Recent advances in dance generation have enabled the automatic synthesis of 3D dance motions. However, existing methods still face significant challenges in simultaneously achieving high realism, precise dance-music synchronization, diverse motion expression, and physical plausibility. To address these limitations, we propose a novel approach that leverages a generative masked text-to-motion model as a distribution prior to learn a probabilistic mapping from diverse guidance signals, including music, genre, and pose, into high-quality dance motion sequences. Our framework also supports semantic motion editing, such as motion inpainting and body part modification. Specifically, we introduce a multi-tower masked motion model that integrates a text-conditioned masked motion backbone with two parallel, modality-specific branches: a music-guidance tower and a pose-guidance tower. The model is trained using synchronized and progressive masked training, which allows effective infusion of the pretrained text-to-motion prior into the dance synthesis process while enabling each guidance branch to optimize independently through its own loss function, mitigating gradient interference. During inference, we introduce classifier-free logits guidance and pose-guided token optimization to strengthen the influence of music, genre, and pose signals. Extensive experiments demonstrate that our method sets a new state of the art in dance generation, significantly advancing the quality and editability over existing approaches.

Recent advancements in 3D human pose estimation from single-camera images and videos have relied on parametric models, like SMPL. However, these models over-simplify anatomical structures, limiting their accuracy in capturing true joint locations and movements, which reduces their applicability in biomechanics, healthcare, and robotics. Biomechanically accurate pose estimation, on the other hand, typically requires costly marker-based motion capture systems and optimization techniques in specialized labs. To bridge this gap, we propose BioPose, a novel learning-based framework for predicting biomechanically accurate 3D human pose directly from monocular videos. BioPose includes three key components: a Multi-Query Human Mesh Recovery model (MQ-HMR), a Neural Inverse Kinematics (NeurIK) model, and a 2D-informed pose refinement technique. MQ-HMR leverages a multi-query deformable transformer to extract multi-scale fine-grained image features, enabling precise human mesh recovery. NeurIK treats the mesh vertices as virtual markers, applying a spatial-temporal network to regress biomechanically accurate 3D poses under anatomical constraints. To further improve 3D pose estimations, a 2D-informed refinement step optimizes the query tokens during inference by aligning the 3D structure with 2D pose observations. Experiments on benchmark datasets demonstrate that BioPose significantly outperforms state-of-the-art methods.

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.

Understanding city-scale vehicular mobility and trip patterns is essential to addressing many problems, from transportation and pollution to public safety. Using spatio-temporal analysis of vehicular mobility, promising solutions can be proposed to alleviate these major challenges, utilizing shared mobility. The rise of transportation networks (e.g., Uber, Lyft), is a mere beginning to shared mobility. In this paper, we address problems of trip representation and matching. Particularly, we study a real-world dataset of trips (from Cologne, Germany), from spatial and temporal perspectives. Comparison of trajectories is desired for applications relying on spatio-temporal phenomena. For that purpose, we present a novel combined spatio-temporal similarity score, based on the weighted geometric mean (WGM) and systematically evaluate its applicability and strengths. First, we use the score to find clusters of trips that were spatially and/or temporally separable using spectral clustering. The score is then used in a real-time matching of trips for Catch-a-Ride (CaR) and CarPooling (CP) scenarios. CaR and CP achieve ≈ 40% and ≈ 25% decrease in traveled distances respectively, at the cost of moving to pick-up and from drop-off locations (i.e., drivers going on average < 700m out of their way on pick-up and drop-of for CP). We find that main advantages of WGM include the flexibility to favor time or space components, and linearity of runtime complexity. Finally, we formulate an optimal free float Car-Sharing scenario (e.g., scheduling a system of automated vehicles or taxis) resulting in an average of ≈ 3.88 trips serviced by a car in one hour.

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.

Mobility and network traffic have been traditionally studied separately. Their interaction is vital for generations of future mobile services and effective caching, but has not been studied in depth with real-world big data. In this paper, we characterize mobility encounters and study the correlation between encounters and web traffic profiles using large-scale datasets (30TB in size) of WiFi and NetFlow traces. The analysis quantifies these correlations for the first time, across spatio-temporal dimensions, for device types grouped into on-the-go Flutes and sit-to-use Cellos. The results consistently show a clear relation between mobility encounters and traffic across different buildings over multiple days, with encountered pairs showing higher traffic similarity than non-encountered pairs, and long encounters being associated with the highest similarity. We also investigate the feasibility of learning encounters through web traffic profiles, with implications for dissemination protocols, and contact tracing. This provides a compelling case to integrate both mobility and web traffic dimensions in future models, not only at an individual level, but also at pairwise and collective levels.

Building's density, as its number of nodes at a specific period, is a significant parameter that affects mobile and smart applications performances and evaluations. Consequently, the buildings' temporal density predictions and their nodes spatial distribution modeling have to follow real-world scenarios to provide a realistic evaluation. However, there is lack of real-world building-level density studies that examine these aspects thoroughly. As a result, this work is a data-driven study that investigates the temporal density predictability and spatial density distributions of more than 100 real buildings with ten different categories, over 150 days across three semesters. The study covers the buildings nodes' temporal modeling and predictions, and their spatial distributions in the building. Seasonal predictive models are utilized to predict hour-by-hour density for a variable length of consequent periods using training data with different lengths. The models include Seasonal Naive, Holt-Winters' seasonal additive, TBATS, and ARIMA-seasonal. The results show that the Seasonal Naive model is often selected as the best predictive model when training phase covers a shorter period. For example, Seasonal Naive predicted with the least error in 73%, 63% and 57% of cases in summer, spring, and fall respectively when using only one week to predict its consecutive five weeks with mean normalized error 25% on average. However, when using five weeks of data to predict the sixth week, the TBATS model predicted with the least error in 60%, 54% and 43% of cases in fall, spring and summer respectively with mean absolute error 19% on average. When investigating the spatial density distributions, power law, log-logistic and lognormal distributions are usually selected as the first best-fit distributions for 82%, 65%, 62% of buildings in the summer, spring and fall respectively.

Distance estimation and proximity classification techniques are essential for numerous IoT applications and in providing efficient services in smart cities. Bluetooth Low Energy (BLE) is designed for IoT devices, and its received signal strength indicator (RSSI) has been used in distance and proximity estimation, though they are noisy and unreliable. In this study, we leverage the BLE TX power level in BLE models.We adopt a comparative analysis framework that utilizes our extensive data library of measurements. It considers commonly used state-of-the-art model, in addition to our data-driven proposed approach. The RSSI and TX power are integrated into several parametric models such as log shadowing and Android Beacon library models, and machine learning models such as linear regression, decision trees, random forests and neural networks. Specific mobile apps are developed for the study experiment. We have collected more than 1.8 millions of BLE records between encounters with various distances that range from 0.5 to 22 meters in an indoor environment. Interestingly, considering TX power when estimating the distance reduced the mean errors by up to 46% in parametric models and by up to 35% in machine learning models. Also, the proximity classification accuracy increased by up to 103% and 70% in parametric and machine learning models, respectively. This work is one of the first studies (if not the first) that analyze in depth the TX power variations in improving the distance estimation and classification.

In this paper we study social networks as an enabling technology for new applications and services leveraging, largely unutilized, opportunistic mobile encounters. More specifically, we quantify mobile user similarity and introduce a novel mathematical framework, grounded in information theory, to characterize fundamental limits and quantify the performance of sample knowledge sharing strategies. First, we introduce generalized, non-temporal and temporal profile structures, beyond geographic location, as a probability mass function. Second, we examine classic and information-theoretic similarity metrics using data in the public domain. A noticeable finding is that temporal metrics give lower similarity indices on the average (i.e., conservative) compared to non-temporal metrics, due to leveraging the wealth of information in the temporal dimension. Third, we introduce a novel mathematical framework that establishes fundamental limits for knowledge sharing among similar opportunistic users. Finally, we show numerical results quantifying the cumulative knowledge gain over time and its upper bound, the knowledge gain limit, using public smartphone data for the user behavior and mobility traces, in the case of fixed as well as mobile scenarios. The presented results provide valuable insights highlighting the key role of the introduced information-theoretic framework in motivating future research along this ripe research direction, studying diverse scenarios as well as novel knowledge sharing strategies.

Dementia, Autism, and Alzheimer's disorders affect millions of people worldwide. Suffering from forgetfulness, affected patients tend to wander off and potentially get into dangerous situations. This work introduces a mobile app, Alzimio, to provide two main alarm functions to these patients; safe-zone geofencing and activity-recognition. Caregivers designate certain activities (or zones) as dangerous, triggering alert message (with activity information) when detected. Several challenges must be overcome to achieve our goals. First, the activities and zones must be detected accurately in a timely fashion, and with high confidence. Second, the algorithms used should operate effectively on regular smartphones without special hardware. Such challenges are not unique to Alzimio, but are general to most Internet of Things (IoT) healthcare apps. In this study, we devise several novel activity-recognition algorithms to meet our goals of accuracy and efficiency. Our threshold-based algorithms intelligently filter and process the output of Android APIs for activity recognition and geofencing, at different time scales. The app was evaluated using extensive scenarios of usage for several months. We find that our max-in- window algorithm is able to achieve over 95% accuracy in less than 30 sec in most scenarios. The optimal threshold was found to be 65% confidence, to achieve best accuracy and delay. The Alzimio app runs efficiently on budget (low-end and medium) Android phones without noticeably affecting power consumption.

