Estimating network proximity and latency
Network proximity and latency estimation is an important component in discovering and locating services and applications. With the growing number of services and service providers in the large-scale Internet, accurately estimating network proximity/latency with minimal probing overhead becomes essential for scalable deployment. Although there exist a number of network distance estimation schemes, they either rely on extensive infrastructure support, require the IP address of the potential targets, falsely cluster distant nodes, or perform poorly with even few measurement errors. We propose Netvigator, a scalable network proximity and latency estimation tool that uses information obtained from probing a small number of landmark nodes and intermediate routers (termed milestones) that are discovered en route to the landmarks, to identify the closest nodes. With very little additional probing overhead, Netvigator uses distance information to the milestones to accurately locate the closest nodes. We developed a Netvigator prototype and report our performance evaluation on PlanetLab and in the intranet of a large enterprise. Netvigator is a running service on PlanetLab as a part of HP Labs' S3 (Scalable Sensing Service).

title: On-Demand Multicast Routing Protocol in Multihop Wireless Mobile Networks
abstract: An ad hoc network is a dynamically reconfigurable wireless network with no fixed infrastructure or central administration. Each host is mobile and must act as a router. Routing and multicasting protocols in ad hoc networks are faced with the challenge of delivering data to destinations through multihop routes in the presence of node movements and topology changes. This paper presents the On-Demand Multicast Routing Protocol (ODMRP) for wireless mobile ad hoc networks. ODMRP is a mesh-based, rather than a conventional tree-based, multicast scheme and uses a forwarding group concept; only a subset of nodes forwards the multicast packets via scoped flooding. It applies on-demand procedures to dynamically build routes and maintain multicast group membership. ODMRP is well suited for ad hoc wireless networks with mobile hosts where bandwidth is limited, topology changes frequently, and power is constrained. We evaluate ODMRP performance with other multicast protocols proposed for ad hoc networks via extensive and detailed simulation.

title: An interactive video delivery and caching system using video summarization
abstract: With the advance of high-speed network technologies, the availability and popularity of streaming media content over the Internet has grown rapidly in recent years. The delivery and caching of streaming media must be handled in a different fashion than that of traditional non-streaming objects such as HTML or image files, because of its distinct characteristics and user viewing patterns. We propose a novel scheme that provides users with the video summary (a number of key-frame images) before they download the file, and options for them to select the starting playback position. We introduce the content analysis service to achieve these functionalities. The video content analysis performs shot boundary detection, key-frame selection, and face detection and tracking. The results of the processing are a segmented video sequence and an XML-based meta-data describing the video content. We also design a caching system that utilizes our video abstraction and summarization technique. Our integrated video delivery and caching system combines content-aware segmentation, prefix caching, prefetching, and cooperative caching. We describe how our scheme can be applied in three proposed caching architectures.

title: Distributed querying of Internet distance information

title: Improving aggregated channel performance through decentralized channel monitoring
abstract: Aggregating low-speed WAN links into a higher-speed logical link promises to improve data-transfer rates to collaborating communities of wireless mobile multihomed devices. Such bandwidth aggregation systems must adapt to link dynamics as the number of links and the channel conditions vary with time due to mobility, power dissipation, and channel interference. A monitoring architecture that accurately measures the link dynamics and promptly feeds this information to the system is vital to realize significant bandwidth aggregation performance gains. In this paper we present various architectural design alternatives for such a monitoring system, and evaluate them using both analysis and simulation. We show that a properly-designed monitoring system can accurately measure and quickly respond to changes in communication link performance while minimizing the control overhead.

title: Caching strategies in transcoding-enabled proxy systems for streaming media distribution networks
abstract: With the wide availability of high-speed network access, we are experiencing high quality streaming media delivery over the Internet. The emergence of ubiquitous computing enables mobile users to access the Internet with their laptops, PDAs, or even cell phones. When nomadic users connect to the network via wireless links or phone lines, high quality video transfer can be problematic due to long delay or size mismatch between the application display and the screen. Our proposed solution to this problem is to enable network proxies with the transcoding capability, and hence provide different, appropriate video quality to different network environment. The proxies in our transcoding-enabled caching (TeC) system perform transcoding as well as caching for efficient rich media delivery to heterogeneous network users. This design choice allows us to perform content adaptation at the network edges. We propose three different TeC caching strategies. We describe each algorithm and discuss its merits and shortcomings. We also study how the user access pattern affects the performance of TeC caching algorithms and compare them with other approaches. We evaluate TeC performance by conducting two types of simulation. Our first experiment uses synthesized traces while the other uses real traces derived from an enterprise media server logs. The results indicate that compared with the traditional network caches, with marginal transcoding load, TeC improves the cache effectiveness, decreases the user-perceived latency, and reduces the traffic between the proxy and the content origin server.

