Dynamic Spectrum Access Platform

Ivan Seskar and D. Raychaudhuri, received a 3-year $288,000 grant 
from NSF for the project "WISER Dynamic Spectrum Access Platform and Infrastructure". This is in collaboration with Prof. Dirk Grunwald of U Colorado, NSF CRI (Computing Research Infrastructure) to further develop the WINLAB/Colorado cognitive radio platform for research community use. The total project cost is $670,000.

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Exploring Cloud Paradigm and Practices for Science and Engineering

Manish Parashar, Ivan Rodero, Javier Diaz Montes received a two-year NSF award of $299,984 for the project "Exploring Cloud Paradigm and Practices for Science and Engineering."

The project abstract is given below.

Clouds abstractions and infrastructure are rapidly becoming part of the advanced research cyber-infrastructure (ACI) providing viable platforms for scientific exploration and discovery. As a result, it is important to understand how emerging data and compute intensive application workflows can effectively utilize a hybrid ACI integrating Cloud abstractions and services, and how such a hybrid ACI can enable new paradigms and practices in science and engineering. This EAGER explores innovative science and engineering application

formulations that are enabled by a hybrid federated ACI that includes Clouds and HPC resources, as well as programming and middleware support for these new application formulations. Specifically, we focus on three key research thrusts: (1) application formulation; (2) programming models, abstractions and systems; and (3) middleware stacks and management services, and explore two applications use cases - (i) an oil reservoir modeling application based on an Ensemble Kalman Filter (EnKF), and (ii) molecular dynamics simulations using asynchronous replica exchange. In each of these use cases we explore how the capabilities provided by resources and services in a federated ACI can be leveraged to optimize metrics such as time-to-science, cost-to-science and/or energy-to-science.

Cloud services are integral to the NSF ACI vision. Clouds are also rapidly becoming an integral part of the ACI available to science and engineering applications, and provide complementary capabilities that can have a significant impact on a range of applications. As a result, this research can have a significant impact on a diverse set of application domains by identifying new paradigms and practices that can make effective use of a hybrid ACI to accelerate science. Furthermore, the results of this research will provide resource providers information about how to best meet the needs of science and engineering applications and how current ACI can achieve broader accessibility and higher efficiencies and productivity. The development of human resources, including the training of students, researchers and software professions, as well as outreach to minorities and underrepresented group, is integral to all aspects of this effort.

Exploiting Sparsity for Interference Management in Broadband Communications

Prof. Waheed Bajwa received funding from the Qatar National Research Foundation, for the project::

"Exploiting Sparsity for Interference Management in Broadband Communications: Theory, Applications, and Testbeds"

This is a 3-year, approx. $1 million grant with UT-Dallas, and Qatar University (http://www.qnrf.org/newsroom/press_releases/detail.php?ID=3366) (pending award paperwork). The acceptance rate for this cycle of awards was 20% (137 out of 710 proposals recommended).

This proposal is expected to fund one graduate student each at Rutgers, UT-Dallas, and QU and also a joint post-doc with residence in QU.

Details of the project are as follows:

Description of the proposal: The main objectives of this proposal are to: i) develop a new unified signal processing framework for interference mitigation in broadband communication systems based on identifying and exploiting sparsity ii) extend the theoretical guarantees in the field of compressive sensing (CS) to encompass the mathematical structure arising in the proposed framework and develop auto-tunable sparse recovery algorithms based on a frame-theoretic understanding of CS theory, and iii) demonstrate on experimental testbeds the value of the proposed framework in enhancing the performance and reducing the complexity of broadband transceivers.

Our proposed approach enjoys many advantages including: i) Computational efficiency due to its focus on low-complexity solutions of the interference mitigation problem; ii) no a priori assumptions are made on the interference probability density function, second-order statistics, frequency support, or power level. Instead, we make only a mild, but highly realistic, assumption on sparsity of the interference signals; and iii) The proposed sparsity-aware approach is applicable to a wide range of interference sources (e.g. radio frequency interference, impulse noise, intersymbol interference, crosstalk, etc..) and a number of applications including digital subscriber lines, wireless local area networks, power line communications, and millimeter-wave beamforming.

