Topology-Aware Multiple Routing Configurations for Fault Tolerant Networking

Real-time services require reliable and fault tolerant communication networks to support their stringent Quality of Service requirements. Multi Topology Routing based IP Fast Re-route (MT-IPFRR) technologies provide seamless forwarding of IP packets during network failures by constructing virtual topologies (VTs) to re-route the disrupted traffic. Multiple Routing Configurations (MRC) is a widely studied MT-IPFRR technique. In this paper, we propose two heuristics, namely mMRC-1 and mMRC-2, to reduce the number of VTs required by the MRC to provide full coverage for single link/node failures, and hence, to decrease its operational complexity. Both heuristics are designed to construct more robust VTs against network partitioning by taking their topological characteristics into consideration. We perform extensive experiments on 3200 topologies with diverse structural properties using our automated topology generation and analysis tool. Numerical results show that the amount of reductions in VT requirements get higher up to 31.84 %, as the networks tend to have more hub nodes whose degree is much higher than the rest of the network.     

Low computation and low latency algorithms for distributed sensor network initialization

In this paper, we show how an underlying system’s state vector distribution can be determined in a distributed heterogeneous sensor network with reduced subspace observability at the individual nodes. The presented algorithm can generate the initial state vector distribution for networks with a variety of sensor types as long as the collective set of measurements from all the sensors provides full state observability. Hence the network, as a whole, can be capable of observing the target state vector even if the individual nodes are not capable of observing it locally. Initialization is accomplished through a novel distributed implementation of the particle filter that involves serial particle proposal and weighting strategies that can be accomplished without sharing raw data between individual nodes. If multiple events of interest occur, their individual states can be initialized simultaneously without requiring explicit data association across nodes. The resulting distributions can be used to initialize a variety of distributed joint tracking algorithms. We present two variants of our initialization algorithm: a low complexity implementation and a low latency implementation. To demonstrate the effectiveness of our algorithms we provide simulation results for initializing the states of multiple maneuvering targets in smart sensor networks consisting of acoustic and radar sensors. Prepared through collaborative participation in the Advanced Sensors Consortium sponsored by the US Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-02-0008.     

Matrix Recipes for Hard Thresholding Methods

In this paper, we present and analyze a new set of low-rank recovery algorithms for linear inverse problems within the class of hard thresholding methods. We provide strategies on how to set up these algorithms via basic ingredients for different configurations to achieve complexity vs. accuracy tradeoffs. Moreover, we study acceleration schemes via memory-based techniques and randomized, ?-approximate matrix projections to decrease the computational costs in the recovery process. For most of the configurations, we present theoretical analysis that guarantees convergence under mild problem conditions. Simulation results demonstrate notable performance improvements as compared to state-of-the-art algorithms both in terms of reconstruction accuracy and computational complexity.     

General direction-of-arrival tracking with acoustic nodes

Traditionally, in target tracking, much emphasis is put on the motion model that realistically represents the target's movements. We first present the classical constant velocity model and then introduce a new model that incorporates an acceleration component along the heading direction of the target. We also show that the target motion parameters can be considered part of a more general feature set for target tracking. This is exemplified by showing that target frequencies, which may be unrelated to the target motion, can also be used to improve the tracking performance. In order to include the frequency variable, a new array steering vector is presented for the direction-of-arrival (DOA) estimation problems. The independent partition particle filter (IPPF) is used to compare the performances of the two motion models by tracking multiple maneuvering targets using the acoustic sensor outputs directly. The treatment is quite general since IPPF allows general type of noise models as opposed to Gaussianity imposed by Kalman type of formulations. It is shown that by incorporating the acceleration into the state vector, the tracking performance can be improved in certain cases as expected. Then, we demonstrate a case in which the frequency variable improves the tracking and classification performance for targets with close DOA tracks.     