Many disease infection cases are acquired and spread in hospital settings. Early detection of such infection cases is necessary to identify population at risk accurately and effectively. This paper introduces a systematic infection detection framework utilizing mobile communication technologies including mobile networking and encounter statistics during infection breakouts. Our target environment is smart connected hospitals using Internet of things. First, the disease infection trace-back problem is defined. Then a detailed practical framework is devised using wireless encounters to facilitate the trace-back to the infection source and identify the population at risk; the nodes that are likely infected. Trace back and filtering methods are then proposed using probabilistic forward and backward search techniques. We use extensive WLAN campus traces of six buildings with different mobility characteristics and over 34K users to drive our simulations. Metrics of true positive, true negative, accuracy and coverage are measured and presented. Our results thus far show promise, where in most cases the accuracy of correctly selecting the infected nodes that have been in the area during the epidemic period were found to reach 70-80% with population selection coverage close to the infection percentage of nodes.

Transportation simulation technologies should accurately model traffic demand, distribution, and assignment parameters for urban environment simulation. These three parameters significantly impact transportation engineering benchmark process, are also critical in realizing realistic traffic modeling situations. In this paper, we model and characterize traffic density distribution of thousands of locations, intersection, and roadways around the world. The traffic densities are generated from millions of images collected over several years and processed using computer vision techniques. The resulting traffic density distribution time series are then analyzed. It is found using the goodness-of-fit test that the traffic density distributions follow heavy-tail models such as Weibull in over 90% of analyzed locations. Moreover, a heavy-tail gives rise to long-range dependence and self-similarity, which we studied by estimating the Hurst exponent (H). Our analysis based on seven different Hurst estimators strongly indicates that the traffic distribution patterns are stochastically self-similar (0.5 ≤ H ≤ 1.0). We believe this is an important finding that will influence the design and development of the next generation traffic simulation techniques and also aid in accurately modeling traffic engineering of urban systems. In addition, it shall provide a much-needed input for the development of smart cities.

A new generation of social discovery and crowd-sourced location-based services promise to integrate mobile computing into our lives more than ever. An essential component in these services is the neighbor or peer selection, that is also needed for peer-to-peer (P2P) mobile applications and multiplayer mobile gaming. One major challenge is posed by the interpretation of the information collected by the smartphones (about nearby devices, locations) to bring the user closer to context-awareness and informed peer selection. We introduce a framework and a corresponding application (ConnectEnc) for mobile peer rating using a multi-dimensional metric scheme based on encounter and location sensing. ConnectEnc maintains a history of discovered nearby devices and locations, and rates them using an array of metrics ranging in complexity from simple (encounter frequency) to complex and novel (location vector and matrix similarity). We have developed and deployed ConnectEnc application on Android and Nokia N810 platform to measure the link between the scores of proposed filters and device selection by the user. Results from the deployment shows that statistically high correlation exists between ConnectEnc recommendations and user selections. Our framework is distributed, modular and can serve as a peer selection platform for other mobile protocols based on specific requirements. It runs on individual devices and does not require data sharing or interaction; and hence can bootstrap other recommendation, cooperation or reputation systems.

The future global Internet is going to have to cater to users that will be largely mobile. Mobility is one of the main factors affecting the design and performance of wireless networks. Mobility modeling has been an active field for the past decade, mostly focusing on matching a specific mobility or encounter metric with little focus on matching protocol performance. This study investigates the adequacy of existing mobility models in capturing various aspects of human mobility behavior (including communal behavior), as well as network protocol performance. This is achieved systematically through the introduction of a framework that includes a multi-dimensional mobility metric space. We then introduce COBRA, a new mobility model capable of spanning the mobility metric space to match realistic traces. A methodical analysis using a range of protocol (epidemic, spraywait, Prophet, and Bubble Rap) dependent and independent metrics (modularity) of various mobility models (SMOOTH and TVC) and traces (university campuses, and theme parks) is done. Our results indicate significant gaps in several metric dimensions between real traces and existing mobility models. Our findings show that COBRA matches communal aspect and realistic protocol performance, reducing the overhead gap (w.r.t existing models) from 80% to less than 12%, showing the efficacy of our framework.

Future vehicular networks shall enable new classes of services and applications for car-to-car and car-to-roadside communication. The underlying vehicular mobility patterns significantly impact the operation and effectiveness of these services, and hence it is essential to model and characterize such patterns. In this paper, we examine the mobility of vehicles as a function of traffic density of more than 800 locations from six major metropolitan regions around the world. The traffic densities are generated from more than 25 million images and processed using background subtraction algorithm. The resulting vehicular density time series and distributions are then analyzed. It is found using the goodness-of-fit test that the vehicular density distribution follows heavy-tail distributions such as Log-gamma, Log-logistic, and Weibull in over 90% of these locations. Moreover, a heavy-tail gives rise to long-range dependence and self-similarity, which we studied by estimating the Hurst exponent (H). Our analysis based on seven different Hurst estimators signifies that the traffic patterns are stochastically self-similar (0.5 ≤ H ≤ 1.0). We believe this is an important finding, which will influence the design and deployment of the next generation vehicular network and also aid in the development of opportunistic communication services and applications for the vehicles. In addition, it shall provide a much needed input for the development of smart cities.

The proliferation of smart-phones has been simply unprecedented. Unlike laptops, smart-phones have a much smaller form factor, are ultra mobile, include a rich set of sensors (e.g., location, motion), and are tightly coupled with the users. These factors pose a fundamental question regarding the characteristics of mobile users' on-line behavior for these devices, and how their differences (vis-a-vis laptops) impact networking protocols and services. Over the past decade the networking community has used extensive data sets, mainly capturing usage of laptops in WLANs, to develop and validate mobility (and usage) models, used in turn to analyze, simulate and design network services and protocols. In this study, using recent WLAN traces, we aim to compare and contrast the spatio-temporal on-line behavioral patterns of mobile users when using smart-phones vs. laptops. The main challenges in our study include the collection of extensive recent traces, the classification of mobile devices, and the analysis of various behavioral metrics on a large-scale. We collect over 46M records of 120K devices during 4 months in 2011. We introduce a classification of smart-phones based on MAC address filters, then use several spatio-temporal and predictability metrics to compare the traces. The result of our analysis shows significant differences between usage of smart-phones and laptops. The differences include shorter session durations for smart-phones (by 67% as compared to laptops), higher number of sessions per day (2 times that of laptops) and visiting new location (3 times that of laptops). We find that these differences are significant and will allow researchers to create better models and future services.

User interests and behavior will play a key role in the design of future mobile networks. In this paper, we introduce a novel technique for the modeling of mobile users interests based on their online activity and mobility. In this study, we conduct domain-specific and location-based analysis and modeling of mobile user interests based on the amount of their online time. Using KS test, we show that domain-specific and location-based models provide more accuracy than a generic model for mobile users interests. The provided models can be applied for design and evaluation of a myriad of interest-based applications and services for future mobile global Internet.

Future vehicular networks shall enable new classes of services and applications for car-to-car and car-to-roadside communication. The underlying vehicular mobility patterns significantly impact the operation and effectiveness of these services, and hence it is essential to model and characterize such patterns. In this paper, we examine the mobility of vehicles as a function of traffic density of more than 800 locations from six major metropolitan regions around the world. The traffic densities are generated from more than 25 million images and processed using background subtraction algorithm. The resulting vehicular density time series and distributions are then analyzed. It is found using the goodness-of-fit test that the vehicular density distribution follows heavy-tail distributions such as Log-gamma, Log-logistic, and Weibull in over 90% of these locations. Moreover, a heavy-tail gives rise to long-range dependence and self-similarity, which we studied by estimating the Hurst exponent (H). Our analysis based on seven different Hurst estimators signifies that the traffic patterns are stochastically self-similar (0.5 ≤ H ≤ 1.0). We believe this is an important finding, which will influence the design and deployment of the next generation vehicular network and also aid in the development of opportunistic communication services and applications for the vehicles. In addition, it shall provide a much needed input for the development of smart cities.

poster Share on Comparing wireless network usage: laptop vs smart-phones Authors: Udayan Kumar CISE, Gainesville, FL, USA CISE, Gainesville, FL, USAView Profile , Jeeyoung Kim cise, gainesville, USA cise, gainesville, USAView Profile , Ahmed Helmy cise, gainesvile, USA cise, gainesvile, USAView Profile Authors Info & Claims MobiCom '13: Proceedings of the 19th annual international conference on Mobile computing & networkingSeptember 2013 Pages 243–246https://doi.org/10.1145/2500423.2504586Published:30 September 2013Publication History 7citation559DownloadsMetricsTotal Citations7Total Downloads559Last 12 Months17Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Opportunistic sensing is a novel approach that exploits the sensing capabilities offered by smartphones and users' mobility to sense large scale areas without requiring the deployment of sensors in-situ. In this work, we propose a novel framework for fully distributed, opportunistic sensing which coherently integrates two main components that operate in DTN mode: i. participant recruitment and ii. data collection. We evaluate our approach by considering alternative implementations of the framework, and by measuring their performance via extensive trace-based simulations. Our results show how the performances of the considered protocols vary, depending on the particular scenario, and suggest guidelines for future development of distributed opportunistic sensing systems.

No abstract available.

People-centric sensing is a novel apporach that exploits the sensing capabilities offered by smartphones and the mobility of users to sense large scale areas without requiring the deployment of sensors in-situ. Given the ubiquitous nature of smartphones, people-centric sensing is a viable and efficient solution for crowdsourcing data. In this work, we propose a fully distributed, opportunistic sensing framework that involves two main components which both work in an ad hoc fashion: Recruitment and Data Collection. We analyzed the feasibility of our distributed approach for both components through preliminary simulations. The results show that our recruitment method is able to select 66% of the nodes that are appropriate for the sensing activity and 88% of the messages sent by these selected nodes reach the sink by using our data collection method.

No abstract available.

