Information Processing in Sensor Networks (IPSN '04)

Sponsorship by IEEE Signal Processing Society and ACM SIGBED
in cooperation with the IEEE Communication Society and ACM Sigmobile
with support from NSF and DARPA

Third International Symposium
Berkeley, California, USA
April 26-27, 2004

Shuttle service is provided for attendees staying at  the Double Tree to the U.C. Campus. Shuttles will board for the day at 7:30 am.

Technical Program

April 25, 2004(Sunday)
WELCOME RECEPTION  / Pre-Registration 6pm @ the Doubletree Hotel.

April 26 (Monday)
8:00-8:20am            Continental Breakfast and Registration

8:30-8:45am                        Welcome and Introduction 
Conference Co-chairs: Kannan Ramchandran and Janos Sztipanovits
Technical Co-chairs: Jennifer C. Hou and Thrasos Pappas

8:45am-10:30am             Oral Presentation Session I.
                            In network modeling, processing, and optimization
Session Chair: Massimo Franceschetti (University of California at Berkeley)
Distributed Regression: an Efficient Framework for Modeling Sensor Network Data
Carlos Guestrin (Intel Research - Berkeley), Peter Bodik (UC Berkeley), Romain Thibaux (UC Berkeley),Mark Paskin (University of California, Berkeley), Samuel Madden (Intel Research - Berkeley)

Locally Constructed Algorithms for Distributed Computations in Ad-Hoc Networks 
Dzulkifli Scherber (University of Maryland),Babis Papadopoulos (University of Mayland)

Distributed Optimization in Sensor Networks
Michael Rabbat (University of Wisconsin - Madison), Robert Nowak (University of Wisconsin - Madison)

The Impact of Spatial Correlation on Routing with Compression in Wireless Sensor Networks 
Sundeep Pattem (University of Southern California), Bhaskar Krishnamachari (University of Southern California), Ramesh Govindan (USC/Information Sciences Institute)

Entropy-based Sensor Selection Heuristic for Target Localization
Hanbiao Wang (University of California, Los Angeles), Kung Yao (UCLA), Gregory Pottie (UCLA), Deborah Estrin (UCLA)

10:30-12:30pm                   Poster Presentation Session I 
                                                Group A: Localization
Semidefinite Programming for Ad Hoc Wireless Sensor Network Localization 
Yinyu Ye (Stanford University), Pratik Biswas (StanfordUniversity)

A Bit-Map-Assisted Energy-Efficient MAC Scheme for Wireless Sensor Networks 
Jing Li (Mississippi State University), Georgios Lazarou (Mississippi State University)

Distributed Online Localization in Sensor Networks Using a Moving Target 
Aram Galstyan (USC Information Sciences Institute),Bhaskar Krishnamachari (University of Southern California), Kristina Lerman (Information Sciences Institute, USC), Sundeep Pattem (University of Southern California)

On the Effect of Localization Errors on Geographic Face Routing in Sensor Networks 
Karim Seada (University of Southern California), Ahmed Helmy (University of Southern California), Ramesh Govindan (USC/Information Sciences Institute)

                 Group B. Collaborative and distributed signal processing
Distributed Beamforming for Information Transfer in Sensor Networks
Gwen Barriac (University of California Santa Barbara), Raghuraman Mudumbai (University of California Santa Barbara), Upamanyu Madhow (University of California, Santa Barbara)

On Distributed Sampling of Smooth Non-Bandlimited Fields 
Animesh Kumar (University of California at Berkeley), Prakash Ishwar (University of California at Berkeley), Kannan Ramchandran (University of California at Berkeley)

Distributed Particle Filtering in Sensor Networks 
Mark Coates (McGill University)

Fusion in Sensor Networks with Communication Constraints 
Saeed Aldosari (Carnegie Mellon University), Jose Moura (Carnegie Mellon University)

                                 Group C. Energy Conservation
An Energy Conservation Method For Wireless Sensor Networks Employing a Blue Noise Spatial Sampling Technique 
Mark Perillo (University of Rochester),Zeljko Ignjatovic (University of Rochester), Wendi Heinzelman (University of Rochester)

