Parameswaran Ramanathan

What do packet dispersion techniques measure

The packet pair technique estimates the capacity of a path (bottleneck bandwidth) from the dispersion (spacing) experienced by two back-to-back packets. We demonstrate that the dispersion of packet pairs in loaded paths follows a multimodal distribution, and discuss the queueing effects that cause the multiple modes. We show that the path capacity is often not the global mode, and so it cannot be estimated using standard statistical procedures. The effect of the size of the probing packets is also investigated, showing that the conventional wisdom of using maximum sized packet pairs is not optimal. We then study the dispersion of long packet trains. Increasing the length of the packet train reduces the measurement variance, but the estimates converge to a value, referred to as the asymptotic dispersion rate (ADR), that is lower than the capacity. We derive the effect of the cross traffic in the dispersion of long packet trains, showing that the ADR is not the available bandwidth in the path, as was assumed in previous work. Putting all the pieces together, we present a capacity estimation methodology that has been implemented in a tool called pathrate.

Distributed target classification and tracking in sensor networks

The highly distributed infrastructure provided by sensor networks supports fundamentally new ways of designing surveillance systems. In this paper, we discuss sensor networks for target classification and tracking. Our formulation is anchored on location-aware data routing to conserve system resources, such as energy and bandwidth. Distributed classification algorithms exploit signals from multiple nodes in several modalities and rely on prior statistical information about target classes. Associating data to tracks becomes simpler in a distributed environment, at the cost of global consistency. It may be possible to filter clutter from the system by embedding higher level reasoning in the distributed system. Results and insights from a recent field test at 29 Palms Marine Training Center are provided to highlight challenges in sensor networks.

A dynamic priority assignment technique for streams with (m, k)-firm deadlines

The problem of scheduling multiple streams of real-time customers, is addressed in this paper. The paper first introduces the notion of (m, k)-firm deadlines to better characterize the timing constraints of real-time streams. More specifically, a stream is said to have (m, k)-firm deadlines if at least m out of any k consecutive customers must meet their deadlines. A stream with (m, k)-firm deadlines experiences a dynamic failure if fewer than m out of any k consecutive customers meet their deadlines. The paper then proposes a priority-based policy for scheduling N such streams on a single server to reduce the probability of dynamic failure. The basic idea is to assign higher priorities to customers from streams that are closer to a dynamic failure so as to improve their chances of meeting their deadlines. The paper proposes a heuristic for assigning these priorities. The effectiveness of this approach is evaluated through simulation under various customer arrival and service patterns. The scheme is compared to a conventional scheme where all customers are serviced at the same priority level and to an imprecise computation model approach. The evaluation shows that substantial reductions in the probability of dynamic failure are achieved when the proposed policy is used.

Sensor deployment strategy for target detection

In order to monitor a region for traffic traversal, sensors can be deployed to perform collaborative target detection. Such a sensor network achieves a certain level of detection performance with an associated cost of deployment. This paper addresses this problem by proposing path exposure as a measure of the goodness of a deployment and presents an approach for sequential deployment in steps. It illustrates that the cost of deployment can be minimized to achieve the desired detection performance by appropriately choosing the number of sensors deployed in each step.

Packet-dispersion techniques and a capacity-estimation methodology

The packet-pair technique aims to estimate the capacity of a path (bottleneck bandwidth) from the dispersion of two equal-sized probing packets sent back to back. It has been also argued that the dispersion of longer packet bursts (packet trains) can estimate the available bandwidth of a path. This paper examines such packet-pair and packet-train dispersion techniques in depth. We first demonstrate that, in general, packet-pair bandwidth measurements follow a multimodal distribution and explain the causes of multiple local modes. The path capacity is a local mode, often different than the global mode of this distribution. We illustrate the effects of network load, cross-traffic packet-size variability, and probing packet size on the bandwidth distribution of packet pairs. We then switch to the dispersion of long packet trains. The mean of the packet-train dispersion distribution corresponds to a bandwidth metric that we refer to as average dispersion rate (ADR). We show that the ADR is a lower bound of the capacity and an upper bound of the available bandwidth of a path. Putting all of the pieces together, we present a capacity-estimation methodology that has been implemented in a tool called pathrate. We report on our experiences with pathrate after having measured hundreds of Internet paths over the last three years.

