the server load in order to maximize the number of clients (connections) admitted while maintaining a high quality for the already admitted connections. We study the adaptiveness our model by proposing promotion/demotion policies for quality adaptation. We also propose a performance metric, called marginal quality, that normalizes the quality (received) across heterogeneous connections. Finally, we evaluate the effectiveness of our model, in terms of connection acceptance rate, connection quality, and fairness in connection quality, through extensive simulation studies.

The phenomenal growths of group communications and QoS-aware applications over the Internet have accelerated the development of two key technologies, namely, multicasting and Differentiated Services (DiffServ). Although both are complementary technologies, the integration of the two technologies is a non-trivial task due to architectural conflicts between multicasting and DiffServ. We propose a protocol for member join/leave in a DiffServ network that is scalable in terms of group size, network size, and number of groups. We detail our join/leave protocol for both intra-domain and inter-domain routing as well as the various different types of multicast trees (single source tree, shortest path tree, shared tree, many-to-many tree). Finally, we present simulation studies regarding the performance of our join/leave protocol.

An autonomous wireless local area network (AWLAN) is a collection of wireless computers that can be rapidly deployed as an ad hoc network without the aid of any established infrastructure or centralized administration. However, existing routing protocols for such networks neither scale nor function on the Internet. Therefore, a solution is required that provides a gateway between the ad hoc network and the Internet. In this paper, we propose a protocol-independent Internet gateway for ad hoc networks, the Cluster Gateway (CG). The proposed Cluster Gateway (CG) provides Internet access by acting as both a service access point and a Mobile IP foreign agent for ad hoc networks. In this paper, we describe the requirements for supporting the CG in any ad hoc routing protocol, the messages sent in order to provide CG support, and several optional enhancements for the CG. Finally, we briefly describe an implementation of the CG over an existing ad hoc routing protocol.