Backcasting: An Adaptive Approach to Energy Conservation in Sensor Networks 
Rebecca Willett (Rice University),Aline Martin (University of Wisconsin-Madison), Robert Nowak (University of Wisconsin - Madison)

A Wake-Up Detector for an Acoustic Surveillance Sensor Network: Algorithm and VLSI Implementation 
David Goldberg (Johns Hopkins University),Andreas Andreou (Johns Hopkins University),  Pedro Julian (Universidad Nacional del Sur),Philippe Pouliquen (Johns Hopkins University), Laurence Riddle (Signal Systems Corporation),Rich Rosasco (Signal Systems Corporation)

Power-Efficient Sensor Placement and Transmission Structure for Data Gathering under Distortion 
Deepak Ganesan (UCLA),Razvan Cristescu (Swiss Federal Institute of Technology in Lausanne), Baltasar Beferull-Lozano (Swiss Federal Institute of Technology Lausanne (EPFL))

                                        Group D.  Connectivity
RoamHBA: Maintaining Group Connectivity in Sensor Networks 
Qing Fang (Stanford University), Jie Liu (Palo Alto Research Center), Leonidas Guibas (Stanford University), Feng Zhao (Microsoft Research)

10:30-12:30pm                                 Demo Session 
                           (in parallel with the poster presentation session I)
Demo one : Future Combat System (FCS) demo using JavaSim
Jennifer C. Hou, Ning Li, Ahmed Sobeih, Honghai Zhang (UIUC). Abstract 

Demo two : VisualSense: Modeling of Wireless Sensor Networks using Ptolemy II
Philip Baldwin, Sanjeev Kohli, Edward A. Lee, Xiaojun Liu, Yang Zhao, Charlie Zhong (UC Berkeley). Abstract

Demo three:  galsC: A language for event-driven embedded systems
Elaine Cheong (UC Berkeley), Jie Liu (Palo Alto Research Center). Abstract

Demo four: Mesh Networking Enhancements for TinyOS
Mike Horton (CEO Crossbow Technology, Inc.). Abstract

Demo Five: WSN-based Shooter Localization
Gyula Simon, Miklos Maroti,  Akos Ledeczi , Gyorgy Balogh, Branislav Kusy, Andras Nadas, Gabor Pap,  Janos Sallai, Ken Frampton (Vanderbilt University). Abstract

Demo Six: GRATIS: Graphical  Development Environment for TinyOS
Peter Volgyesi, Miklos Maroti, Sebestyen Dora, Esteban Osses, Akos Ledeczi (Vanderbilt University). Abstract

12:30-1:00pm                                   Lunch

1:00-2:30pm                         Keynote Speech
Keynote topic: Is there SECS in your future ? (Self-repairing  Entertaining Customized  Systems)
Keynote Speaker: Dr. Jean Paul Jacob (IBM research)

2:30-2:45pm                      Afternoon Break

2:45-4:05pm            Oral Presentation Session II.
                            Network capacity and achievable rate
Session Chair: S. Sandeep Pradhan (University of Michigan, Ann Arbor)
Reliability vs. Efficiency in Distributed Source Coding for Field-Gathering 
Daniel Marco (University of Michigan), David Neuhoff (University of Michigan)

On the Scalability and Capacity of Wireless Networks with Omnidirectional Antennas 
Onur Arpacioglu (Cornell University), Zygmunt Haas (Cornell University)

Multi-Hop Communication is Order-Optimal for Homogeneous Sensor Networks 
Arnab Chakrabarti (Rice University), Ashutosh Sabharwal (Rice University), Behnaam Aazhang (Rice University)

Lattice Sensor Networks: Capacity Limits, Optimal Routing and Robustness to Failures 
Guillermo Barrenechea (Swiss Federal Institute of Technology Lausanne (EPFL)), Baltasar Beferull-Lozano (Swiss Federal Institute of Technology Lausanne (EPFL)), Martin Vetterli (Swiss Federal Institute of Technology)