Wireless networks and personalized mobile devices are deeply integrated and embedded in our lives. Such wide adoptions of new technologies will impact user behavior and in turn will affect network performance. It is imperative to characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks. One major challenge in characterizing user behavior stems from the significant size and complexity of user behavioral data. Without summarization and dimension reduction, the sheer amount of data does not provide much useful information. The key contribution of the paper is a novel similarity metric based on a matrix representation of mobility preferences and its decomposition. This method provides an efficient way to reduce important spatiotemporal dynamics in user mobility into a few eigen-behavior vectors. This also facilitates nodes to exchange their mobility summaries and determine their mutual similarity locally. Without any assumption on the properties of user population, we use unsupervised learning (clustering) techniques to classify WLAN users. Such a user grouping scheme based on learned user behavior is crucial for applications relying on the usage context of each mobile device (e.g., participatory sensing, social-relationship-aware message forwarding). In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses to showcase its efficacy. While our findings partly validate intuitive repetitive behavioral trends and user grouping, it is surprising to find the qualitative commonalities and striking consistency of user behavior from the two universities. We discover multimodal user behavior for more than 60 percent of the users, and there are hundreds of distinct groups with unique behavioral patterns in both campuses. The sizes of the major groups follow a power-law distribution.

Users behavior and interests will play a central role in future mobile networks. We introduce a systematic method for large-scale multi-dimensional analysis of online activity for thousands of mobile users across 79 buildings over 100 web domains. We propose a modeling approach based on self-organizing maps for discovering, organizing and visualizing different mobile users' trends from billions of WLAN and netflow records. We find surprisingly that users' trends based on domains or locations can be modeled using a self-organizing map with clearly distinct characteristics. This is the first study to acquire such detailed results for wireless users Internet access patterns.

Present day society is changing daily and the mobile device (laptop, PDA, smartphones, etc.) industry is striving to keep pace with these rapid changes. With the proliferation of numerous light weight devices along with the wide spread use of wireless local area networks (WLANs) in many public places we are more connected-on-the-go than ever. Recent studies have shown that there exist repetitive behavioral trends in the association pattern of groups in large WLANs. We believe that the predictability of WLAN users may affect socially aware networks in the future. To shed light on the changes and how protocols involving the mobility of users can change, we follow a systematic analysis methodology. First, we study the evolution of user mobility using extensive network traces collected over five years, using new mobility metrics that we define. In addition, we investigate a series of prediction methods in order to better predict WLAN users and study how changes in mobility influence the success rate of such predictors. We find that the more mobile a user becomes the less predictable he or she becomes. In our study we measure mobility by looking at the distinct number of access points(APs) visited by each user. The average number of distinct APs visited steadily grows at a rate of 3 APs each year over the 5 year period WLAN trace collected at Dartmouth College. The magnitude of correlation between the AP encounter ratio and prediction over all years is at a steady 0.5 to 0.6. Coupled with this we also show that the prediction success rate for 2005-2006 users are at an average 20% lower than that of the users of 2001-2002. We also investigated different granularities of success to see how these contribute to the overall predictability of mobile users. This study of user mobility and predictability paves the way for better understanding mobile users nowadays and gives us insight to potential evolution of network users' behavior in the future.

On behalf of the Technical Program Committee, we welcome all of you to the IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC 2011) in the beautiful campus of Bahcesehir University, Istanbul, Turkey! We are indeed delighted that this year's IEEE IWCMC accomplishes its goal under the conference theme “Making Wireless Communities,” and continues its tradition of providing the premier forum for presentation of research results and experience reporting on the cutting edge research in the general areas of wireless communications and mobile computing. This year, we received more than 1000 submissions from 51 countries worldwide. Each paper received at least three peer technical reviews, comprised of 49 Symposia Chairs/Co-Chairs and a total of more than 450 TPC members from academia, government laboratories, and industries. After carefully examining all the received review reports, the IEEE IWCMC 2011 TPC finally selected about 35% high-quality papers for presentation at the conference and publication in the IEEE IWCMC 2011 proceedings. The conference program starts on Monday July 4thwith a full day Tutorials that is free of charge to all our attendees. Then, each day starts with a keynote speaker chosen from renowned world-class leaders in the area-Dr. Rick Stevens, Dr. Mario Gerla, and Dr. Sajal Das, highlighting the latest research trends in the wireless communications, mobile computing, and networks. This year, the technical sessions reflect the continued and growing interests in a wide range of spectrum, including wireless communications and networks, cross-layer design and optimization, mobile computing, wireless sensor networks, network security, and use of wireless technologies in social emergency applications. We also added a special Workshop this year to address practical aspects of Wireless Communications and Mobile Computing, such as Multihop Wireless Network Testbeds and Experiments, Network and Communications for Advanced Society, and Federated Wireless Sensor Systems (FedSenS). There are five special sessions composed of invited papers from renowned experts from around the world. Outstanding papers will be selected for four Special Issues in well known international journals. Our objective in the future is to reduce the acceptance rate further to reach 30% and less. In addition, we would like to reduce the number of Symposia and Workshops as well to meet the conference theme.

In very close future, human interests and behavior will play a central role in design of mobile networks. Thus, simulation of user behaviors is imperative for the design and evaluation of future mobile networks. However, multi-dimensionality of human interests makes it difficult for us to provide realistic simulations. In this paper, we address challenges in this regard and propose a multivariate technique based on Gaussian mixture models as the first step for simulation of mobile users' interests considering website visitations. We also introduce an evaluation technique for measuring the quality of high-dimensional simulation output. Using our collected dataset including billions of WLAN netflow records for 100 web domains at 79 buildings, we show that our technique not only keeps the characteristics of hidden behavioral groups, but also is flexible enough to approximate the real dataset with given required accuracy.

Significant advances in mobile networks have taken place recently, specifically with regard to multi-hop wireless networks, including DTNs and sensor networks. A testing environment that is conducive to realistic evaluation of such networks and their protocols and services is critical. In our efforts toward realizing this goal, we propose a novel mobile testbed of two main components. The first consists of a network of robots with personality-mimicking, human-encounter behaviors, which is our focus for this work. The personality is built upon behavioral profiling of mobile users based on our extensive wireless-network analysis. The second component combines the testbed with human society using a new concept that we refer to as participatory testing utilizing crowd sourcing. We blend these two components in the design of our testbed to bridge the gap between controlled and uncontrolled environments. Our prototype testbed demonstrates a realistic mobile networking testbed that emulates human behavior based on profiles while providing a scalable and reproducible environment. In addition, we report on our initial actual implementation of the proposed testbed architecture using two iRobot Create, one HP iPAQ PDA and 9 Nokia tablet devices. Our initial results show that by incorporating personalities into the mobile nodes, they can use autonomous, distributed and efficient algorithms to identify communities based on profile similarity. Correlating such community identification with the mobility of the nodes can indeed result in behavior mimicking traced mobile communities in the real world. Our work is the first to investigate this direction and opens several promising opportunities for research in the area of mobile test beds with many variations in DTN evaluation and participatory networking.

In mobile networking, the main goal of mobility modeling and simulation is the ability to accurately reproduce effects of realistic mobility on the performance of networking protocols. In the areas of adhoc and delay tolerant networks (DTNs), recent work on mobility modeling focused on replicating metrics of encounter statistics and spatio-temporal preferences. No studies have been conducted, however, to show whether matching these metrics is sufficient to accurately reproduce DTN protocol performance. In this study, we address this specific problem, and attempt to show the sufficiency (or lack thereof) of existing encounter and mobility metrics in reproducing realistic effects of mobility on networking protocols. We first analyze the characteristics of two well-established mobility models; the random direction and the time-variant community (TVC) models, and study whether they capture encounter statistics and preference patterns observed in real-world traces. Second, we contrast the performance of epidemic routing in DTNs based on the mobility models, to that based on extensive mobility traces. We provide two main findings. First, careful parameterization of the models can indeed replicate the metrics in question (e.g., inter-encounter time distribution). Second, even carefully crafted mobility models surprisingly result in protocol performance that is dramatically different from the trace-driven performance. The difference in message delivery delays can reach 67%, while difference in reachability approaches 80%. Such findings strongly suggest the need to revisit mobility modeling. Furthermore, they clearly show the insufficiency of existing encounter and preference metrics as a measure of mobility model goodness. Systematically establishing a new set of meaningful mobility metrics should certainly be addressed in future works.

User online behavior and interests will play a central role in future mobile networks. We introduce a systematic method for large-scale multi-dimensional modeling and analysis of online activity and mobility for thousands of mobile users across 79 buildings over a variety of web domains. We propose a modeling approach based on kind of neural-networks, called self-organizing maps (SOM), for discovering, organizing and visualizing different mobile users' trends from billions of WLAN records. We find surprisingly that users' trends based on domains and locations can be accurately modeled using a self-organizing map with clearly distinct characteristics. We also find many non-trivial correlations between different types of web domains and locations.

IEEE 802.11-based devices employ rate adaptation algorithms to dynamically switch data rates to accommodate the fluctuating wireless channel conditions. Many studies observed that when there are other stations transmitting in the network, existing rate adaptation performance degrades significantly due to its inability to differentiate losses between wireless noise and contention collisions. In this paper, we first conduct a systematic evaluation on the effectiveness of various rate adaptation protocols, which try to address this issue by exploiting optional RTS frames to isolate the wireless losses from collision losses. We observe that these existing schemes do not perform well in many background traffic scenarios and can mislead the rate adaptation algorithms to persist on using similar data rate combinations regardless of background traffic level, thus resulting in performance penalty in certain scenarios. The fundamental challenge is to dynamically adjust the rate selection decision objectives with respect to different background traffic levels, as well as fluctuating wireless conditions. In light of such observations, we design a new Background traffic-aWAre RatE adaptation algorithm (BEWARE) that addresses the above challenge. BEWARE uses a mathematical model to calculate on the fly the expected packet transmission time based on current wireless channel and background traffic conditions. We implement BEWARE design in a Linux-based driver, and the test-bed experiment results show that BEWARE outperforms other rate adaptation algorithms by up to 250% in various indoor and outdoor scenarios.