Adapting packet fair queueing algorithms to wireless networks

Parames~varan Ramana.tllan Pratbinla t\graIval Dept. of Elect. & Comp. Engr. I[)ter]~et. ArcIl. Research Lab University of Wisconsin Bellcore, hIorristotvn Nladison, WI 53706 Ne\v .Jersey 07960. parmesll!@ece.\visc.edu pagra~val(~bellcore. com Bit errors are fairly common during transmission in a wireless network. As a result, a straight-forwarcl application of existing packet fair queueing (PFQ) algorithms from wireIine to wireless networks results in an ine~cient use of ~he Kmited wireless bandwidth. In this paper, we propose a simple approach for adapting the existing PFQ algorithms for the wire]ine networks to provide the same kind of long-term fairness guarantees while making efficient use of the wireless bandwidth. In the proposed approach, long-term fairness guarantees are provided by supplementing the bandwidth given to sessions which have not received satisfactory service in the short -term due to poor quahty of their wireless channel. To efficiently keep track of the amount of supplemental bandwidth for each session, the paper introduces the concept of a long-term fairness server. This concept. also allows one to easily integrate the proposed approach with any of the existing PFQ algorithms.

Fault tolerance in collaborative sensor networks for target detection

Collaboration in sensor networks must be fault-tolerant due to the harsh environmental conditions in which such networks can be deployed. We focus on finding algorithms for collaborative target detection that are efficient in terms of communication cost, precision, accuracy, and number of faulty sensors tolerable in the network. Two algorithms, namely, value fusion and decision fusion, are identified first. When comparing their performance and communication overhead, decision fusion is found to become superior to value fusion as the ratio of faulty sensors to fault free sensors increases. As robust data fusion requires agreement among nodes in the network, an analysis of fully distributed and hierarchical agreement is also presented. The impact of hierarchical agreement on communication cost and system failure probability is evaluated and a method for determining the number of tolerable faults is identified.

Real-time computing: a new discipline of computer science and engineering

This paper surveys the state of the art in real-time computing. It introduces basic concepts and identifies key issues in the design of real-time systems. Solutions proposed in literature for tackling these issues are also briefly discussed. >

Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network

Two novel distributed particle filters with Gaussian mixer approximation are proposed to localize and track multiple moving targets in a wireless sensor network. The distributed particle filters run on a set of uncorrelated sensor cliques that are dynamically organized based on moving target trajectories. These two algorithms differ in how the distributive computing is performed. In the first algorithm, partial results are updated at each sensor clique sequentially based on partial results forwarded from a neighboring clique and local observations. In the second algorithm, all individual cliques compute partial estimates based only on local observations in parallel, and forward their estimates to a fusion center to obtain final output. In order to conserve bandwidth and power, the local sufficient statistics (belief) is approximated by a low dimensional Gaussian mixture model (GMM) before propagating among sensor cliques. We further prove that the posterior distribution estimated by distributed particle filter convergence almost surely to the posterior distribution estimated from a centralized Bayesian formula. Moreover, a data-adaptive application layer communication protocol is proposed to facilitate sensor self-organization and collaboration. Simulation results show that the proposed DPF with GMM approximation algorithms provide robust localization and tracking performance at much reduced communication overhead.

Dynamic resource allocation schemes during handoff for mobile multimedia wireless networks

User mobility management is one of the important components of mobile multimedia systems. In a cell-based network, a mobile should be able to seamlessly obtain transmission resources after handoff to a new base station. This is essential for both service continuity and quality of service assurance. In this paper, we present strategies for accommodating continuous service to mobile users through estimating resource requirements of potential handoff connections. A diverse mix of heterogeneous traffic with diverse resource requirements is considered. The investigate static and dynamic resource allocation schemes. The dynamic scheme probabilistically estimates the potential number of connections that will be handed off from neighboring cells, for each class of traffic. The performance of these strategies in terms of connection blocking probabilities for handoff and local new connection requests are evaluated. The performance is also compared to a scheme previously proposed by Yu and Leung (see IEEE J. Select. Areas Commun., vol.15, p.1208-25, 1997). The results indicate that using dynamic estimation and allocation, we can significantly reduce the dropping probability for handoff connections.