4:05-5:05pm                   Oral Presentation Session III.
                                              Energy efficient design 
Session Chair: Rajesh Gupta (University of California, San Diego)
Effect of Overhearing Transmissions on Energy Efficiency in Dense Sensor Networks 
Prithwish Basu (BBN Technologies), Jason Redi (BBN Technologies)

Flexible Power Scheduling for Sensor Networks 
Barbara Hohlt (University of California, Berkeley), Lance Doherty (UC Berkeley), Eric Brewer (University of California at Berkeley)

An Energy-Aware Data-Centric Generic Utility Based Approach in Wireless Sensor Networks 
Wei-Peng Chen (University of Illinois at Urbana Champaign), Lui Sha (University of Illinois at Urbana-Champaign)

5:30pm Shuttle service from Campus (in front of HEARST MINING CIRCLE) to Double Tree

6:15pm Hornblower Dinner Cruise Boards at Double Tree Marina dock

6:45pm(Departing)-9:45pm(Returning)    Dinner Cruise

April 27 (Tuesday)

8:30-9:45am                         Keynote Speech
Keynote topic: PEDAMACS: Sensor networks for measuring traffic
Keynote Speaker: Dr. Pravin Varaiya (UC Berkeley)

9:45-10:00am                             Morning Break 

10:00-11:45am                  Oral Presentation Session IV.
                                              Estimation and detection 
Session Chair: Urbashi Mitra (University of Southern California)
Nonparametric Belief Propagation for Self-Calibration in Sensor Networks 
Alexander Ihler (Massachusetts Institute of Technology),John Fisher (MIT AI Lab), Randy Moses (The Ohio State University),Alan Willsky (MIT)

Distributed State Representation for Tracking Problems in Sensor Networks 
Juan Liu (Palo ALto research Center),Jie Liu (Palo Alto Research Center), Maurice Chu (PARC), Jim Reich(Palo ALto research Center),Feng Zhao (Microsoft Research)

How to Distribute Sensors in a Random Field? 
Xin Zhang (Cornell University),Stephen Wicker (Cornell University)

Estimation from Lossy Sensor Data: Jump Linear Modeling and Kalman Filtering 
Alyson Fletcher (University of California, Berkeley),Sundeep Rangan (Flarion Technologies), 
Vivek Goyal (Massachusetts Institute of Technology)

The Sybil Attack in Sensor Networks: Analysis & Defenses 
James Newsome (Carnegie Mellon University), Elaine Shi (Carnegie Mellon University), Dawn Song (CMU), Adrian Perrig (Carnegie Mellon University)

11:45-2:30pm    Working Lunch Poster Presentation Session II 
                            Group E. Network capacity and achievable rate
Complexity Constrained Sensor Networks: Achievable Rates for Two Relay Networks and Generalizations 
Urbashi Mitra (University of Southern California),Ashutosh Sabharwal (Rice University)

Fractional Cascaded Information in a Sensor Network 
Jie Gao (Stanford University),Leonidas Guibas (Stanford University), John Hershberger (Mentor Graphics),Li Zhang (HP Labs)

Power-Bandwidth-Distortion Scaling Laws for Sensor Networks
Michael Gastpar (University of California, Berkeley), Martin Vetterli (Swiss Federal Institute of Technology)

Rate-distortion problem for physics based distributed sensing 
Baltasar Beferull-Lozano (Swiss Federal Institute of Technology Lausanne (EPFL)), Robert Konsbruck (Swiss Federal Institute of Technology - EPFL), Martin Vetterli (EPFL)

                                           Group F. Synchronization
Adaptive Clock Synchronization in Sensor Networks
Santashil PalChaudhuri (Rice University), Amit Kumar Saha (Rice University), David B. Johnson (Rice University)

Improved Interval-Based Clock Synchronization in Sensor Networks
Philipp Blum (ETH Zurich),Lennart Meier (ETH Zurich),Lothar Thiele (ETH Zurich)

