Human reliability analysis (HRA) is crucial in deep-sea submersible operations, where extreme stressors and equipment degradation strongly influence operator performance. Conventional SPAR-H methods are limited by static treatment of human error probabilities (HEPs) and lack of integration with equipment failure data. To address these gaps, this study develops an enhanced HRA framework that integrates hesitant fuzzy linguistic term sets (HFLTs) within a two-tuple linguistic environment to handle expert uncertainty in assessing human reliability factors (HRFs). The Decision-Making Trial and Evaluation Laboratory is applied to capture interdependencies among HRFs, and failure-in-time data are incorporated into a Weibull-based enhancement of SPAR-H to estimate time-varying HEPs. An analysis of 21 standard tasks and 14 emergency procedures indicates that HEPs increase in relation to task duration, time pressure, and the risk of equipment failure. The maximum value observed is 0.45 during emergency ascent, which is directly attributable to compounded cognitive overload, limited decision-making time, and heightened effects of equipment failure degradation. Workload redistribution significantly reduces the overall error probability, with a 44.5% reduction when tasks are shared with a copilot and a 70.6% reduction when tasks are allocated to a scientist. Benchmarking against THERP, HEART, static SPAR-H, and existing models confirms improved realism and adaptability compared to static first- and second-generation methods. These findings highlight the importance of structured task allocation and integrated human–equipment modelling for enhancing mission safety in critical operations.

In this paper, we consider a basic issue in wireless computing, where mobile devices (with relatively limited memory) fetch data (text, images, multimedia, etc.) from access points over wireless channels of fluctuating quality. When the channel quality is low, slow data downloads can contribute to application latency, and degradation of the user experience. To mitigate this issue, mobile devices can prefetch data during good quality channel periods preemptively, in anticipation of using them during low quality channel epochs. In other words, under favorable wireless channel conditions, the mobile terminal can prefetch data aggressively to reduce application latency when channel conditions have degraded. Considering channel fluctuations, memory constraints, and application latency, the issue at each point in time is whether to prefetch or not. A dynamic programming approach is taken here to determine the optimal prefetching policy. The latter is leveraged to develop a prefetching algorithm “Fetch-or-Not” (FON). We then design a randomized “Fetch-or-Not” (RFON) prefetching algorithm, which uses a randomized approximation of FON, thus lifting the need for online optimization and substantially reducing the computational complexity. Simulations are used to demonstrate that our low-complexity schemes perform well when compared to the optimal. In addition, our schemes outperform benchmark design techniques in realistic channel conditions.

In wireless media streaming from multiple access points, handoff control ensures optimal connectivity as the wireless environment fluctuates, while rebuffering control seeks to maximize user experience by avoiding playout jitters and prolonged freeze times. A joint policy aims to capture the interactions between these controls, but requires a fluctuating wireless environment to be estimated frequently, a process which can interrupt packet retrieval in many mobile applications. In this paper we develop a model for maximizing user QoE by optimally scanning wireless channels while making handoff and rebuffering decisions. We design a control scheme that utilizes partial knowledge of the wireless environment to decide when to explore alternative connections, when to handover to them, and when to exploit an access point while employing channel-aware rebuffering control. We develop a model for the general case of multiple access points, then computationally evaluate the system in specific relevant cases. We find that the resulting optimal control takes the form of threshold-based policies, and that it compares favorably to the omniscient policy with full knowledge of all channel states. We also find that the optimal decision regions can be greatly simplified with only a small decrease in optimality.

MultiNets is a system supporting seamless switch-over between wireless interfaces on mobile devices in real-time. MultiNets is configurable to run in three different modes: (i) Energy Saving mode --for choosing the interface that saves the most energy based on the condition of the device, (ii) Offload mode --for offloading data traffic from the cellular to WiFi network, and (iii) Performance mode --for selecting the network for the fastest data connectivity. MultiNets also provides a powerful API that gives the application developers: (i) the choice to select a network interface to communicate with a specific server, and (ii) the ability to simultaneously transfer data over multiple network interfaces. MultiNets is modular, easily integrable, lightweight, and applicable to various mobile operating systems. We implement MultiNets on Android devices as a show case. MultiNets does not require any extra support from the network infrastructure and runs existing applications transparently. To evaluate MultiNets, we first collect data traces from 13 actual Android smartphone users over three months. We then use the collected traces to show that, by automatically switching to WiFi whenever it is available, MultiNets can offload on average 79.82% of the data traffic. We also illustrate that, by optimally switching between the interfaces, MultiNets can save on average 21.14 KJ of energy per day, which is equivalent to 27.4% of the daily energy usage. Using our API, we demonstrate that a video streaming application achieves 43--271% higher streaming rate when concurrently using WiFi and 3G interfaces. We deploy MultiNets in a real-world scenario and our experimental results show that depending on the user requirements, it outperforms the state-of-the-art Android system either by saving up to 33.75% energy, achieving near-optimal offloading, or achieving near-optimal throughput while substantially reducing TCP interruptions due to switching.

