UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory 1 A Wireless Sensor Network For...
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Transcript of UNIVERSITY OF SOUTHERN CALIFORNIA Embedded Networks Laboratory 1 A Wireless Sensor Network For...
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
1
A Wireless Sensor Network For Structural Monitoring
(Wisden)
Collaborators: Ning Xu, Krishna Kant Chintalapudi, Deepak Ganesan, Alan Broad, Ramesh Govindan, Deborah Estrin,
Jeongyeup Paek, Nupur Kothari
Sumit Rangwala
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
2
BackgroundBackground• Structural health monitoring (SHM)
– Detection and localization of damages in structures» Structural response
• Ambient vibration (earthquake, wind etc)
• Forced vibration (large shaker)
• Current SHM systems– Sensors (accelerometers) placed at different structure location
– Connected to the centralized location » Wires (cables)
» Single hop wireless links
– Wired or single hop wireless data acquisition system
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
3
MotivationMotivation• Are wireless sensor
networks an alternative?
• Why WSN?– Scalable
» Finer spatial sampling
– Rapid deployment
• Wisden– Wireless multi-hop data
acquisition system
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
4
ChallengesChallenges• Reliable data delivery
– SHM intolerant to data losses
• High aggregate data rate– Each node sampling at 100 Hz or above
» About 48Kb/sec (10 node,16-bit sample, 100Hz, 3 axes)
• Data synchronization– Synchronizing samples from different sources at the base station
• Resource constraints– Limited bandwidth and memory
• Energy efficiency– Future work
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
5
Wisden ArchitectureWisden Architecture
Challenges Architectural Component
Description
Reliable data delivery
Reliable Data Transport
Hybrid hop-by-hop and end-to-end error recovery
High data rate Compression Silence suppression
Wavelet based compression
Data Synchronization
Data Synchronization
Residence time calculation in the network
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
6
Reliable Data TransportReliable Data Transport• Routing
– Nodes self-organize in a routing tree rooted at the base station
– Used Woo et al.’s work on routing tree construction
• Reliability – Hop-by-hop recovery
» How ?• NACK based• Piggybacking and
overhearing
» Why hop by hop? • High packet loss
NACK
Retransmission
NACK
Retransmission
NACK
Retransmission
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
7
Reliable Data Transport (cont.)Reliable Data Transport (cont.)– End to End packet recovery
» How ?• Initiated by the base station (PC) • Same mechanism as hop-by-hop NACK
» Why ? • Topology changes leads to loss of missing packet information• Missing packet information may exceed the available memory
– Data Transmission rate» Rate at which a node inject data
• Currently pre-configured for each node at R/N– R = nominal radio bandwidth – N = total number of nodes
» Adaptive rate allocation part of future work.
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
8
CompressionCompression• Sampled data significant
fraction of radio bandwidth• Event based compression
– Detect Event » Based on maximum
difference in sample value over a variable window size
– Quiescent period» Run length encoding
– Non-quiescent period» No compression
– Saving proportional to duty-cycle of vibration
• Drawback– High latency
Quiescent Period
Event Quiescent Period
Compression No Compression
Compression
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
9
Compression For Low LatencyCompression For Low Latency• Progressive storage and
transmission– Event detection
– Wavelet decomposition and local storage
– Compression » Low – resolution components
are transmitted
– Raw data, if required available from local storage
• Current Status
– Evaluated on standalone implementation
– To be integrated into Wisden
Wavelet Decomposition
Quantization, Thresholding, Run length coding
Sink
Flash Storage
To sink on demand
Reliable Data Transport
Event
Low resolution components
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
10
Data SynchronizationData Synchronization• Synchronize data samples at the
base station– Generation time of each sample in
terms of base station clock
– Network wide clock synchronization not necessary
• Light-weight approach– As each packet travels through the
network » Time spent at each node
calculated using local clock and added to the field “residence time”
» Base station subtracts residence time from current time to get sample generation time.
– Time spent in the network defines the level of accuracy
S
q AAq
A
q A +
q B
Bq
B
TA=T-(qA + qB) TC=T-(qC + qD)
qC C
qC
qC + q
D DqD
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
11
ImplementationImplementation• Hardware
– Mica2 motes – Vibration card (MDA400CA
from Crossbow)» High frequency sampling (up
to 20KHz)» 16 bit samples» Programmable anti-aliasing
filter
• Software– TinyOS– Additional software
» 64-bit clock component» Modified vibration card
firmware
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
12
Deployment ScenarioDeployment Scenario11
• Seismic test structure– Full scale model of an
actual hospital ceiling structure
• Four Seasons building – Damaged four-storey office
building subjected to forced-vibration
1Not presented in the paper
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
13
Seismic Test Structure SetupSeismic Test Structure Setup• Setup
– 10 node deployment– Sampling at 50 Hz along three
axes– Transmission rate at 0.5
packets/sec– Impulse excitation using
hydraulic actuators• For validation
– A node sending data to PC over serial port (Wired node)
– A co-located node sending data to the PC over the wireless multihop network (Wisden node)
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
14
Results: Frequency ResponseResults: Frequency Response
• Low frequency modes captured• High frequency modes lost
– Artifact of compression scheme we used
Power spectral density: Wisden node Power spectral density: Wired node
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
15
Results: Packet Reception and LatencyResults: Packet Reception and Latency• Packet reception
– 99.87 % (cumulative over all nodes)
– 100 %, if we had waited longer
• Latency– 7 minutes to collect data for
1 minute of vibration
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
16
Four Seasons BuildingFour Seasons Building• Setup
– 10 node deployment– Sampling at 50 Hz along
three axes– Transmission rate at 0.5
packets/sec– Excitation using eccentric
mass shakers
• For validation– Wisden nodes places
alongside floor mounted force-balance accelerometer (Wired node)
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
17
Results: Frequency ResponseResults: Frequency Response
• Dominant frequency captured• Noise
– Sampling differences, force balanced accelerometer much more sophisticated, packet losses
Power spectral density: Wisden Node Power spectral density: Wired Node
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
18
Results: Packet ReceptionResults: Packet Reception• Packet reception
– High data loss» Due to a bug
UNIVERSITY OFSOUTHERN CALIFORNIA
Embedded Networks Laboratory
19
Conclusions and Future WorkConclusions and Future Work• Wisden – A wireless data acquisition system that provides
– Reliable data collection– Supports high sampling rate– Data synchronization
• Future work– Adaptive rate allocation scheme– Integrating wavelet based compression– Power efficiency
• Wisden version 0.1 available at http://enl.usc.edu/
Thank you