Multi-user Data Sharing
System in Radar Sensor
Networks
Ming Li, Tingxin Yan, Deepak Ganesan, Eric Lyons,
Prashant Shenoy, Arun Venkataramani, and Michael Zink
Department of Computer Science
University of Massachusetts, Amherst
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Emerging Rich Sensor Networks
Radar Sensor Network
Camera Sensor Network
Richer energy
Tethered power
High data rate
Many MB/second
Diverse users/applications needs
E.g. First responders, Commuters,
Insurance, for traffic monitoring
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
CASA Radar Sensor Networks
Emergency
Personnel
Normal
User
Meteorologist
Densely monitoring the lower
troposphere
Tornado, storm, flood, …
Internet
Precipitation
Query
Tornado
Detection
Reflectivity
Overview
High rate sensor streams
300MB per radar scan every 30
seconds
Stream-based system
Proxy
Data
Processing
Data processing is done on proxy
Wide-area wireless mesh network
Data Stream
Multiple, diverse user needs
Emergency personnel,
meteorologist, other…
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Challenges in Multi-user WSNs
Emergency
Personnel
Researcher
NWS
3D
Assim
Tornado
Detection
A
C
Wind Dir
Estimation
Limited network resources
Bandwidth << Data needs
Diverse end user query needs
Diverse data quality metrics
Tornado: location error.
Wind direction: direction error
B
D
Different spatial areas of
interest
Wind direction: overlapping
area between radars
Different data fidelity needs
Tornado detection > 3D
assimilation
A B
C D
Different priorities and
deadlines
UNIVERSITY OF MASSACHUSETTS, AMHERST
Priority:NWS > Em. Mgr
Em. Mgr
< NWS
• Deadline:
Department of Computer
Science
Problem Statement
NWS
Emergency
Personnel
3D
Assim
Tornado
Detection
A
C
Researcher
Wind Dir
Estimation
How to design next generation
wireless radar sensor networks to:
Jointly optimize for different data
quality metrics and different
priorities and deadlines of different
users
B
D
Share bandwidth and data across
different users
Adapt gracefully to bandwidth
dynamics
A B
C D
Prioritize important data during
critical weather events.
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Key Ideas in Multi-User Data Sharing
Utility-driven transmission scheduling to prioritize
data transmission and maximize overall utility
Progressive compression to minimize bandwidth
usage and adapt to bandwidth fluctuation
Global transmission control to prioritize data
transmission among radars
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Outline
Motivation
Key Ideas
Progressive Compression
Utility-driven Transmission
Scheduling
Global Transmission Control
Evaluation
Summary
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Progressive Compressor
NWS
Emergency
Personnel
Radar
Progressive
Compressor
Utility-based
Scheduler
Raw Data
…
Researcher
SPIHT algorithm [set partition in
hierarchical trees]
Wavelet-based encoder, preserves
important features of interest for
meteorologist
Adapts to bandwidth fluctuation
Most important data is transmitted first
Err
Data Size
Decode error of SPIHT
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Progressive Compressor
NWS
Tornado
Detection
Emergency
Personnel
Assimilation
Wind Dir
Estimation
Progressive
Compressor
Utility-based
Scheduler
Raw Data
…
Researcher
Radar
Operation of Progressive compressor:
Split scan into spatial regions, each with a set
of queries associated with it.
Generate a separate stream for each spatial
region.
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Utility-based Scheduler
NWS
Tornado
Detection
Emergency
Personnel
Assimilation
Wind Dir
Estimation
Progressive
Compressor
Utility-based
Scheduler
Raw Data
…
Researcher
Radar
Utility-based Scheduler
Decides which packet offers greatest improvement to overall
utility
Key questions
How to determine utility of packet to a query?
How to aggregate utilities across diverse queries?
How to schedule packets based on their utilities?
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Utility of a Packet to a Query
What does scheduler have
Data Stream
Marginal Data MSE
SPIHT
Error Trace
What does scheduler need
Marginal query quality
How to map data MSE to query quality
Train a mapping function a priori using sample data sets
Distance between detected tornado and the actual one
Application
level
Training
Intensity of actual tornado
Intensity of detected tornado
Tor Err
Networking
level
Data MSE
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Aggregate Utility across Diverse
Queries
Aggregated utility is a weighted combination of utilities for
each query
Weightquery=f(query_priority, query_deadline)
UtilAgg=sum(Weightquery*Utilquery)
Query1
Tornado
Detect
Weight1
Utor, W1
Utility1
Tornado Utility
Utility
Query2
Assimilatio
n
Weight2
Uasm, W2
Utility2
Assim Utility
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Schedule Packets based on Utility
Schedules packet with the highest utility
Optimal if utility function as a function of data size is
concave
Utility
Data Size
P1
P2
U1>U2>U3
Scheduler
P1
P3
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Outline
Motivation
Key Ideas
Progressive Compression
Utility-driven Transmission Scheduling
Global Transmission Control
Evaluation
Summary
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Global Transmission Control
Problem:
Radar with critical data may not get sufficient bandwidth
Solution:
Proxy pauses streams that are achieving low/no utility gain
High Util
Tornado
Detect
Low Util
Assim
Proxy
Global
Transmission
Control
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Outline
Motivation
Key Ideas
Progressive Compression
Utility-driven Transmission Scheduling
Global Transmission Control
Evaluation
Summary
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Evaluation
Testbed
13 MacMini as adhoc mesh nodes
3-hop topology
Data Sets
Real data traces from Oklahoma radar testbed
Simulated data by ARPS(Advanced Regional Prediction of
Storms)
Query Pattern
Tornado Detection, 3D assimilation and Wind Direction
assimilation queries arrive in a round robin manner. Deadlines
are chosen from a Poisson distribution with mean at 30
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
seconds.