                       Group G. Modeling and Evaluation Methodology
Modeling of Sensor Nets in Ptolemy II 
Philp Baldwin (UC Berkeley),Sanjeev Kohli (UC Berkeley),Edward Lee (Berkeley), Xiaojun Liu (UC Berkeley)

Scattered data selection for dense sensor networks
Lance Doherty (UC Berkeley),Kristofer Pister (UC Berkeley)

Constraint-Guided Dynamic Reconfiguration in Sensor Networks
Sachin Kogekar (Vanderbilt University),Sandeep Neema (Vanderbilt University/ISIS), Brandon Eames (Vanderbilt University),Xenofon Koutsoukos (Vanderbilt University), Akos Ledeczi (Vanderbilt University),Miklos Maroti (Vanderbilt University)

                               Group H. Estimation and Detection
Sensing Uncertainty Reduction Using Low Complexity Actuation 
Aman Kansal (University of California, Los Angeles), Eric Yuen (University of California, Los Angeles),William Kaiser (University of California, Los Angeles), Gregory Pottie (University of California at Los Angeles), Mani Srivastava (University of California at Los Angeles)

Loss Inference in Wireless Sensor Networks based on Data Aggregation
Gregory Hartl (University of Toronto),Baochun Li (University of Toronto)

Robust distributed estimation in sensor networks using the Embedded Triangles algorithm 
Veronique Delouille (Rice University),Ramesh Neelamani (ExxonMobil), Richard Baraniuk (Rice University)

2:00-3:30pm                               Panel Discussion
Where are we in Sensor Networks today?  A retrospective evaluation and an assessment of future challenges and prospects.
Dr. P.R. Kumar (moderator, University of Illinois at Urbana Champaign)
Dr. Gary Shaw (MIT, Lincoln Laboratory)
Dr. Deborah Estrin (UCLA)
Dr. Sri Kumar (DARPA)
Dr. Kris Pister (Dust Inc./Univ. of California at Berkeley)

3:30-3:45pm                               Afternoon Break 

3:45-5:15pm                        Oral Presentation Session V.
                                        Query processing and data collection
Session Chair: Bhaskar Krishnamachari (University of Southern California)
A Probabilistic Approach to Inference with Limited Information in Sensor Networks 
Rahul Biswas (Stanford University),Sebastian Thrun (Stanford University), 
Leonidas Guibas (Stanford University)

Efficient and Robust Query Processing in Dynamic Environments Using Random Walk Techniques 
Chen Avin (University of California at Los Angeles), Carlos Brito (UCLA)

On the Scalability of Hierarchical Cooperation for Dense Sensor Networks
Tamer ElBatt (HRL Laboratories, LLC)

Virtual Radar Imaging for Sensor Networks 
Bharath Ananthasubramaniam (University of California, Santa Barbara), Upamanyu Madhow (University of California, Santa Barbara)

5:15-6:35pm                      Oral Presentation Session VI.
                                              Coverage and connectivity 
Session Chair: Baochun Li (University of Toronto)
Naps: Scalable, Robust Topology Management in Wireless Ad Hoc Networks
David Ratajczak (University of California - Berkeley), Brighten Godfrey (University of California, Berkeley)

Co-Grid: An Efficient Coverage Maintenance Protocol for Distributed Sensor Networks 
Guoliang Xing (Washington University in St. Louis) , Chenyang Lu (Washington University in St. Louis),  Robert Pless (Washington University in St. Louis), Joseph A. O'Sullivan (Washington University in St. Louis)

Set K-Cover Algorithms for Energy Efficient Monitoring in Wireless Sensor Networks 
Zoe Abrams (Stanford University), Ashish Goel (Stanford University), Serge Plotkin (Stanford University)

Network Coverage Using Low Duty-Cycled Sensors: Random & Coordinated Sleep Algorithms 
Chih-fan Hsin (University of Michigan), Mingyan Liu(University of Michigan)