Is it possible to migrate TCP/IP flows between different networks on modern mobile devices without infrastructure support or protocol changes? To answer this question, we make three research contributions: 1) We report a comprehensive characterization of IP traffic on 27 iPhone 3GS users for three months. 2) Driven by these findings, we devise two simple, effective, and easily deployable system mechanisms to support seamless flow migration without network support, and extensively evaluate their effectiveness using our field collected traces of real-life usage. Wait-n-Migrate leverages the fact that most flows are short lived. It establishes new flows on newly available networks but allows preexisting flows on the old network to terminate naturally. Resumption Agent takes advantage of the resumption functionality of modern protocols to securely resume flows without application intervention. Combined, they provide an unprecedented opportunity to immediately deploy policies that leverage multiple networks to improve the performance, efficiency, and connectivity of mobile devices. 3) We report an iPhone-based implementation of these system mechanisms and demonstrate their overhead to be negligible. Furthermore, we employ a sample switching policy, AutoSwitch, to demonstrate their performance. Through traces and field measurements, we show that AutoSwitch reduces user disruptions by an order of magnitude.

Many mobile applications retrieve content from remote servers via user generated queries. Processing these queries is often needed before the desired content can be identified. Processing the request on the mobile devices can quickly sap the limited battery resources. Conversely, processing user queries at remote servers can have slow response times due communication latency incurred during transmission of the potentially large query. We evaluate a network-assisted mobile computing scenario where mid-network nodes with “leasing” capabilities are deployed by a service provider. Leasing computation power can reduce battery usage on the mobile devices and improve response times. However, borrowing processing power from mid-network nodes comes at a leasing cost which must be accounted for when making the decision of where processing should occur. We study the tradeoff between battery usage, processing and transmission latency, and mid-network leasing. We use the dynamic programming framework to solve for the optimal processing policies that suggest the amount of processing to be done at each mid-network node in order to minimize the processing and communication latency and processing costs. Through numerical studies, we examine the properties of the optimal processing policy and the core tradeoffs in such systems.

We consider the problem of scalable video streaming from a server to multinetwork clients over heterogeneous access networks, with the goal of minimizing the distortion of the received videos. This problem has numerous applications including: 1) mobile devices connecting to multiple licensed and ISM bands, and 2) cognitive multiradio devices employing spectrum bonding. In this paper, we ascertain how to optimally determine which video packets to transmit over each access network. We present models to capture the network conditions and video characteristics and develop an integer program for deterministic packet scheduling. Solving the integer program exactly is typically not computationally tractable, so we develop heuristic algorithms for deterministic packet scheduling, as well as convex optimization problems for randomized packet scheduling. We carry out a thorough study of the tradeoff between performance and computational complexity and propose a convex programming-based algorithm that yields good performance while being suitable for real-time applications. We conduct extensive trace-driven simulations to evaluate the proposed algorithms using real network conditions and scalable video streams. The simulation results show that the proposed convex programming-based algorithm: 1) outperforms the rate control algorithms defined in the Datagram Congestion Control Protocol (DCCP) by about 10–15 dB higher video quality; 2) reduces average delivery delay by over 90% compared to DCCP; 3) results in higher average video quality of 4.47 and 1.92 dB than the two developed heuristics; 4) runs efficiently, up to six times faster than the best-performing heuristic; and 5) does indeed provide service differentiation among users.

Network deduplication (dedup) is an attractive approach to improve network performance for mobile devices. With traditional deduplication, the dedup~source uses only the portion of the cache at the dedup~destination that it is aware of. We argue in this work that in a mobile environment, the dedup~destination (say the mobile) could have accumulated a much larger cache than what the current dedup~source is aware of. This can occur because of several reasons ranging from the mobile consuming content through heterogeneous wireless technologies, to the mobile moving across different wireless networks.

In this paper we present Multi Nets, a system which is capable of switching between wireless network interfaces on mobile devices in real-time. Multi Nets is motivated by the need of smart phone platforms to save energy, offload data traffic, and achieve higher throughput. We describe the architecture of Multi Nets and demonstrate the methodology to perform switching in Linux based mobile OSes such as Android. Our analysis on mobile data traces collected from real users shows that with real-time switching we can save 27.4% of the energy, offload 79.82% of the data traffic, or achieve 7 times more throughput on average. We deploy Multi Nets in a real world scenario and our experimental results show that depending on the user requirements, it outperforms the state-of-the-art Android system either by saving up to 33.75% energy, or achieving near-optimal offloading, or achieving near-optimal throughput while substantially reducing TCP interruptions due to switching.