Study on human mobility is gaining increasing attention from the research community with its multiple applications to use in mobile networks, particularly for the purpose of message delivery in the Delay Tolerant Networks. To understand the potential of mobile nodes better as message relays, our study investigates the encounter pattern of mobile devices. Specifically, we examine the extensive network traces that reflect mobility of communication devices. We analyze the periodicity in encounter pattern by using power spectral analysis. Strong periodicity was observed among rarely encountering mobile nodes while the periodicity was weaker among frequently encountering nodes. Further, we present a method to search regularly encountering pairs and discuss the findings. To our knowledge, we are the first to analyze the periodicity of encounter pattern with large network traces, which is a critical basis for designing an efficient delivery scheme using mobile nodes.

School and College campuses face a perceived threat of violent crimes and require a realistic plan against unpredictable emergencies and disasters. Existing emergency systems (e.g., 911, campus-wide alerts) are quite useful, but provide delayed response (often tens of minutes) and do not utilize proximity or locality. There is a need to exploit proximity-based help for immediate response and to deter any crime. In this paper, we propose SHIELD, an on-campus emergency rescue and alert management service. It is a fully distributed infrastructureless platform based on proximity-enabled trust and cooperation. It relies on nearby localized responses sent using Bluetooth and/or WiFi to achieve minimal response time and maximal availability thereby augmenting the traditional notion of centralized emergency services. Analysis of campus crime statistics and WLAN traces surprisingly show a strong positive correlation (over 55%) between on-campus crime statistics and spatiotemporal density distribution of on-campus mobile users. This result is promising to develop a platform based on mutual trust and cooperation. Finally, we also show a prototype application to be used in such scenarios.

Many interactions between network users rely on trust, which is becoming particularly important given the security breaches in the Internet today. These problems are further exacerbated by the dynamics in wireless mobile networks. In this paper we address the issue of trust advisory and establishment in mobile networks, with application to ad hoc networks, including DTNs. We utilize encounters in mobile societies in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. Four new trust advisor filters are introduced - including encounter frequency, duration, behavior vectors and behavior matrices - and evaluated over an extensive set of real-world traces collected from a major university. Two sets of statistical analyses are performed; the first examines the underlying encounter relationships in mobile societies, and the second evaluates DTN routing in mobile peer-to-peer networks using trust and selfishness models. We find that for the analyzed trace, trust filters are stable in terms of growth with time (3 filters have close to 90% overlap of users over a period of 9 weeks) and the results produced by different filters are noticeably different. In our analysis for trust and selfishness model, our trust filters largely undo the effect of selfishness on the unreachability in a network. Thus improving the connectivity in a network with selfish nodes.

Human behavior and interests will play a central role in future mobile networks. We introduce a systematic method for large-scale multi-dimensional analysis of online activity for thousands of mobile users over 100 web domains. We propose a modeling approach based on self-organizing maps for discovering, organizing and visualizing different mobile users' trends from billions of WLAN and netflow records. We find surprisingly that users' trends can be modeled using a self-organizing map with clearly distinct characteristics while similar domains are modeled by neighboring nodes. This is the first study to acquire such detailed results for mobile Internet usage.

The next frontier for sensor networks is sensing the human society. Several mobile societies are emerging, especially with wide deployment of wireless LANs (WLANs) on campuses. WLAN traces can provide much insight into mobile user behavior. Such insight is essential to develop realistic models and to design better networks, and analyze effects of social attributes on mobile network usage. The most extensive libraries of wireless traces are collected from university campuses, are anonymized and do not provide affiliation, gender or preference information explicitly. Hence, it becomes a challenge to analyze network usage characteristics for social groups using the existing traces. In this paper, we present two novel scientific techniques to classify WLAN users into social groups. The first technique uses mapping of the traces into buildings (e.g., dept. buildings, libraries, sororities and fraternities) to extract affiliation and gender information based on network usage statistics. The second technique utilizes directory information that can be linked to WLAN users to extract useful information. For example, usernames of the WLAN users (if available) can be used to find user's gender based on first name and databases. As a case study we perform classification and behavior analysis of users by gender. Extensive WLAN traces from two major universities are collected over three years and analyzed. Results from both the methods are cross-validated and show more than 90% correspondence.

Design and simulation of future mobile networks will center around human interests and behavior. We propose a design paradigm for mobile networks driven by realistic models of users' on-line behavior, based on mining of billions of wireless-LAN records. We introduce a systematic method for large-scale multi-dimensional co-clustering of web activity for thousands of mobile users at 79 locations. We find surprisingly that users can be consistently modeled using ten clusters with disjoint profiles. Access patterns from multiple locations show differential user behavior. This is the first study to obtain such detailed results for mobile Internet usage.

In this paper, we analyze multiple wireless LAN (WLAN) traces from university and corporate campuses. In particular, we consider important events between mobile nodes in wireless networks-encounters. We seek to understand encounter patterns in the mobile network from a holistic view, using a graph analysis approach. Such an analysis sheds light on the diverse, nonhomogeneous nature of users in the given environments in terms of their encounter events with other nodes. Furthermore, we evaluate the feasibility of forming an infrastructureless network to reach most of the nodes utilizing time-varying internode connectivity through encounters, and the robustness of such an ad hoc communication network. Our analysis shows that while the encounter events are “sparse” (i.e., any given node does not encounter with many other nodes), the connectivity of the whole network is well-maintained, and a Small World pattern of nodal encounter emerges for the observation periods longer than one day. More interestingly, the encounter events collectively form a robust communication network, in which store-carry-forward message dissemination can be successful even with over 20 percent noncooperative nodes or removal of short-lived (up to minutes) encounter events.

Abstract Many interactions between network users rely on trust, which is becoming particularly important given the security breaches in the Internet today. These problems are further exacerbated by the dynamics in wireless mobile networks. In this paper, we address the issue of trust advisory and its establishment in mobile networks, with application to ad hoc networks, including delay tolerant (DTNs). We utilize encounters in mobile societies in novel ways, noticing that mobility provides opportunities to build proximity, location, and similarity based trust. Four new trust advisor filters are introduced – including encounter frequency, duration, behavior vectors, and behavior matrices. The filters are evaluated over an extensive set of real‐world traces collected from a major university. Two sets of statistical analyses are performed; the first examines the underlying encounter relationships in mobile societies, and the second evaluates DTN routing in mobile peer‐to‐peer networks using trust and selfishness models. We find that for the analyzed trace, trust filters are stable in terms of growth with time (three filters have close to 90% overlap of users over a period of 9 weeks) and the results produced by different filters are noticeably different. In our analysis for trust and selfishness model, our trust filters largely undo the effect of selfishness on the unreachability in a network. Thus improving the connectivity in a network with selfish nodes. We hope that our initial promising results open the door for further research on proximity‐based trust. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Communication and information dissemination in times of disasters could have a critical role in aiding the emergency personnel and saving lives. Unfortunately, regular communication becomes more challenging at these times, as the infrastructure might be damaged or overloaded, and the requirements for communication (e.g., response time, capacity) becomes highly critical. Ad‐hoc networks forming between wireless devices have always been considered as a promising technology at disaster recovery situations. In this paper, we examine the need for efficient and robust information dissemination mechanisms on top of infrastructure‐less ad‐hoc networks. We argue that distributed rendezvous‐based approaches are more promising for emergency data dissemination than approaches based on network flooding or centralized storage. We present the design of a geographic rendezvous‐based architecture and evaluate its performance in detail against other variants and traditional approaches. The study shows its efficiency, high success rate in delivering data, and its robustness to failures and mobility; characteristics highly valuable at emergency situations. Copyright © 2008 John Wiley & Sons, Ltd.

With the wide spread deployment of wireless LANs (WLANs), it is becoming necessary to conduct analysis of libraries of measurements taken from such operational networks. The availability of these trace libraries can be quite useful to provide realistic models of network load and user mobility, among others. To maintain user privacy, techniques of trace anonymization may be used to hide information. In this paper, we propose to study the fundamental trade off between the utility of WLAN traces and its privacy. The study provides several realistic case studies in which privacy attacks may be conducted, and questions the efficacy of existing anonymization techniques. Our initial quantitative analysis to estimate mobile users' k-anonymity in WLAN traces shows surprisingly unique usage patterns, which may compromise anonymity. The main contribution of this paper is to articulate the compelling challenges facing anonymization of wireless networks traces and to shed some light on the answer to a most intriguing question: Just how private are wireless network traces?

We propose a novel framework to critically analyze a given MAC protocol for wireless ad hoc networks with respect to its correctness criteria and performance metrics. The framework is composed of wanted state generation and test scenario generation algorithms. The wanted state generation algorithm generates a set of conditions that meet our study objective. The test generation algorithm then generates complete test scenarios that satisfy our objective, e.g., minimize the value of a particular performance metric. The core of our search engine utilizes novel algorithms that use combinations of goal-oriented backward and forward search and implications as well as heuristics that enable the generation of worst case scenarios in manageable complexity for our practical purposes. We demonstrate the effectiveness of our approach by using our framework to analyze the worst case performance, in terms of throughput and fairness of IEEE 802.11 for ad hoc networks. For all topologies, the worst case scenarios generated by our framework show the worst performance among all scenarios that we generate. The scenarios generated by our framework include the scenarios typically used for performance evaluation of IEEE 802.11 protocol. The case study of IEEE 802.11 shows that the complexity of our novel algorithms are quite practical.