Prof. Gruteser wins grant to develop a Dedicated Short Range Communications (DSRC) Vehicle-to-Vehicle (V2V) Simulator

Prof. Gruteser has been awarded a contract to develop a Dedicated Short Range Communications (DSRC) Vehicle-to-Vehicle (V2V) Scalability Simulator for the Crash Avoidance Metrics Partnership Vehicle Safety Communications 3 consortium, which is comprised of many of the world's major car makers, under USDOT's connected vehicle technology research program. The project will use field test data from hundreds of DSRC equipped vehicles to develop and calibrate simulation models so that the simulator can attempt to accurately predict V2V communication performance in very dense, interference-limited scenarios. Ultimately, DSRC radio technology is expected to lead to the next generation of car safety and efficiency technologies.

Controlling Teams of Autonomous Mobile Beamformers

Prof. Athina Petropulu, in collaboration with Prof. Michael M. Zavlanos of Duke University, received support from NSF NeTS for the project "Controlling Teams of Autonomous Mobile Beamformers."

The goal of this research is to develop a new framework to control teams of mobile robots, cooperating in a beamforming fashion, to transmit information between multiple source-destination pairs, while meeting quality-of-service constraints and consuming minimum power. The approach of this project ensures robust communications and longevity in challenging environments, arising during the transmission of high-rate data, such as video or images, or in environments where there is no line-of-sight. It also allows significant performance gains compared to static systems that do not consider mobility.

The intellectual merit of this research lies in the development of a cyber-physical system of mobile beamformers, where the physical space of robot trajectories and velocities constitutes an input to the cyber space of wireless communications, and vice versa.

Integration of the resulting discrete and continuous dynamics and different time scales requires the synthesis of new theoretical results drawing from control theory, wireless networking, distributed optimization, and hybrid control. This cyber-physical system combines the following interrelated objectives: Distributed control of mobile beamformers; Node selection, grouping and motion scheduling; Rich models of the communication space; Platform deployment and validation.

Successful completion of this research will provide these necessary components in facilitating the design of mobile autonomous systems and fostering their adoption. Wide availability of such systems can have a significant societal impact on, e.g., search, rescue and recovery operations, environmental monitoring for homeland security, or surveillance and reconnaissance missions.

Big Bandwidth: Finding Anomalous Needles in the Spectrum Haystack

Professor Wade Trappe and Larry Greenstein have been awarded an NSF grant "EARS: Collaborative Research: Big Bandwidth: Finding Anomalous Needles in the Spectrum Haystack" that will explore the problem of scanning large amounts of spectrum in order to detect anomalous usage of that spectrum. The project involves a collaboration between Rutgers University and Princeton University. The Rutgers component of the project is $300K.

An Integrated Middleware Framework to Enable Extreme Collaborative Science

Dr. Shantenu Jha received funding from the Department of Energy's Office of Advanced Scientific Computing Research (ASCR) for his project "An Integrated Middleware Framework to Enable Extreme Collaborative Science". Dr. Jha is the PI and there are two collaborators, Drs. D. S. Katz from the Univ. of Chicago and J. Weissman from the Univ. of Minnesota.

This project aims to bridge the gap between application requirements and diverse and heterogeneous platforms, by developing a middleware framework that can support the needs of tools and services in support of distributed scientific collaborative applications at extreme scales. We consider a variety of distributed

applications, including those that operate on rich data pipelines in a distributed collaborative environment including: data generation and capture, data preprocessing, data analysis, and data storage and delivery. For these applications, tools and services are needed at all levels of this pipeline to enable data discovery, data transmission and streaming, data placement and storage, resource discovery, computation scheduling, and co-scheduling. Other types of applications share most of these needs. A framework-based middleware provides an integrated way to address the many co-dependent issues in extreme-scale environments such as the emergence of disparate resource platforms and network capabilities, the inherent distribution of compute and data, and multiple-levels of application and run-time decision making.

We propose a middleware framework that provides powerful abstractions for distributed computational and storage resources, and containers for computational tasks and distributed data. This project will address fundamental research challenges required to realize these abstractions, including techniques to enable resilience and performance for both data and computation. Our framework will enable a varied set of tools to be more easily constructed.