Despite the growing popularity of mobile web browsing, the energy consumed by a phone browser while surfing the web is poorly understood. We present an infrastructure for measuring the precise energy used by a mobile browser to render web pages. We then measure the energy needed to render financial, e-commerce, email, blogging, news and social networking sites. Our tools are sufficiently precise to measure the energy needed to render individual web elements, such as cascade style sheets (CSS), Javascript, images, and plug-in objects. Our results show that for popular sites, downloading and parsing cascade style sheets and Javascript consumes a significant fraction of the total energy needed to render the page. Using the data we collected we make concrete recommendations on how to design web pages so as to minimize the energy needed to render the page. As an example, by modifying scripts on the Wikipedia mobile site we reduced by 30% the energy needed to download and render Wikipedia pages with no change to the user experience. We conclude by estimating the point at which offloading browser computations to a remote proxy can save energy on the phone.

Monitoring transit traffic at one or more points in a network is of interest to network operators for reasons of traffic accounting, debugging or troubleshooting, forensics, and traffic engineering. Previous research in the area has focused on deriving a placement of monitors across the network toward the end of maximizing the monitoring utility of the network operator for a given traffic routing. However, both traffic characteristics and measurement objectives can dynamically change over time, rendering a previously optimal placement of monitors suboptimal. It is not feasible to dynamically redeploy/reconfigure measurement infrastructure to cater to such evolving measurement requirements. We address this problem by strategically routing traffic subpopulations over fixed monitors. We refer to this approach as MeasuRouting. The main challenge for MeasuRouting is to work within the constraints of existing intradomain traffic engineering operations that are geared for efficiently utilizing bandwidth resources, or meeting quality-of-service (QoS) constraints, or both. A fundamental feature of intradomain routing, which makes MeasuRouting feasible, is that intradomain routing is often specified for aggregate flows. MeasuRouting can therefore differentially route components of an aggregate flow while ensuring that the aggregate placement is compliant to original traffic engineering objectives. In this paper, we present a theoretical framework for MeasuRouting. Furthermore, as proofs of concept, we present synthetic and practical monitoring applications to showcase the utility enhancement achieved with MeasuRouting.

Retrieving information about books on a bookshelf by snapping a photo of book spines with a mobile device is very useful for bookstores, libraries, offices, and homes. In this paper, we develop a new mobile augmented reality system for book spine recognition. Our system achieves very low recognition delays, around 1 second, to support real-time augmentation on a mobile device's viewfinder. We infer user interest by analyzing the motion of objects seen in the viewfinder. Our system initiates a query during each low-motion interval. This selection mechanism eliminates the need to press a but ton and avoids using degraded motion-blurred query frames during high-motion intervals. The viewfinder is augmented with a book's identity, prices from different vendors, average user rating, location within the enclosing bookshelf, and a digital compass marker. We present a new tiled search strategy for finding the location in the bookshelf with improved accuracy in half the time as in a previous state-of-the-art system. Our AR system has been implemented on an Android smartphone.

Virtual worlds, such as Second Life, are computer-simulated spaces divided into multiple regions, in which each user controls an avatar to perform actions (such as run and fly) in order to interact with other users. Second Life incurs diverse traffic patterns in different regions and with different actions. Hence, we propose region- and action-aware Second Life clients, which adapt to and take advantages of the diverse traffic patterns for user-specified optimization criterion, such as high visual quality, low energy consumption, and short response time. To achieve this, we develop a parameterized traffic model to predict Second Life traffic patterns. We systematically derive the traffic model parameters using public Second Life traces [1], and we validate the model accuracy using another set of real network traces. To the best of our knowledge, region- and action-aware virtual world clients have never been considered in the literature. In addition, the proposed parameterized traffic model is of interest in its own right to various parties, including: (i) virtual world developers, (ii) researchers, and (iii) Internet Service Providers (ISPs).

Mobile data usage over cellular networks has been dramatically increasing over the past years. Wi-Fi based wireless networks offer a high-bandwidth alternative for offloading such data traffic. However, intermittent connectivity, and battery power drain in mobile devices, inhibits always-on connectivity even in areas with good Wi-Fi coverage. This paper presents WiFisense, a system that employs user mobility information retrieved from low-power sensors (e.g., accelerometer) in smartphones, and further includes adaptive Wi-Fi sensing algorithms, to conserve battery power while improving Wi-Fi usage. We implement the proposed system in Android-based smartphones and evaluate the implementation in both indoor and outdoor Wi-Fi networks. Our evaluation results show that WiFisense saves energy consumption for scans by up to 79% and achieves considerable increase in Wi-Fi usage for various scenarios.