In this paper we discuss a framework to visualize user dynamics in large-scale WLAN traces on the Google Earth interface. The visualization technique helps us to observe the users within the temporal and geographic context, and it is a powerful tool to show case several earlier research results, including user classification and behavior-aware protocol design. We plan to use the visualization tool to help user mobility/encounter pattern prediction in the future.

In this paper we advocate a service paradigm,profile-cast, within the communication framework of delay tolerant networks (DTN) (K. Fall, 2003). This novel approach leverages the behavioral patterns of mobile network users for delivering messages to a sub-group of users as defined by theirprofiles(e.g., interest, social affiliation, etc.). We study large data sets of usermobilityprofilesand present a case-study ofmobilityprofile-castwith asimilarity-basedforwarding protocol. We show that behavior-aware protocol design has a great potential - we reduce the total number of transmissions to 45% of flooding under 92% delivery success rate, or to only 3% transmissions of flooding under 61% delivery success rate. It also leads to shorter delay (at least 30% less) as compared to a random transmission protocol.

IEEE 802.11-based devices employ rate adaptation algorithms to dynamically switch data rates to accommodate the fluctuating wireless channel conditions. Many studies observed that, when there are other stations transmitting in the network, existing rate adaptation performance degrades significantly due to the inability of differentiating losses between wireless noise and contention collisions. Previous studies proposed to exploit optional RTS frames to isolate the wireless losses from collision losses, and thus improve rate adaptation performance. In this paper, we conduct a systematic evaluation on the effectiveness of various existing rate adaptation algorithms and related proposals for loss differentiations, with multiple stations transmitting background traffic in the network. Our main contributions are two-fold. Firstly, we observe that existing RTS-based loss differentiation schemes do not perform well in all background traffic scenarios. In addition, our study reveals that RTSbased loss differentiation schemes can mislead the rate adaptation algorithms to persist on using similar data rate combinations regardless of background traffic level, thus result in performance penalty in certain scenarios. The fundamental challenge is that a good rate adaptation algorithm must dynamically adjust the rate selection decision objectives with respect to different background traffic levels. Secondly, we design a new Background traffic aware rate adaptation algorithm (BEWARE) that addresses the above challenge. BEWARE uses a mathematical model to calculate on-the-fly the expected packet transmission time based on current wireless channel and background traffic conditions. Our simulation results show that BEWARE outperforms other rate adaptation algorithms without RTS loss differentiation by up to 250% and with RTS by up to 25% in throughput.

In this research, we first investigate the cross-layer interaction between TCP and routing protocols in the IEEE 802.11 ad hoc network. On-demand ad hoc routing protocols respond to network events such as channel noise, mobility, and congestion in the same manner, which, in association with TCP, deteriorates the quality of an existing end-to-end connection. The poor end-to-end connectivity deteriorates TCP's performance in turn. Based on the well-known TCP-friendly equation, we conduct a quantitative study on the TCP operation range using static routing and long-lived TCP flows and show that the additive-increase, multiplicative-decrease (AIMD) behavior of the TCP window mechanism is aggressive for a typical multihop IEEE 802.11 network with a low-bandwidth-delay product. Then, to address these problems, we propose two complementary mechanisms, that is, the TCP fractional window increment (FeW) scheme and the Route-failure notification using BUIk-losS Trigger (ROBUST) policy. The TCP FeW scheme is a preventive solution used to reduce the congestion-driven wireless link loss. The ROBUST policy is a corrective solution that enables on-demand routing protocols to suppress overreactions induced by the aggressive TCP behavior. It is shown by computer simulation that these two mechanisms result in a significant improvement of TCP throughput without modifying the basic TCP window or the wireless MAC mechanisms.

Recent experimental studies have shown that wireless links in real sensor networks can be extremely unreliable, deviating to a large extent from the idealized perfect-reception-within-range models used in common network simulation tools. Previously proposed geographic routing protocols commonly employ a maximum-distance greedy forwarding technique that works well in ideal conditions. However, such a forwarding technique performs poorly in realistic conditions as it tends to forward packets on lossy links. Based on a recently developed link loss model, we study the performance of a wide array of forwarding strategies, via analysis, extensive simulations and a set of experiments on motes. We find that the product of the packet reception rate and the distance improvement towards destination ( PRR × d ) is a highly suitable metric for geographic forwarding in realistic environments.

Encounter-based network is a frequently-disconnected wireless ad-hoc network requiring immediate neighbors to store and forward aggregated data for information disseminations. Using traditional approaches such as gateways or firewalls for deterring worm propagation in encounter-based networks is inappropriate. Because this type of network is highly dynamic and has no specific boundary, we need a fully distributed security response mechanism. We propose the worm interaction approach that relies upon automated beneficial worm generation aiming to alleviate problems of worm propagations in such networks. This work is motivated by the ‘War of the Worms’ of the Internet worms between competing worms such as NetSky, Bagle and MyDoom. To understand the dynamic of worm interactions and its performance, we mathematically model several classes of worms and interactions using ordinary differential equations and analyze their behaviors.

’War of the worms’ is a war between opposing computer worms, creating complex worm interactions as well as detrimental impact on infrastructure. For example, in September 2003 the Welchia worms were launched to terminate the Blaster worms and patch the vulnerable hosts. In this paper, we try to answer the following questions: How can we explain the dynamic of such phenomena with a simple mathematical model? How can one worm win this war? How do other factors such as locality preference, bandwidth, worm replication size and reaction time affect the number of infected hosts? We propose a new Worm Interaction Model (based upon and extending beyond the epidemic model) focusing on random-scan worm interactions. We also propose a new set of metrics to quantify effectiveness of one worm terminating other worm. We validate our worm interaction model using extensive ns-2 simulations. This study provides the first work to characterize and investigate worm interactions of random-scan worms in multi-hop networks.

Realistic mobility models are important to understand the performance of routing protocols in wireless ad hoc networks, especially when mobility-assisted routing schemes are employed, which is the case, for example, in delay-tolerant networks (DTNs). In mobility-assisted routing, messages are stored in mobile nodes and carried across the network with nodal mobility. Hence, the delay involved in message delivery is tightly coupled with the properties of nodal mobility. Currently, commonly used mobility models are simplistic random i.i.d. model that do not reflect realistic mobility characteristics. In this paper we propose a novel time-variant community mobility model. In this model, we define communities that are visited often by the nodes to capture skewed location visiting preferences, and use time periods with different mobility parameters to create periodical re-appearance of nodes at the same location. We have clearly observed these two properties based on analysis of empirical WLAN traces. In addition to the proposal of a realistic mobility model, we derive analytical expressions to highlight the impact on the hitting time and meeting times if these mobility characteristics are incorporated. These quantities in turn determine the packet delivery delay in mobility-assisted routing settings. Simulation studies show our expressions have error always under 20%, and in 80% of studied cases under 10%.

Previously, we have developed a framework to perform systematic analysis of CSMA/CA based wireless MAC protocols. The framework first identifies protocol states that meet our study objective of minimizing a given performance metric. It then applies search techniques and heuristics to construct sequences of protocol events in a given topology that satisfy our objective. In this paper, we demonstrate that our framework can easily be extended to evaluate performance of new protocols by evaluating two completely different variants, namely MAC protocols for (i) quality of service (QoS), and (ii) power control. In each case, we identify previously unknown problems with the protocol. In particular, we generate scenarios where throughput of a lower priority class can be as high as 5 times compared to the throughput of a higher priority class, thus contradicting the basic notion of QoS. Traditional performance evaluation approaches typically evaluate average performance but do not capture the worst cases, nor do they expose the protocol breaking points. Thus this paper demonstrates the usefulness of a systematic approach in evaluating the protocol breaking points.

Recent years have witnessed significant growth in the adoption of portable wireless communication and computing devices (e.g., laptops, PDAs, smart phones) and large-scale deployment of wireless networks (e.g., cellular, WLANs). We envision that future usage of mobile devices and services will be highly personalized. Users will incorporate these new technologies into their daily lives, and the way they use new devices and services will reflect their personality and lifestyle. Therefore it is imperative to study and characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks and services. In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses. We represent the data using location-preference vectors, and utilize unsupervised learning (clustering) to classify trends in user behavior using novel similarity metrics. Matrix decomposition techniques are used to identify (and differentiate between) major patterns. We discover multi-modal user behavior and hundreds of distinct groups with unique behavioral patterns in both campuses, and their sizes follow a power-law distribution. Our methods and findings might provide new directions in network management and behavior-aware network protocols and applications, to name a few.

Recent advance in IEEE 802.11 based standard has pushed the wireless bandwidth up to 600Mbps while keeping the same wireless medium access control (MAC) schemes for full backward compatibility. However, it has been shown that the inefficient protocol overhead casts a theoretical throughput upper limit and delay lower limit for the IEEE 802.11 based protocols, even the wireless data rate goes to infinitely high. Such limits are important to understand the bottleneck of the current technology and develop insight for protocol performance improvements. This paper uses a queuing system approach to extend the discussions of IEEE 802.11 protocol throughput and delay limits to the situation that arbitrary non-saturated background traffic is present in the network. We derive analytical models to quantify the performance limits for distributed coordination function (DCF) of legacy 802.11a/b/g and enhanced distributed coordination access (EDCA) of IEEE 802.11e. We find such limits are functions of the underlying MAC layer backoff parameters and algorithms, and are highly dependent on the load that background traffic injects into the network. Depending on the rate of arrival traffic, the packet delay limit may become unbounded such that no delay sensitive services can be operated under such condition. Moreover, we also discuss the effects of different frame aggregation schemes on the performance limits when data rate is infinite. The developed model and analysis provide a comprehensive understanding of the performance limitations for IEEE 802.11 MAC, and are useful in gauging the expected QoS for the purposes such as admission control

Summary form only given. In wireless ad hoc and sensor networks, building efficient and scalable protocols is a very challenging task due to the lack of infrastructure and the high dynamics. Geographic protocols, that take advantage of the location information of nodes, are very valuable in these environments. The state required to be maintained is minimum and the discovery overhead is low, in addition to the fast response to dynamics. In this paper, we present a state-of-the-art overview of geographic protocols providing basic functions such as geographic routing, geocasting, and geographic-based rendezvous mechanisms. We introduce also some of our work on assessing and improving the robustness of geographic protocols to nonideal realistic conditions corresponding to the real-world environments.