title: Finding the most unusual time series subsequence: algorithms and applications
abstract: In this work we introduce the new problem of finding time seriesdiscords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While discords have many uses for data mining, they are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence) unlike most anomaly detection algorithms that typically require many parameters. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is three to four orders of magnitude faster than brute force, while guaranteed to produce identical results. We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms, space telemetry, respiration physiology, anthropological and video datasets.

title: Distributed and energy-efficient target localization and tracking in wireless sensor networks
abstract: In this paper, we propose and evaluate a distributed, energy-efficient, light-weight framework for target localization and tracking in wireless sensor networks. Since radio communication is the most energy-consuming operation, this framework aims to reduce the number of messages and the number of message collisions, while providing refined accuracy. The key element of the framework is a novel localization algorithm, called Ratiometric Vector Iteration (RVI). RVI is based on distance ratio estimates rather than absolute distance estimates which are often impossible to calculate. By iteratively updating the estimated location using the distance ratio, RVI localizes the target accurately with only three sensors' participation. After localization, the location of the target is reported to the subscriber. If the target is stationary or moves around within a small area, it is wasteful to report (almost) the same location estimates repeatedly. We, therefore, propose to dynamically adjust a reporting frequency considering the target's movement so that we can reduce the number of report messages while maintaining tracking quality. Extensive simulation results show that the proposed framework combining RVI and the movement-adaptive report scheduling algorithm reduces the localization error and total number of the transmitted messages up to half of those of the existing approaches.

title: An Efficient Neighbor Knowledge Based Broadcasting for Mobile Ad Hoc Networks
abstract: Although a flooding is the most commonly used method for one-to-all communications, it often results in huge traffic in mobile ad hoc networks. Several optimization schemes have been proposed to alleviate this problem. However, none of them offers a complete solution and has their own limitations, such as, lack of reliability, high latency or excessive control packet overhead. In this paper, we propose the FONIAH (Flooding based on One- hop Neighbor Information and Adaptive Holding) scheme to overcome these limitations.

title: Noise-robust automatic speech recognition using mainlobe-resilient time-frequency quantile-based noise estimation
abstract: In standard speech recognition systems in which training data are clean speech, the presence of background noise in received signal can severely deteriorate the recognition performance. This paper presents a simple noise-robust speech recognition system based on a modified noise spectral estimation method called mainlobe-resilient time-frequency quantile-based noise estimation (M-R T-F QBNE), which focuses on the mainlobes at harmonic frequencies. We estimate the global signal-to-noise ratio (SNR) and select a recognition model, which is best matched to the SNR operating range. Experimental results show that the recognition accuracy of the proposed recognition system is higher than that of the AURORA2 clean training baseline by 23%. Compared to multicondition training, the proposed method achieves comparable recognition accuracy.

title: Deletion measures for generalized linear mixed effects models
abstract: Generalized linear mixed models (GLMMs) have wide applications in practice. Similar to other data analyses, the identification of influential observations that may be potential outliers is an important step beyond estimation in GLMMs. Since the pioneering work of Cook in 1977, deletion measures have been applied to many statistical models for identifying influential observations. However, as this well-known approach is based on the observed-data likelihood, it is very difficult to apply it to developing diagnostic measures for GLMMs due to the complexity of the observed-data likelihood that involves multidimensional integrals. The objective of this article is to develop diagnostic measures for identifying influential observations. Deletion measures are developed on the basis of the conditional expectation of the complete-data log-likelihood at the E-step of a stochastic approximation Markov chain Monte Carlo algorithm. Making use of by-products of the estimation to compute building blocks of the proposed diagnostic measures and activating appropriate approximations, the proposed methods require little additional computation. The performance of the methods is illustrated by an artificial example, a real example, and some simulation studies.