Cognitive radios (CRs) can mitigate the impending spectrum scarcity problem by utilizing their capability of accessing licensed spectrum bands opportunistically. While most existing work focuses on enabling such opportunistic spectrum access for stationary CRs, mobility is an important concern to secondary users (SUs) because future mobile devices are expected to incorporate CR functionality. In this paper, we identify and address three fundamental challenges encountered specifically by mobile SUs. First, we model channel availability experienced by a mobile SU as a two-state continuous-time Markov chain (CTMC) and verify its accuracy via in-depth simulation. Then, to protect primary/incumbent communications from SU interference, we introduce guard distance in the space domain and derive the optimal guard distance that maximizes the spatio-temporal spectrum opportunities available to mobile CRs. To facilitate efficient spectrum sharing, we formulate the problem of maximizing secondary network throughput within a convex optimization framework, and derive an optimal, distributed channel selection strategy. Our simulation results show that the proposed spectrum sensing and distributed channel access schemes improve network throughput and fairness significantly, and reduce SU energy consumption for spectrum sensing by up to 74%.

Despite the successful use of local image features for large-scale object recognition, they are not effective in recognizing book spines on bookshelves. This is because some book spines contain only text components that do not yield distinguishing image features. To overcome this issue, we develop a new approach that combines a text-based spine recognition pipeline with an image feature-based spine recognition pipeline. The text within the book spine image is recognized and used as keywords to search a book spine text database. The image features of the book spine image are searched through a book spine image database. The search results of the two approaches are then carefully combined to form the final result. We implement the proposed hybrid book recognition pipeline used in a book inventory management system, and conduct extensive experiments to evaluate its performance. The experimental results show that while text-based or image feature-based systems only achieve a recall of 72%, the proposed hybrid system achieves a recall of ~91%.

Although network traces of virtual worlds are valuable to ISPs (Internet service providers), virtual world software developers, and research communities, they do not exist in the public domain. In this work, we implement a complete testbed to efficiently collect and analyze network traces from a popular virtual world: Second Life. We use the testbed to gather traces from 100 regions with diverse characteristics. The network traces represent more than 60 hours of virtual world traffic and the trace files are created in a well-structured and concise format. Our preliminary analysis on the collected traces is consistent with previous work in the literature. It also reveals some new insights: for example, local avatar/object density imposes clear implications on traffic patterns. The developed testbed and released trace files can be leveraged by research communities for various studies on virtual worlds. For example, accurate traffic models can be derived from our trace files, which in turn can guide developers for better virtual world designs

Many emerging location-aware applications require position information. However, these applications rarely use celltower-based localization because of its inaccuracy, preferring instead to use the more energy-hungry GPS.In this paper, we present CAPS, a Cell-ID Aided Positioning System. CAPS leverages near-continuous mobility and the position history of a user to achieve significantly better accuracy than the celltower-based approach, while keeping energy overhead low. CAPS is designed based on the insight that users exhibit consistency in routes traveled, and that cell-ID transition points that the user experiences can, on a frequently traveled route, uniquely identify position. To this end, CAPS uses a cell-ID sequence matching technique to estimate current position based on the history of cell-ID and GPS position sequences that match the current cell-ID sequence. We have implemented CAPS on Android-based smartphones and have extensively evaluated it at different locations, and for different platforms and carriers. Our evaluation results show that CAPS can save more than 90% of the energy spent by the positioning system compared to the case where GPS is always used, while providing reasonably accurate position information with errors less than 20% of the celltower-based scheme.

This paper addresses the problem of disseminating multiple live videos to mobile users by using a hybrid cellular and ad hoc network. Specifically, we develop techniques to optimize the overall quality of video delivery by: (a) exploiting the flexibility of layered videos for in-network adaptation to reduce the gap between video coding rate and network capacity, and (b) alleviating the load of individually handling a large number of flows at the cell tower by using device-to-device ad hoc connectivity to deliver videos. We study the problem of optimally choosing the mobile devices that will serve as gateways from the cellular to the ad hoc network, the ad hoc routes from the gateway to individual devices, and the layers to deliver on these ad hoc routes. We develop a Mixed Integer Linear Program (MILP) based solution to the considered problem. We also develop a heuristic algorithm to select the devices, routes, and layers more efficiently than the ideal, but potentially time-consuming MILP-based algorithm. We evaluate the proposed techniques via through simulations. The simulation results show that the proposed algorithms significantly outperform the current solution in terms of overall video quality, transmission latency, delivery ratio, and missed frame ratio. For example, compared to the current cellular network, the MILP-based and the heuristic algorithms result in up to 20 dB higher video quality. Furthermore, the heuristic algorithm runs efficiently yet achieves near-optimal quality: at most 2.3 dB gap across all experiments.