Providing approximate max-min fair bandwidth allocation among flows within a network or at a single router has been an important research problem. In this paper, we study the space complexity of fairness algorithms, and the communication complexity of distributed global fairness algorithms. We show that in order to enforce max-min fairness with bounded errors, a router must maintain per-flow state. Then we present a practical edge-marking based architecture to demonstrate the enforcement of approximate global max-min fairness for representative scenarios with multiple bottlenecks and non-responsive traffic. We validate our architecture using packet level simulations.

This paper describes systematical development of TCP adversarial scenarios where we use short-lived TCP flows to adversely influence long-lived TCP flows. Our scenarios are interesting since, (a) they point out the increased vulnerabilities of recently proposed scheduling, AQM and routing techniques that further favor short-lived TCP flows and (b) they are more difficult to detect when intentionally found to target long-lived TCP flows. We systematically exploit the ability of TCP flows in slow-start to rapidly capture greater proportion of bandwidth compared to long-lived TCP flows in congestion avoidance phase, to a point where they drive long-lived TCP flows into timeout. We use simulations, analysis and experiments to systematically study the dependence of the severity of impact on long-lived TCP flows on key parameters of short-lived TCP flows-including their locations, durations and numbers, as well as the intervals between consecutive flows. We derive characteristics of pattern of short-lived flows that exhibit extreme adverse impact on long-lived TCP flows. Counter to common beliefs, we show that targeting bottleneck links does not always cause maximal performance degradation for the long-lived flows. In particular, our approach illustrates the interactions between TCP flows and multiple bottleneck links and their sensitivities to correlated losses in the absence of 'non-TCP friendly' flows and paves the way for a systematic synthesis of worst-case congestion scenarios. While randomly generated sequences of short-lived TCP flows may provide some reductions (up to 10%) in the throughput of the long-lived flows, the scenarios we generate cause much greater reductions (>85%) for several TCP variants and for different packet drop policies (DropTail, RED).

The IEEE 802.11g standard has been proposed to enhance the data rate of wireless LAN connections up to 54 Mbps, while ensuring backward compatibility with legacy 802.11b devices at the same time. However, in a hybrid 802.11b/g network, the throughput of 802.11g devices is compromised because of not only the overhead to interoperate with 802.11b devices, but also the unbalanced medium contentions between devices with different versions of the standard. We propose a hybrid Markov-chain based model to quantify this throughput reduction effect in 802.11b/g mixed scenarios. The analytical model is further verified with simulations and field measurements under different station numbers, data rates, and data packet sizes. In addition, a simple frame-bursting technique is shown to balance the throughput between 802.11b and 802.11g stations.

Analyzing TCP operation over 802.11 multihop ad hoc networks involves a cross-layer study. In this work, we investigate the effect of congestion and MAC contention on the interaction between TCP and on-demand ad hoc routing protocol in the 802.11 ad hoc networks. Our study reveals several problems stemming from lack of coordination and sharing in such networks. It is observed that TCP induces the over-reaction of routing protocol and hurts the quality of end-to-end connection. So, one of the critical sources of lowering TCP throughput lies in the TCP window mechanism itself. To fix this problem, we propose a fractional window increment (FeW) scheme for TCP to prevent the over-reaction of the on-demand routing protocol by limiting TCP's aggressiveness. The proposed scheme is applicable to a wide range of transport protocols using the basic TCP mechanism, and the protocol behavior is analytically tractable. Our simulation results demonstrate that the proposed scheme can dramatically improve TCP performance and network stability in a variety of 802.11 multihop networks. For example, in some chain-like topologies, the proposed scheme outperforms basic TCP by over 90%, and recent related variants of TCP (ADTCP, LRED) by over 70%.

Mobile ad hoc networks (MANETs) provide a lot of promise for many practical applications. However, MANETs are vulnerable to a number of attacks due to its autonomous nature. DoS/DDoS attacker traceback is especially challenging in MANETs for the lack of infrastructure. In this paper, we propose an efficient on-the-fly search technique, SWAT, to trace back DoS and DDoS attackers in MANETs. Our scheme borrows from small worlds, utilizes the concept of contacts, and use traffic pattern matching (TPM) and traffic volume matching (TVM) techniques. We also propose multi-directional search, in-network processing and query suppression to reduce communication overhead in energy-constrained MANETs and increase traceback robustness against spoofing and collusion. Simulation results show that SWAT successfully traces back DoS and DDoS attacker under reasonable background traffic. In addition, SWAT incurs low communication overhead (22% compared to flooding-based search).

This paper introduces a new approach that addresses data contamination problems from attacks in unattended wireless sensor networks. We propose a sliding-window based spatio-temporal correlation analysis called "Abnormal Relationships Test (ART)" to effectively detect, respond and immune to inserted spoofed data from both various-ID impersonators and compromised nodes. Also a systematic approach is given to identify the appropriate sliding window size and correlation coefficient threshold. Our study shows that correlation property of observed phenomenon is not always transitive, different phenomenon from same set of nodes at the same or different period of time can have different correlation coefficients. Our simulation results reveal interesting relationships of outlier percentage and correlation coefficient. With proper parameter setting ART achieves high attack detection rate (90% for correlated attacks and 94% for random attacks even with 100% data insertion).

Abstract — We investigate the combination of distributed ge-ographic routing with transmission power control for energy efficient delivery of information in multihop wireless networks. Using realistic models for wireless channel fading as well as radio modulation and encoding, we first show that the optimal power control strategy over a given link should set the transmission power to achieve a special signal-to-noise ratio (SNR) constant that can be computed using an elegant characteristic equation. Counter-intuitively, for typical radios, this corresponds to an optimal operating point of SNR that lies in the transitional region (where packet error rates are non-negligible). We then propose a local power efficiency metric for distributed routing such that at each step the transmitter picks as the next hop the neighbor for which this metric is maximized. Through extensive simulations, we compare the performance of the proposed algorithm and globally optimal routing algorithms. We show that in randomly deployed 2-D networks, the combination of this local metric for routing with optimal power control has close performances, in terms of average power consumption under different node density settings and physical transmission power limits, to the best strategy using global network link state information. In particular, when electronic power is relatively low, the proposed algorithm can provide up to six times reduction in power usage compared to channel-unaware routing algorithms. I.

One of the main challenges in ad hoc networks research is understanding the effect of mobility on the performance of routing protocols. In a previous study, we have shown why mobility affects the performance of routing protocols. In this study, we further extend our approach to analyze the interplay between mobility and the protocol mechanistic building blocks. Through this approach we hope to explain how performance varies with mobility by decomposing the protocol into parameterized mechanistic building blocks based on their functionalities. Then, we apply this approach to reactive MANET routing protocols like AODV and DSR, which enables us to build a common building block architecture that encompasses these reactive protocols. The effect of mobility on each building block is evaluated. We are specifically interested in understanding the contribution of each building block to the overall protocol performance. Through simulations, several lessons on protocol design are learnt. For example, in both AODV and DSR, flooding and caching seem to have a great effect on performance, while salvaging in DSR barely seems to have an effect on the protocol performance.

A wireless ad hoc network is an infrastructureless network where nodes are mobile. The evaluation of ad hoc networking protocols is very sensitive to mobility. As a result, exploring a set of rich mobility models in ad hoc networks is essential. The purpose of this paper is to introduce a classification of various mobility models, in addition to proposing the contraction, expansion, and hybrid mobility models. These proposed models cover scenarios in which nodes merge, scatter, or switch to different movement patterns over time. We also investigate a set of mobility metrics to capture characteristics of mobility. We implement our mobility models in the IMPORTANT framework in NS-2. We use our framework to evaluate the performance of ad hoc routing protocols. Our study shows that no one metric is sufficient to capture mobility, but average node degree and link duration are very useful in capturing mobility dynamics. We note a wide spectrum of performance outcomes with mobility. Also, we observe that in some scenarios (involving contraction) performance improves with added velocity.

A mobile ad-hoc network (MANET) is a collection of wireless nodes that cooperatively form a network in the absence of any infrastructure or administration. The grid location service (GLS) is a well-known distributed location service that tracks mobile node locations in ad hoc networks. GLS can be used as a location service in a geographic-based routing protocol to considerably improve the scaling properties of mobile networks. Previous studies of GLS have used the random way point (RWP) mobility model for the performance evaluation. While RWP is useful, it may not capture some important mobility characteristics, of scenarios where MANETs may be deployed. In this paper we re-visit the GLS architecture and evaluate its performance over a wide, variety of parameter settings and mobility models. We study the behavior of GLS under different mobility models including Freeway, Manhattan and Group mobility models. These models capture interesting mobility characteristics like spatial dependence, temporal dependence and geographic restrictions. Our results show that protocol performance is indeed sensitive to the mobility model. We attribute this significant variance of performance to the interaction between the dynamics of the network connectivity and the protocol mechanisms, and analyze such interaction in our study. One interesting finding is that for group mobility, where; node movement is highly correlated and location discovery is conceivably simpler, the performance of GLS in fact degrades instead of improving. This different behavior for group mobility points to a subtle interaction and deeper coupling between the mobility model and the underlying grid.