Determining the Utility Function
Tornado detection needs more accurate data than 3D assimilation.
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Performance of Utility-driven
Scheduler
Compare utility-driven scheduler to random scheduler
2x
The utility-based scheduler achieves 2 times higher utility than the
random scheduler
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Scalability
Demonstrates that our system as a whole scales well
with network size
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Scalability
Baseline System
Averaging compression
Bandwidth estimation
Estimate bandwidth for next epoch (30 secs) based on
history of bandwidth from previous epochs.
Conservative estimate to ensure that compressed scan
can be transmitted in the 30 seconds.
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Scalability
Demonstrates that our system as a whole scales well
with network size
4%
3x
30%
38%
10x
Our system achieves more than 10 times higher utility than the
baseline system
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Related Work
Multi-query optimization in sensor network
SQL Queries and simple aggregates: Trigoni, et. al [DCOSS
2005]
We have more complex data processing requirements.
Utility-driven network design in sensor networks and
Internet
SORA [NSDI05], Kelly et al [JORS98]
Does not address application-level data quality metrics and
data sharing between users
Global transmission control
Conflict Graph – Jain et al [Wireless Network 05], Rate
control – Rangwala et al [Sigcomm06]
We use application level utility of data to control
transmissions.
Radar sensor networks
Schedules radar scanning strategy to satisfy end-user needs
ZinkUet
al [EESR05].
NIVERSITY
OF MASSACHUSETTS, AMHERST • Department of Computer Science
Summary
Illustrates new challenges in next-gen radar sensor
networks
Design and implementation of a multi-user data sharing
system that:
Gracefully degrades utility under bandwidth fluctuations by
using progressive compression
Utility-driven packet scheduling based on end-user data
quality metrics, priorities, and deadlines.
Globally prioritizes data transmission across radars.
Results show one order of magnitude improvement in
application utility over existing radar data transmission
system.
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
The End
Questions?
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Future work
Joint radar sensing, bandwidth and energy optimization
Extend system to other types of WSNs like camera sensor
networks.
Design a hop-by-hop bulk transfer protocol that optimizes
radar data transfer
Explore rate control and bandwidth allocation for global
transmission control
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
MUDS Overview
Radar
Utility-based
Scheduler
Progressive
Compressor
…
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Utility-based Scheduler
NWS
Tornado
Detection
Radar
Progressive
Compressor
Wind Dir
Estimation
Raw Data
Researcher
Utility-based
Scheduler
…
Emergency Assimilation
Personnel
Utility-based Scheduler
Schedules packets between different streams based on the overall
utility of each packet to the queries
Each stream is shared by multi-queries and each application has
different data needs
How to determine utility of packet to an application?
How to aggregate utilities across diverse queries?
How to schedule packets based on their utilities?
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Utility-based Scheduler
NWS
Tornado
Detection
Radar
Progressive
Compressor
Wind Dir
Estimation
Raw Data
Researcher
Utility-based
Scheduler
…
Emergency Assimilation
Personnel
Utility-based Scheduler
Schedules packets between different streams based on the overall
utility of each packet to the queries
Each stream is shared by multi-queries and each application has
different data needs
How to determine utility of packet to an application?
How to aggregate utilities across diverse queries?
How to schedule packets based on their utilities?
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Adaptation to Bandwidth Fluctuation
Compare progressive SPIHT to non-progressive SPIHT
Progressive
4x
SPIHT
Data Stream
Non-Progressive
SPIHT
Compressed Data
Bandwidth Estimator
Moving Window
Bandwidth Trace
Progressive SPIHT achieves up to 4 times lower data
MSE than the non-progressive scheme
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
Scalability
Baseline System
Bandwidth estimation
CDF
5%
Bandwidth
Averaging compression
Scan 1
Scan 2
Scan 3
Transmit 1
Transmit 2
…
Sense
Transmit 3
Transmit
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science
…
Progressive Compressor
NWS
Tornado
Detection
Emergency
Personnel
Assimilation
Wind Dir
Estimation
Progressive
Compressor
Utility-based
Scheduler
Raw Data
…
Researcher
Radar
SPIHT algorithm [set partition in
hierarchical trees]
Wavelet-based encoder, preserves
important features of interest for
meteorologist
Adapts to bandwidth fluctuation
Most important data is transmitted first
Err
Data Size
Decode error of SPIHT
UNIVERSITY OF MASSACHUSETTS, AMHERST • Department of Computer Science