Monitoring transit traffic at one or more points in a network is of interest to network operators for reasons of traffic accounting, debugging or troubleshooting, forensics, and traffic engineering. Previous research in the area has focused on deriving a placement of monitors across the network towards the end of maximizing the monitoring utility of the network operator for a given traffic routing. However, both traffic characteristics and measurement objectives can dynamically change over time, rendering a previously optimal placement of monitors suboptimal. It is not feasible to dynamically redeploy/reconfigure measurement infrastructure to cater to such evolving measurement requirements. We address this problem by strategically routing traffic sub-populations over fixed monitors. We refer to this approach as MeasuRouting. The main challenge for MeasuRouting is to work within the constraints of existing intra-domain traffic engineering operations that are geared for efficiently utilizing bandwidth resources, or meeting Quality of Service (QoS) constraints, or both. A fundamental feature of intra-domain routing, that makes MeasuRouting feasible, is that intra-domain routing is often specified for aggregate flows. MeasuRouting, can therefore, differentially route components of an aggregate flow while ensuring that the aggregate placement is compliant to original traffic engineering objectives. In this paper we present a theoretical framework for MeasuRouting. Furthermore, as proofs-of-concept, we present synthetic and practical monitoring applications to showcase the utility enhancement achieved with MeasuRouting.

We consider multihomed scalable video streaming, where videos are transmitted by a single server to multiple clients over heterogeneous access networks. The specific problem that we address is to determine which video packets to transmit over each network, in order to minimize a cost function of the expected video distortion at the clients. We present a network model and a video model that capture the network conditions and video characteristics, respectively. We develop an integer program for deterministic packet scheduling. We propose different cost functions in order to provide service differentiation and address fairness among users. We propose several suboptimal convex problems for randomized packet scheduling, and study their performance and complexity. We propose an algorithm that yields a good performance and is suitable for real-time applications. We conduct extensive trace-driven simulations to evaluate the proposed algorithms using real network conditions and scalable video streams. The simulation results show that the proposed algorithm: (i) outperforms the rate control algorithms defined in the Datagram Congestion Control Protocol (DCCP) by about 10 dB, (ii) results in video quality, of 4.33 dB and 1.84 dB higher than the two heuristics developed in [1], (iii) runs efficiently, up to six times faster than one of the heuristics, and (iv) indeed can provide service differentiation among users.

Manual generation of a book inventory is time-consuming and tedious, while deployment of barcode and radio-frequency identification (RFID) management systems is costly and affordable only to large institutions. In this paper, we design and implement a mobile book recognition system for conveniently generating an inventory of books by snapping photos of a bookshelf with a smartphone. Since smartphones are becoming ubiquitous and affordable, our inventory management solution is cost-effective and very easy to deploy. Automatic and robust book recognition is achieved in our system using a combination of spine segmentation and bag-of-features image matching. At the same time, the location of each book is inferred from the smartphone's sensor readings, including accelerometer traces, digital compass measurements, and WiFi signatures. This location information is combined with the image recognition results to construct a location-aware book inventory. We demonstrate the effectiveness of our book spine recognition and location estimation techniques in recognition experiments and in an actual mobile book recognition system.

Location-based applications have become increasingly popular on smartphones over the past years. The active use of these applications can however cause device battery drain owing to their power-intensive location-sensing operations. This paper presents an adaptive location sensing framework that significantly improves the energy efficiency of smartphones running location-based applications. The underlying design principles of the proposed framework involve substitution, suppression, piggybacking, and adaptation of applications' location-sensing requests to conserve energy. We implement these design principles on Android-based smartphones as a middleware. Our evaluation results show that the design principles reduce the usage of the power-intensive GPS (Global Positioning System) by up to 98% and improve battery life by up to 75%.

For mobile image matching applications, a mobile device captures a query image, extracts descriptive features, and transmits these features wirelessly to a server. The server recognizes the query image by comparing the extracted features to its database and returns information associated with the recognition result. For slow links, query feature compression is crucial for low-latency retrieval. Previous image retrieval systems transmit compressed feature descriptors, which is well suited for pairwise image matching. For fast retrieval from large databases, however, scalable vocabulary trees are commonly employed. In this paper, we propose a rate-efficient codec designed for tree-based retrieval. By encoding a tree histogram, our codec can achieve a more than 5times rate reduction compared to sending compressed feature descriptors. By discarding the order amongst a list of features, histogram coding requires 1.5times lower rate than sending a tree node index for every feature. A statistical analysis is performed to study how the entropy of encoded symbols varies with tree depth and the number of features.