Abstract of demo one
: Future Combat System (FCS) demo using JavaSim
As part of the DARPA/NMS efforts, we have implemented in J-Sim an essential set of classes to simulate FCS communications technology, that includes the satellite link model, (omni-directional and directional) antenna models the irregular terrain model (that emulates TIREM), the IEEE 802.11 MAC interference and contention model, the ad hoc routing protocol (AODV), and an essential set of sensor network components (with both the sensing and communications channels).  To facilitate planning of dynamic on-the-move (OTM) mobile ad-hoc networks, we have incorporated a UAV placement algorithm that dynamically adjusts the fly paths and altitudes of UAVs based on the movement traces of ground entities, with the objective of maintaining global network connectivity.  We will carry out a full fledged version of FCS simulation based on a 527-node scenario (with movement traces of all the warfare entities provided by SAIC) in the demo. A Java3D-integrated visualization tool (that was built into J-Sim) will be used to display terrain, node movement, and network status (in terms of network connectivity and performance).
Abstract of demo two: VisualSense: Modeling of Wireless Sensor Networks using Ptolemy II
VisualSense is an open-source framework for modeling and design of wireless sensor networks built on Ptolemy II. This extensible framework supports actor-oriented definition of sensor nodes, wireless communication channels, physical media such as acoustic channels, and wired subsystems. The software architecture consists of a set of base classes for defining channels and sensor nodes, a library of subclasses that provide certain specific channel models and node models, and an extensible visualization framework. Custom nodes can be defined by subclassing the base classes and defining the behavior in Java or by creating composite models using any of several Ptolemy II modeling environments, including continuous-time, dataflow, state machines, and discrete-events. Custom channels can be defined by subclassing the WirelessChannel base class and by attaching functionality defined in Ptolemy II submodels.  This demo will illustrate the capabilities with several examples of models that include mobility, media access control, and networking strategies.
Abstract of demo three: galsC: A language for event-driven embedded systems
We introduce galsC, a language and compiler designed for TinyGALS, a globally asynchronous, locally synchronous model for programming event-driven embedded systems, especially sensor networks. The current release, designed for Berkeley motes, is compatible with TinyOS 1.x and nesC. At the local level, software components communicate with each other via synchronous method calls. Components are composed to form actors. At the global level, actors communicate with each other asynchronously via message passing. A complementary model called TinyGUYS is a guarded yet synchronous model designed to allow thread-safe sharing of global state between actors without explicitly passing messages. The TinyGALS programming model is structured such that code for all inter-actor communication, actor triggering mechanisms, and access to guarded global variables can be automatically generated from a high level specification. By raising concurrency concerns above the level of TinyOS components, the TinyGALS programming model allows programmers to focus on the main tasks that the application must execute.  Programs developed using this task-oriented model are thread safe and easy to debug.
Abstract of demo four: Mesh Networking Enhancements for TinyOS
Crossbow is displaying a new Surge Graphical User Interface for performance monitoring and evaluation of wireless mesh networks.  The demonstration uses the latest enhancement for reliability and low power in mesh networks.
Astract of demo five: WSN-based Shooter Localization
An ad-hoc wireless sensor network-based system is presented that detects and accurately locates shooters even in dense urban environments. The system is based on small, inexpensive distributed processing nodes. The localization accuracy of the system in open terrain is competitive with that of existing centralized countersniper systems. However, the proposed sensor network-based solution surpasses the traditional approach in urban environments because it can mitigate multipath effects by utilizing a large number of simultaneous measurements at different locations. In this demonstration, in addition to the overall system architecture, the time synchronization, sensor localization and message routing services and the unique sensor fusion algorithm are also described. Finally, an analysis of the experimental data gathered during field trials at a US military facility is given.
Abstract of demo six: GRATIS: Graphical  Development Environment for TinyOS
We present a model-based approach to the development of applications based on TinyOS (with nesC), developed at UC Berkeley, an important platform. OS and application component interfaces along with their interdependencies are captured in a graphical environment and the glue code that ties together the application and OS components are automatically generated. Furthermore, the component interfaces are captured in a hierarchical interfaces automata language. Component composition are then verified using the UPAAL verification tool.  GRATIS is a fully functional modeling, code generation and parsing environment developed using model integrated technology, specifically the Generic Modeling Environment (GME).



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