Human MLH1 deficiency predisposes to hematological malignancy and neurofibromatosis type 1

Heterozygous germ-line mutations in the DNA mismatch repair genes lead to hereditary nonpolyposis colorectal cancer. The disease susceptibility of individuals who constitutionally lack both wild-type alleles is unknown. We have identified three offspring in a hereditary nonpolyposis colorectal cancer family who developed hematological malignancy at a very early age, and at least two of them displayed signs of neurofibromatosis type 1 (NF1). DNA sequence analysis and allele-specific amplification in two siblings revealed a homozygous MLH1 mutation (C676T-->Arg226Stop). Thus, a homozygous germ-line MLH1 mutation and consequent mismatch repair deficiency results in a mutator phenotype characterized by leukemia and/or lymphoma associated with neurofibromatosis type 1.     

Target Tracking Using a Joint Acoustic Video System

In this paper, a multitarget tracking system for collocated video and acoustic sensors is presented. We formulate the tracking problem using a particle filter based on a state-space approach. We first discuss the acoustic state-space formulation whose observations use a sliding window of direction-of-arrival estimates. We then present the video state space that tracks a target's position on the image plane based on online adaptive appearance models. For the joint operation of the filter, we combine the state vectors of the individual modalities and also introduce a time-delay variable to handle the acoustic-video data synchronization issue, caused by acoustic propagation delays. A novel particle filter proposal strategy for joint state-space tracking is introduced, which places the random support of the joint filter where the final posterior is likely to lie. By using the Kullback-Leibler divergence measure, it is shown that the joint operation of the filter decreases the worst case divergence of the individual modalities. The resulting joint tracking filter is quite robust against video and acoustic occlusions due to our proposal strategy. Computer simulations are presented with synthetic and field data to demonstrate the filter's performance     

Enhancing Multiple Routing Configurations through systematic analysis of topological characteristics

Previous studies show that topological characteristics in IP networks significantly influence the performance of networking algorithms, which therefore should be systematically analyzed using diverse topologies prior to their deployments. In this paper, we present an elaborate topological dependency analysis of a multi‐topology routing‐based IP fast re‐route technology, namely, Multiple Routing Configurations (MRC), using a large topology pool with diverse properties. Through an extensive analysis using our automated topological analysis tool, we discover a significant correlation between the performance of MRC and topological characteristics. MRC needs to construct a higher number of virtual topologies to provide full alternate path coverage if a network topology tends to have more hub nodes, whose degree is much higher than the rest of the network. Inspired by our topological analysis results, we propose a new heuristic algorithm enhancing MRC. Numerical experiments demonstrate that our heuristic significantly improves the performance of MRC confirming the effectiveness of the systematic analysis of topological characteristics. Copyright © 2016 John Wiley & Sons, Ltd.     

Decentralized State Initialization with Delay Compensation for Multi-modal Sensor Networks

Decentralized processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in smart sensor networks since they provide the ability to scale, reduce vulnerability, reduce communication, and share processing responsibilities among individual nodes. Sharing the processing responsibilities allows parallel processing of raw data at the individual nodes. However, this introduces other difficulties in multi-modal smart sensor networks, such as non-observability of the targets’ states at any individual node and various delays such as varying processing delays, communication delays and signal propagation delays for the different modalities. In this paper, we provide a novel algorithm to determine the initial probability distribution of multiple targets’ states in a decentralized manner. The targets’ state vectors consist of the targets’ positions and velocities on the 2D plane. Our approach can determine the state vector distribution even if the individual sensors alone are not capable of observing it. Our approach can also compensate for varying delays among the assorted modalities. The resulting distribution can be used to initialize various tracking algorithms. Our approach is based on Monte Carlo methods, where the state distributions are represented by a weighted set of discrete state realizations. A robust weighting strategy is formulated to account for missed detections, clutter and estimation delays. To demonstrate the effectiveness of the algorithm, we simulate a network with direction-of-arrival nodes and range-Doppler nodes.