High variability of access resources in heterogeneous wireless networks and limited computing power and battery life of mobile computing devices such as smartphones call for novel approaches to satisfy the quality-of-service requirements of emerging wireless services and applications. Towards this end, we first investigate a Markov-based stochastic scheme for modeling and estimation of bandwidth and delay on heterogeneous wireless networks. Borrowing clustering techniques from machine learning literature for intelligent state quantization, we demonstrate that the performance of the Markov model is enhanced significantly. We implement a measurement tool Zeus on smartphones and collect real-world data on 802.11g, 2.5G, and 3G wireless networks. The accuracy of the developed model is evaluated through simulation studies based on the collected data. Furthermore, a distributed rate-control scheme leveraging the predictions of our model is developed and observed to be much more efficient than a baseline additive-increase multiplicative- decrease scheme.

We investigate a novel robust flow control framework for heterogeneous network access by devices with multi-homing capabilities. Towards this end, we develop an H-infinity-optimal control formulation for allocating rates to devices on multiple access networks with heterogeneous time-varying characteristics. H-infinity analysis and design allow for the coupling between different devices to be relaxed by treating the dynamics for each device as independent of the others. Thus, the distributed end-to-end rate control scheme proposed in this work relies on minimum information and achieves fair and robust rate allocation for the devices. An efficient utilization of the access networks is established through an equilibrium analysis in the static case. We perform measurement tests to collect traces of the available bandwidth on various WLANs and Ethernet. Through simulations, our approach is compared with AIMD and LQG schemes. In addition, the efficiency, fairness, and robustness of the H-infinity-optimal rate controller developed are demonstrated via simulations using the measured real world network characteristics. Its favorable characteristics and general nature indicate applicability of this framework to a variety of networked systems for flow control.

We consider the problem of rate allocation among multiple simultaneous video streams sharing multiple heterogeneous access networks. We develop and evaluate an analytical framework for optimal rate allocation based on observed available bit rate (ABR) and round-trip time (RTT) over each access network and video distortion-rate (DR) characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the total expected distortion of all video streams. We present a distributed approximation of its solution and compare its performance against Hinfin-optimal control and two heuristic schemes based on TCP-style additive-increase-multiplicative-decrease (AIMD) principles. The various rate allocation schemes are evaluated in simulations of multiple high-definition (HD) video streams sharing multiple access networks. Our results demonstrate that, in comparison with heuristic AIMD-based schemes, both media-aware allocation and Hinfin-optimal control benefit from proactive congestion avoidance and reduce the average packet loss rate from 45% to below 2%. Improvement in average received video quality ranges between 1.5 to 10.7 dB in PSNR for various background traffic loads and video playout deadlines. Media-aware allocation further exploits its knowledge of the video DR characteristics to achieve a more balanced video quality among all streams.

We design a test methodology to analyze in detail the video quality received at each peer in a peer-to-peer (P2P) video streaming system. The metrics that we employ at each peer include video PSNR, statistical analysis of frame-freeze events, the amount of time to wait before video playback starts, nature of the data-paths established to serve the peer, protocol overhead and duplicate data received. These metrics are estimated by analyzing the packet reception times at each peer and utilizing information about the original uncompressed video as well as the encoded video. We use this framework to compare the performance of three P2P video streaming systems by deploying them on our controlled traffic-shaped network test-bed. We can emulate the same network conditions and peer behavior for testing different systems and ensure that the experiments are repeatable. These measurements highlight the differences between systems based upon their underlying implementation, overlay architecture, and choice of protocols. This measurement study helps to gauge the performance of currently available P2P video streaming systems and points out desirable performance improvements.

We evaluate the performance of a large-scale live P2P video multicast session comprising more than 120, 000 peers on the Internet. Our analysis highlights P2P video multicast characteristics such as high bandwidth requirements, high peer churn, low peer persistence in the P2P multicast system, significant variance in the media stream quality delivered to peers, relatively large channel start times, and flash crowd effects of popular video content. Our analysis also indicates that peers are widely spread across the IP address space, spanning dozens of countries and hundreds of ISPs and Internet ASes. As part of the P2P multicast evaluation several QoS measures such as fraction of stream blocks correctly received, number of consecutive stream blocks lost, and channel startup time across peers. We correlate the observed quality with the underlying network and with peer behavior, suggesting several avenues for optimization and research in P2P video multicast systems.