 

High-Dimensional Linear Models? Bring 'Em On!

rof. Waheed Bajwa received a grant entitled "High-Dimensional Linear Models? Bring 'Em On!", from the very competitive NSF Division of Computing and Communication Foundations (CCF) under its Core Program Communications and Information Foundations (CIF). The total budget is $167,543 for 3 years and Dr. Bajwa is the sole PI.

The project description is as follows:

This research addresses the challenge of high-dimensional data analysis within the context of linear models by developing low-complexity

inference methods based on marginal correlations of predictors with the response variable. One of the distinguishing features of this research is its emphasis on mathematical characterization of the performance of developed methods in the most general of terms. This is accomplished by drawing connections with the literature on finite frame theory. Because of the fairly general nature of this research, it significantly advances the state-of-the-art in inference problems arising in myriad areas, such as genomics, tumor classification, network monitoring and computer tomography. In addition, the frame-theoretic focus of this research lays the foundations for future cross-fertilization of ideas between statistical inference and frame theory.

WINLAB team to assist DARPA in administering the DARPA Spectrum Challenge

A team of ECE researchers at WINLAB, including Wade Trappe, Ivan Seskar, Chris Rose and Dipankar Raychaudhuri, are assisting DARPA in administering the DARPA Spectrum Challenge (http://www.darpa.mil/spectrumchallenge/). The purpose of the DARPA Spectrum Challenge is to encourage teams to design radio protocols that can best use a given communication channel in the presence of other dynamic users and interfering signals. The radio protocols, which will be implemented in a software radio platform, must be designed to guarantee successful communication while in the presence of other radios that may have conflicting co-existence objectives. The Spectrum Challenge will entail head-to-head competitions between a team's radio protocol and an opponent's in a structured testbed environment. WINLAB will prepare the ORBIT wireless testbed for hosting contests where the qualifying teams compete to demonstrate which protocols can best communicate competitively against each other or cooperatively with each other.

NSF Awards Janne Lindqvist $1.3M

Prof. Janne Lindqvist was awarded three grants by the NSF for a total of ~$1.3M. The projects support Dr. Lindqvist's long-term research thrusts of nudging human behavior with computer systems, and usable security for mobile systems. The award titles are: 

1) "Local Community Crowdsourcing of Physical-World Tasks with Myrmex",
2) "Redesigning Mobile Privacy: Helping Developers to Protect Users", and
3) "Capturing People's Expectations of Privacy with Mobile Apps by Combining Automated Scanning and Crowdsourcing Techniques"

"Local Community Crowdsourcing of Physical-World Tasks with Myrmex" was awarded $530,134.00. Myrmex is a novel crowdsourcing system for physical-world tasks that helps bring people in communities together to help each other.

Myrmex leverages the capacity of computers (monitoring, matching participants to tasks) to facilitate human activity (assigning and performing tasks), to create a new local community application. Prof. Lindqvist's collaborators in the project include Prof. Mor Naaman, Prof. Marco Gruteser, and Prof. Winter Mason (Stevens Institute of Technology).

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"Redesigning Mobile Privacy: Helping Developers to Protect Users" was awarded $318,636.00. This project will aid developers and nudge them to follow privacy principles by making their usage of personal information transparent. For example, system APIs designed from a privacy perspective will make it easier to obtain more general information, rather than potentially more sensitive, fine-grained personal information. Prof. Lindqvist's collaborator in the project is Prof. Marco Gruteser.

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"Capturing People's Expectations of Privacy with Mobile Apps by Combining Automated Scanning and Crowdsourcing Techniques" was awarded $400,000.00. The goal of the work is to (a) capture people's expectations and surprises in using mobile apps in a scalable manner, and to (b) summarize these perceptions in a simple format to help people make better trust decisions. Prof. Lindqvist's collaborators in the project include Prof. Jason Hong and Prof. Joy Zhang (Carnegie Mellon University).

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Dr. Janne Lindqvist is an Assistant Research Professor at WINLAB/ECE. More info on his activities is available at http://www.winlab.rutgers.edu/~janne/

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