Sensor networks would be usually used for the collection of measured data. In many cases, a tree structure is formed for data query, data dissemination and other operations. As sensor nodes fail, this underlying tree structure is impacted or, at the worst case, disabled. In this paper, we propose and compare three algorithms to restore the tree structure for sensor network under network dynamics. Two of the algorithms use global information at the base station (or sink), while the third uses only local information. Through simulations, we observe that the localized algorithm outperforms the two global algorithms in terms of communication cost, corresponding energy and latency. In addition, we also gain a deeper insight into the performance tradeoff for algorithms in sensor network. We clearly identify energy-latency tradeoff for the global algorithms, as well as energy-accuracy (optimality) tradeoff for the localized algorithm.

We propose a novel, location-centric, architecture for isolating misbehavior and establishing trust routing in sensor networks. Our scheme fits the data-centric nature of sensor networks and is suitable for use in energy-constrained networks. Much of our protocols operate in the sinks relieving the sensors from a lot of functionality. Our protocols select trusted paths that do not include misbehaving nodes, by identifying insecure locations and routing around them efficiently via detour points using embedded blacklists and modified geographic or trajectory routing. For insecure location discovery we propose efficient one-shot probing. Cheat-proofing is achieved using location correlation to remove false reporting. Our simulations show how our scheme effectively increases the throughput and energy-efficiency of a sensor network and alleviates the effect of route infection in geographic routing.

Summary form only given. In large-scale wireless networks such as mobile ad hoc and sensor networks, efficient and robust service discovery and data-access mechanisms are both essential and challenging. Rendezvous-based mechanisms provide a valuable solution for provisioning a wide range of services. We describe rendezvous regions (RRs) - a novel scalable rendezvous-based architecture for wireless networks. RR is a general architecture proposed for service location and bootstrapping in ad hoc networks, in addition to data-centric storage, configuration, and task assignment in sensor networks. In RR the network topology is divided into geographical regions, where each region is responsible for a set of keys representing the services or data of interest. Each key is mapped to a region based on a hash-table-like mapping scheme. A few elected nodes inside each region are responsible for maintaining the mapped information. The service or data provider stores the information in the corresponding region and the seekers retrieve it from there. We run extensive detailed simulations, and high-level simulations and analysis, to investigate the design space, and study the architecture in various environments including node mobility and failures. We evaluate it against other approaches to identify its merits and limitations. The results show high success rate and low overhead even with dynamics. RR scales to large number of nodes and is highly robust and efficient to node failures. It is also robust to node mobility and location inaccuracy with a significant advantage over point-based rendezvous mechanisms.

In the absence of location errors, geographic routing - using a combination of greedy forwarding and face routing - has been shown to work correctly and efficiently. The effects of location errors on geographic routing have not been studied before. In this work we provide a detailed analysis of the effects of location errors on the correctness and performance of geographic routing in static sensor networks. First, we perform a micro-level behavioral analysis to identify the possible protocol error scenarios and their conditions and bounds. Then, we present results from an extensive simulation study of GPSR and GHT to quantify the performance degradation due to location errors. Our results show that even small location errors (of 10% of the radio range or less) can in fact lead to incorrect (non-recoverable) geographic routing with noticeable performance degradation. We then introduce a simple modification for face routing that eliminates probable errors and leads to near perfect performance.

Recent experimental studies have shown that wireless links in real sensor networks can be extremely unreliable, deviating to a large extent from the idealized perfect-reception-within-range models used in common network simulation tools. Previously proposed geographic routing protocols commonly employ a maximum-distance greedy forwarding technique that works well in ideal conditions. However, such a forwarding technique performs poorly in realistic conditions as it tends to forward packets on lossy links. We identify and illustrate this weak-link problem and the related distance-hop trade-off, whereby energy efficient geographic forwarding must strike a balance between shorter, high-quality links, and longer lossy links. The study is done for scenarios with and without automatic repeat request (ARQ).

Geocasting is the delivery of packets to nodes within a certain geographic area. For many applications in wireless networks, geocasting is an important and frequent communication service. The challenging problem in geocasting is distributing the packets to all the nodes within the geocast region with high probability but with low overhead. According to our study we notice a clear tradeoff between the proportion of nodes in the geocast region that receive the packet and the overhead incurred by the geocast packet especially at low densities and irregular distributions. We present two novel protocols for geocasting that achieve high delivery rate and low overhead by utilizing the local location information of nodes to combine geographic routing mechanisms with region flooding. We show that the first protocol (GFG) has close-to-minimum overhead in dense networks and that the second protocol (GFPG) provides guaranteed delivery without global flooding or global network information even at low densities and with the existence of region gaps. A practical version of the second protocol (GFPG) has the desirable property of perfect delivery at all densities and close-to-minimum overhead at high densities. We evaluate our mechanisms and compare them using simulation to the currently proposed geocasting mechanisms. The results show the significant improvement in delivery rate (up to 63% higher delivery percentage in low density networks) and reduction in overhead (up to 80% reduction) achieved by our mechanisms.

Geographic routing in mobile ad hoc networks has proved to provide drastic performance improvement over strictly address-centric routing schemes. While geographic routing has been shown to be correct and efficient when location information is accurate, its performance in the face of location errors is not well understood. In this paper, we study the effect of inaccurate location information caused by node mobility under a rich set of scenarios and mobility models. We identify two main problems, named LINK and LOOP, that are caused by mobility-induced location errors. Based on the analysis via ns-2 simulations, we propose two mobility prediction schemes - neighbor location prediction (NLP) and destination location prediction (DLP) to mitigate these problems. Simulation results have shown noticeable improvement under all mobility models used in our study. Our schemes achieve up to 27% improvement in packet delivery and 37% reduction in network resource wastage on average without incurring any additional communication or intense computation.

We develop a detailed approach to study how mobility impacts the performance of reactive mobile ad hoc network routing protocols. In particular, we examine how the statistics of path durations including probability density functions vary with the parameters such as the mobility model, relative speed, number of hops, and radio range. We find that at low speeds, certain mobility models may induce multimodal distributions that reflect the characteristics of the spatial map, mobility constraints and the communicating traffic pattern. However, this paper suggests that at moderate and high velocities the exponential distribution with appropriate parameterizations is a good approximation of the path duration distribution for a range of mobility models. Analytically, we show that the reciprocal of the average path duration has a strong linear relationship with the throughput and overhead of dynamic source routing (DSR), which is also confirmed by simulation results. In addition, we show how the mathematical expression obtained for the path duration distribution can also be used to prove that the nonpropagating cache hit ratio in DSR is independent of velocity for the freeway mobility model. These two case studies illustrate how various aspects of protocol performance can be analyzed with respect to a number of significant parameters including the statistics of link and path durations.

Congestion control is a major requirement for multicast to be deployed in the current Internet. Due to the complexity and conflicting tradeoffs, the design and testing of multicast congestion control protocols is difficult. In this paper, we present a novel framework for systematic testing of multicast congestion control protocols. In our framework, we first design an appropriate model for the studied protocols based on the protocols specifications and correctness conditions, and then we develop an automated search engine to generate all possible error scenarios and filter these errors to come up with a selected set of scenarios that we evaluate in more detailed simulations. Our methodology helps in identifying the potential problems of the studied protocols and points to possible improvements. We hope that this will provide a valuable tool to expedite the development and standardization of such protocols.

Geographic routing has been introduced in mobile ad hoc networks and sensor networks. Under ideal settings, it has been proven to provide drastic performance improvement over strictly address centric routing schemes. While geographic routing has been shown to be correct and efficient when location information is accurate, its performance in the face of location errors is not well understood. We study the effect of inaccurate location information caused by node mobility under a rich set of scenarios and mobility models. We identify two main problems, named LLNK and LOOP, that are caused by mobility-induced location errors. Based on analysis via ns-2 simulations, we propose two mobility prediction schemes - neighbor location prediction (NLP) and destination location prediction (DLP) to mitigate these problems. Simulation results show noticeable improvement under all mobility models used in our study. Under the settings we examine, our schemes achieve up to 27 percent improvement in packet delivery and 37 percent reduction in network resource wastage, on average without incurring any additional communication or intense computation.

The advent of multicast and the growth and complexity of the Internet has complicated network protocol design and evaluation. Evaluation of Internet protocols usually uses random scenarios or scenarios based on designers' intuition. Such an approach may be useful for average case analysis but does not cover boundary-point (worst or best case) scenarios. To synthesize boundary-point scenarios, a more systematic approach is needed. In this paper, we present a method for automatic synthesis of worst and best case scenarios for protocol boundary-point evaluation. Our method uses a fault-oriented test generation (FOTG) algorithm for searching the protocol and system state space to synthesize these scenarios. The algorithm is based on a global finite state machine (FSM) model. We extend the algorithm with timing semantics to handle end-to-end delays and address performance criteria. We introduce the notion of a virtual LAN to represent delays of the underlying multicast distribution tree. Our algorithms utilize implicit backward search using branch and bound techniques and start from given target events. As a case study, we use our method to evaluate variants of the timer suppression mechanism, used in various multicast protocols, with respect to two performance criteria: overhead of response messages and response time. Simulation results for reliable multicast protocols show that our method provides a scalable way for synthesizing worst case scenarios automatically. Results obtained using stress scenarios differ dramatically from those obtained through average case analyses. We hope for our method to serve as a model for applying systematic evaluation to other multicast protocols.