Automatic CD cover recognition has interesting applications for comparison shopping and music sampling. We demonstrate a real-time CD cover recognition using a cameraphone. By snapping a picture of a CD cover with her cameraphone, a user can conveniently retrieve information related to the CD. Robust image feature extraction is applied to overcome the image distortions in the query photo. To limit the amount of data transmitted over a wireless network, we compress the query image or features extracted from the query image. On the database side, fast and reliable image matching against a database of 10,000 CD covers is accomplished using a scalable vocabulary tree.

Illumination distortion due to uncontrolled lighting can severely degrade the color appearance of a photo. Frequently, the desired colors for objects in a newly taken query image are found in a previously stored database image. Then, the goal is to change the colors in the query image to match the colors in the database image. This paper presents a color restoration system that automatically retrieves a database image which matches the query image, even if the two images are taken from different viewpoints and under different illuminations. Robust features enable both accurate retrieval from the database and efficient sampling of the color differences between the query and database images. A spatially varying color mismatch model is generated, and the colors of the query image are effectively restored.

Multimedia applications for mobile devices are increasing and growing more sophisticated. Many of these applications require computationally intensive processing, such as image processing, source coding, feature extraction and feature matching. If all of this processing were performed on the mobile device, its limited battery supply would quickly deplete. However, if the request must be transmitted through a network and processed at a remote application server large delays may be incurred due to communication latency-especially if the original size of the request message is very large. In this paper, we propose the use of mid-network processing on intermediary nodes in a multistage tandem network. We refer to this as ldquowireless network-Assisted computingrdquo. Allowing for mid-network processing can alleviate some of the processing burden on the mobile device, thereby extending its lifetime. It can also reduce communication latency by reducing the amount of information transmitted along each link. Certainly ldquoleasingrdquo processing power from these nodes comes at a price-in some cases it is beneficial to lease, in others it is not. We examine the core tradeoff between battery usage, latency, and usage of processing power at mid-network nodes. We identify some interesting properties of the optimal processing schedule. Through numerical analysis we study these properties and tradeoffs.

Contemporary wireless devices integrate multiple networking technologies, such as cellular, WiMax and IEEE 802.11a/b/g, as alternative means of accessing the Internet. Efficient utilization of available bandwidth over heterogeneous access networks is important, especially for media streaming applications with high data rates and stringent delay requirements. In this work we consider the problem of rate allocation among multiple video streaming sessions sharing multiple access networks. We develop and evaluate an analytical framework for optimal video rate allocation, based on observed available bit rate (ABR) and round trip time (RTT) over each access network, as well as the video distortion-rate (DR) characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the sum of expected distortion of all video streams. We then present a distributed approximation of the optimization, which enables autonomous rate allocation at each device in a media- and network-aware fashion. Performance of the proposed allocation scheme is compared against robust rate control based on H∞ optimal control and two heuristic schemes employing TCP style additive-increase-multiplicative-decrease (AIMD) principles. Wesimulate in NS-2 [1] simultaneous streaming of multiple high-definition(HD) video streams over multiple access networks, using ABR and RTT traces collected on Ethernet, IEEE 802.11g, and IEEE 802.11b networks deployed in a corporate environment. In comparison with heuristic AIMD-based schemes, rate allocation from both the media-aware convex optimization scheme and H∞ optimal control benefit from proactive avoidance of network congestion, and can reduce the average packet loss ratio from 27% to below 2%, while improving the average received video quality by 3.3 - 4.5 dB in PSNR.

A global-scale low cost outdoor Internet access infrastructure is finally attainable. Emerging projects are leveraging the proliferation of private Wi-Fi networks to build a global-scale ubiquitous access infrastructure from autonomous, independently owned Internet connections at homes and other private properties. To ensure the trace ability and accountability required by the broadband ISPs and private owners of these Wi-Fi networks, reliable authentication and authorization are needed. This paper describes authentication on the edge, a localized and distributed authentication method for open Wi-Fi networks. Three main ideas are used to adapt to the variability and unreliability of these networks: the use of certificate-based authentication, the distribution of a segmented certificate revocation list to all entities and the self organization of access points into a social lookup network. These methods achieve the scalability needed for the overwhelming size and volume of a global network and increase resiliency against temporary failures in the infrastructure.

We consider a scenario where devices with multiple networking capabilities access networks with heterogeneous characteristics. In such a setting, we address the problem of efficient utilization of multiple access networks (wireless and/or wireline) by devices via optimal assignment of traffic flows with given utilities to different networks. We develop and analyze a device middleware functionality that monitors network characteristics and employs a Markov Decision Process (MDP) based control scheme that in conjunction with stochastic characterization of the available bit rate and delay of the networks generates an optimal policy for allocation of flows to different networks. The optimal policy maximizes, under available bit rate and delay constraints on the access networks, a discounted reward which is a function of the flow utilities. The flow assignment policy is periodically updated and is consulted by the flows to dynamically perform network selection during their lifetimes. We perform measurement tests to collect traces of available bit rate and delay characteristics on Ethernet and WLAN networks on a work day in a corporate work environment. We implement our flow assignment framework in ns-2 and simulate the system performance for a set of elastic video-like flows using the collected traces. We demonstrate that the MDP based flow assignment policy leads to significant enhancement in the QoS provisioning (lower packet delays and packet loss rates) for the flows, as compared to policies which do not perform dynamic flow assignment but statically allocate flows to different networks using heuristics like average available bit rate on the networks.

Almost a decade long research on the performance of TCP in wireless networks has resulted in many proposals and solutions to the problem of TCP throughput degradation. Several of these measures, however, have their share of drawbacks. With the continuing emergence of wireless technologies ever since the work on TCP performance over wireless began, smart link-layer mechanisms like adaptive modulation and coding, power control, and incremental redundancy have been designed and deployed. In this work, we outline a cross-layer optimization framework based on the congestion control dynamics of a bulk-transfer TCP flow and demonstrate its application to networks which offer link-layer adaptive measures. We begin by observing that the TCP's congestion window dynamics are comprised of certain recurring patterns which we term as cycles. We then overlay a TCP throughput optimization methodology that selects link-layer transmission modes (e.g. modulation scheme, coding rate, transmission power, or a combination thereof) in accordance with TCP dynamics and wireless channel conditions. We provide insights into the working of the optimization procedure which protects TCP segments against losses on the wireless channel when the TCP congestion window size (in bytes) is below the bandwidth-delay product of the network. The protection against wireless channel losses is rendered by the link-layer by employing robust modulation and coding schemes, high transmission power, etc. We show that TCP dynamics aware link adaptation measures lead to substantial enhancement of TCP throughput in EGPRS and IEEE 802.11a networks

Although the problem of mal-performance of TCP in the wireless scenario has been extensively addressed, an integrated solution has remained undeciphered. Conventionally, measures have been suggested to either adapt or shield TCP from non-congestion losses over the wireless channel. On the other hand, the issues of wireless channel compensation and error control for achieving a better quality channel, have been extensively studied too. Recent efforts to investigate joint approaches do not model TCP dynamics over wireless channel, but study the impact of power control and link layer error correction mechanisms in relation to standard TCP throughput models. In this work, we propose and evaluate transmission power adaptation as an integrated solution to mal-performance of TCP in wireless networks. The congestion control dynamics of bulk-transfer TCP flow are modeled for the wireless channel, and an optimization framework is delineated. Based on dynamic programming solutions and low-complexity heuristics, adaptive power control measures are suggested and analyzed for their merit. Via simulations, we demonstrate that suitable power adaptation can lead to a considerable improvement in TCP throughput for slow and fast fading channels.

In this work we assess the performance of a WLAN in different vehicular traffic and mobility scenarios. Furthermore, we investigate ad-hoc routing for a vehicular network. We deploy a topology consisting of vehicles bearing laptop computers equipped with IEEE 802.11b compliant equipment. Test scenarios for WLAN assessment are varied by conducting experiments under different vehicular mobility, peer-distance and driving environment conditions. The network throughput and the quality of the channel are observed to degrade with increasingly stressful communication scenarios. Based on empirical observations, we present results that can facilitate the development of efficient applications for inter-vehicular communication. To address multihop routing, we investigate and demonstrate the application of link connectivity assessment to efficient ad-hoc routing. A framework for enhancements is delineated and incorporated in the implementation of the optimized link state routing (OLSR) protocol. We demonstrate that the link quality assessment based enhancements improve the performance of OLSR.

Owing to the absence of any static support structure, ad-hoc networks are prone to link failures. The 'shortest path seeking' routing protocols may not lead to stable routes. The consequent route failures that ensue, lead to the degradation of system throughput. This paper suggests a routing protocol wherein the route selection is done on the basis of an intelligent residual lifetime assessment of the candidate routes. Schemes for performance enhancement with TCP and non-TCP traffic in ad-hoc networks are proposed. The protocol is backed by simulations in ns that show excellent adaptation to increasing network mobility. We have also introduced new route cache management and power aware data transmission schemes.