In future ad hoc networks, transactions are expected to become one of the primary types of flows. Transactions require only a very small number of packets to complete. Hence, providing optimal (or shortest path) routes to such transfers consumes more energy than the actual transfer. Conventional routing protocols are thus unsuitable for transactions. In this paper, we present a novel architecture for transaction routing in large-scale ad hoc networks. In our approach we aim at reducing the total energy consumption of successful delivery as opposed to finding shortest path routes. Our architecture uses a hybrid approach, where each mobile node obtains information about nodes in its proximity, up to R hops away, using a proactive link state protocol. Beyond the proximity, we introduce the novel notion of contacts that act as short cuts to reduce the degrees of separation between the request source and the target. We propose an efficient, on-demand, contact selection protocol. No location information is assumed. Extensive simulations are used to evaluate the performance of our protocol in terms of energy consumption and request success rate. We compare our architecture to flooding and ZRP. Our results show substantial power savings for our contact-based technique, especially for large networks.

A mobile ad hoc network (MANET) is a collection of wireless mobile nodes forming a temporary network without using any existing infrastructure. Since not many MANETs are currently deployed, research in this area is mostly simulation based. Random waypoint is the commonly used mobility model in these simulations. Random waypoint is a simple model that may be applicable to some scenarios. However, we believe that it is not sufficient to capture some important mobility characteristics of scenarios in which MANETs may be deployed. Our framework aims to evaluate the impact of different mobility models on the performance of MANET routing protocols. We propose various protocol independent metrics to capture interesting mobility characteristics, including spatial and temporal dependence and geographic restrictions. In addition, a rich set of parameterized mobility models is introduced including random waypoint, group mobility, freeway and Manhattan models. Based on these models several 'test-suite' scenarios are chosen carefully to span the metric space. We demonstrate the utility of our test-suite by evaluating various MANET routing protocols, including DSR, AODV and DSDV. Our results show that the protocol performance may vary drastically across mobility models and performance rankings of protocols may vary with the mobility models used. This effect can be explained by the interaction of the mobility characteristics with the connectivity graph properties. Finally, we attempt to decompose the routing protocols into mechanistic "building blocks" to gain a deeper insight into the performance variations across protocols in the face of mobility.

We develop a detailed approach to study how mobility impacts the performance of reactive MANET routing protocols. In particular we examine how the statistics of path durations including PDFs vary with the parameters such as the mobility model, relative speed, number of hops, and radio range. We find that at low speeds, certain mobility models may induce multi-modal distributions that reflect the characteristics of the spatial map, mobility constraints and the communicating traffic pattern. However, our study suggests that at moderate and high velocities the exponential distribution with appropriate parameterizations is a good approximation of the path duration distribution for a range of mobility models. The reciprocal of the average path duration is analytically shown to have a strong linear relationship with the throughput and overhead that is confirmed by the simulation results for DSR.

Recently, geographic routing in wireless networks has gained attention due to several advantages of location information. Location information eliminates the necessity to set up and maintain explicit routes. These advantages allow scalability especially in dynamic and unstable wireless networks. However, no matter which technologies or techniques a location system uses, its measurements will have some amount of quantifiable inaccuracy depending on environment and system. These inaccuracies may affect the performance and even correctness of geographic routing. However, thus far, these impacts have not been studied in-depth. In this paper, we analyze the impact of location inaccuracy on geographic routing. Our study shows the significant impact of location inaccuracy on the performance of geographic routing in terms of packet drops, non-optimal paths and routing loops.

No abstract available.

In data-centric sensor networks, where data processing and transfer are oblivious to node IDs, conventional node-based security models are not suitable. We introduce the novel concept of secure locations to address non-cooperative and malicious behavior in location-aware sensor networks. Our architecture also introduces a scalable trust-based routing protocol (TRANS) to track, update and route around untrusted locations using variants of geographic and trajectory routing. As part of our protocol we provide an efficient algorithm for identifying and isolating misbehaving or compromised sensors based on their approximate locations. Our simulations show the efficacy of our approach in malicious node isolation and route infection reduction.

Fairness to current Internet traffic, particularly TCP, is an important requirement for new protocols in order to be safely deployed in the Internet. This specifically applies to multicast protocols that should be deployed with great care. In this paper we provide a set of experiments that can be used as a benchmark to evaluate the fairness of multicast congestion control mechanisms when running with competing TCP flows. We carefully select the experiments in such a way to target specific congestion control mechanisms and to reveal the differences between TCP and the proposed multicasting protocol. This enables us to have a better understanding oft he proposed protocol behavior and to evaluate its fairness and when violations can happen. To clarify our experiments we carry them on a single-rate case study protocol, pgmcc, using NS-2 simulations. Our analysis shows the strengths and potential problems of the protocol and point to possible improvements. Several congestion control mechanisms are targeted by the experiments such as timeouts, response to ACKs and losses, independent and congestion losses effect. In addition, we evaluate multicast mechanisms such as the effect of multiple receivers, group representative selection, and feedback suppression when there is network support.

Emerging ad hoc networks are infrastructure-less networks consisting of wireless devices with various power constraints, capabilities and mobility characteristics. An essential capability in future ad hoc networks is the ability to provide scalable multicast services. This paper presents a novel adaptive architecture to support multicast services in large-scale wide-area ad hoc networks. Existing works on multicast in ad hoc networks address only small size networks. Our main design goals are scalability, robustness and efficiency. We propose a self-configuring hierarchy extending zone-based routing with the notion of contacts based on the small world graphs phenomenon and metrics of stability and mobility. We introduce a new geographic-based multicast address allocation scheme coupled with adaptive anycast based on group popularity. Our scheme promises efficient and robust operation in the common case. Also, based on the new concept of rendezvous regions, we provide a bootstrap mechanism for the multicast service; a challenge generally ignored in previous work.

We propose intra-domain multicast-based mobility as a solution for IP mobility. Our architecture addresses problems with existing IP mobility proposals; mainly scalability and handoff performance. In our scheme mobility proxies are used to allocate per-domain multicast addresses to mobiles for use in micro mobility. State aggregation is studied as an essential element to improve scalability of our approach. We introduce a simple, yet very efficient aggregation algorithm, based on bitwise lossy aggregation. An important result obtained indicates that state tends to be concentrated in less than 20% of the nodes and that our scheme is extremely efficient in reducing the state in those nodes. We show that our scheme achieves much higher aggregation gain than conventional prefix-based aggregation.

We present a new methodology for developing systematic and automatic test generation algorithms for multipoint protocols. These algorithms attempt to synthesize network topologies and sequences of events that stress the protocol's correctness or performance. This problem can be viewed as a domain-specific search problem that suffers from the state space explosion problem. One goal of this work is to circumvent the state space explosion problem utilizing knowledge of network and fault modeling, and multipoint protocols. The two approaches investigated are based on forward and backward search techniques. We use an extended finite state machine (FSM) model of the protocol. The first algorithm uses forward search to perform reduced reachability analysis. Using domain-specific information for multicast routing over LAN, the algorithm complexity is reduced from exponential to polynomial in the number of routers. This approach, however, does not fully automate topology synthesis. The second algorithm, the fault-oriented test generation, uses backward search for topology synthesis and uses backtracking to generate event sequences instead of searching forward from initial states. Using these algorithms, we have conducted studies for correctness of the multicast routing protocol PIM.

Systematic testing of robustness by evaluation of synthesized scenarios STRESS is a methodology developed for the systematic testing of protocols, and includes algorithms for generating topologies and event sequences that rigorously test the correctness or performance of a given protocol. In this paper, we apply the STRESS method to mobile IP (MIP) and the multicast address resolution server (MARS) protocol for supporting IP-multicast over ATM. For each protocol, we develop a protocol model and analyze its robustness. We also analyze complexity of the STRESS test generation algorithms. In the process, we identify the limitations of the existing STRESS models and algorithms, and propose extensions to carry out our case studies. With the aid of STRESS, we were able to identify several protocol behaviors that lead to error or performance degradation. For MIP we identified such behaviors with the crash of a home agent or the loss of a registration message. For MARS undesired behavior was detected with the crash of the MARS server or source and the selective loss of a join or leave message. The complexity of forward search was found to be O(n/sup 2/) for both MIP and MARS. Incorporating the fault model in the search was found to affect the number of states searched. The crash of a home agent in MIP, and the crash of a data source or server in MARS, were both found to increase the number of states searched. However, the asymptotic complexity was not affected.

Current multicast routing protocols can be classified into three types according to how the multicast tree is established: broadcast and prune (e.g., DVMRP, PIM-DM), membership advertisement (e.g., MO-SPF), and rendezvous-based (e.g., CBT, PIM-SM). Rendezvous-based protocols associate with each logical multicast group address, a physical unicast address, referred to as the 'core' or 'rendezvous point' (RP). Members first join a multicast tree rooted at this rendezvous point in order to receive data packets sent to the group. Rendezvous mechanisms are well suited to large wide-area networks because they distribute group-specific data and membership information only to those routers that are on the multicast distribution tree. However, rendezvous protocols require a bootstrap mechanism to map each logical multicast address to its current physical rendezvous point address. The bootstrap mechanism must adapt to network and router failures but should minimize unnecessary changes in the group-to-RP mapping. In addition, the bootstrap mechanism should be transparent to the hosts. This paper describes and analyzes the bootstrap mechanism developed for PIM-SM. The mechanism employs an algorithmic mapping of multicast group to rendezvous point address, based on a set of available RPs distributed throughout a multicast domain. The primary evaluation measures are convergence time, message distribution overhead, balanced assignment of groups to RPs, and host impact. The mechanism as a whole, and the design lessons in particular, are applicable to other rendezvous-based multicast routing protocols as well.

We propose a method for using simulation to analyze the robustness of multiparty (multicast-based) protocols in a systematic fashion. We call our method Systematic Testing of Robustness by Examination of Selected Scenarios (STRESS). STRESS aims to cut the time and effort needed to explore pathological cases of a protocol during its design. This paper has two goals: (1) to describe the method, and (2) to serve as a case study of robustness analysis of multicast routing protocols. We aim to offer design tools similar to those used in CAD and VLSI design, and demonstrate how effective systematic simulation can be in studying protocol robustness.