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SAVE: Sensor Anomaly
Visualization Engine
Lei Shi1 Qi Liao2 Yuan He3
Rui Li4 Aaron Striegel2 Zhong Su1
1 IBM
Research
— China
2
University of
Notre Dame
3
Hong Kong University of
Science and Technology &
Tsinghua University
4
Xi’an Jiao Tong
University
GreenOrbs Project
A Long-Term Kilo-Scale Wireless Sensor Network System in the Forest
Sensor
Motes
Packaging
&
Enclosure
Deployments in Zhejiang Forest University, China
Outline
 Problem & Related Work
 Data Collection
 SAVE Overview
 Visual Analytics for the Sensor Anomalies
– Temporal Expansion Model (Routing Topology and Dynamics)
– Correlation Graph (Dimension Correlation and Dynamics)
– High Dimension Data Projection (Dimension Values and Dynamics)
 A Case on Sensor Failure Diagnosis
 User Feedbacks
 Commercialization with SmartMTS
 Conclusion
Problem & Related Work
 Diagnosis of large-scale sensor networks in
the wild is challenging!
– Various resource constraints in computing, storage
and transmission => Hard to reuse traditional network
management approaches
– Huge performance variability or even frequent system
failures due to the outdoor deployments (sometimes
in hostile environment)
– Lack of automatic algorithms and models to
accurately identify the sensor anomalies
TOS
SIM
 Related work
Mote
View
– Network simulators
• MOTE_VIEW, TOSSIM, NetTopo, TinyViz
GrowthRingMap
– Sensor network tools
• SNA, Surge, SpyGlass, SNAMP
– Sensor fault classifications
• Outliers, spikes, stuck-at, noise
– Relevant visualizations
• GrowthRingMap, SpiralGraph,
StarCoordinate
StarCoordinate
Data Collection
 Sensor data is measured at each node (mote) and transmitted as a
couple of packets every 10 minutes to a central sink node for data fusion
– Sensor Readings
• Environmental indicators: temperature, light, humidity, CO2
• Need preprocessing to translate to real-world scales
– Routing Path to the Sink
• Each node in the path is piggybacked during the packet forwarding process
• Used to create the routing topology
– Wireless Link Status to the Neighbors
• Typical link quality indicators: RSSI, LQI, ETX
– Networking/System Statistics
• Radio power-on time, number of packets transmitted/received/dropped/etc.
• Routing protocol statistics: parent change events and no parent events
SAVE Overview
TEM Graph
Dimension
Projection
View
Dimension
Details
View
Correlation
Graph
Scented Time Slider
Temporal Expansion Model
 Difficulties to represent the
dynamic sensor routing &
delivery network
– Sensor routing independent to
their geospatial locations
– Frequent re-routing across time
– Delivery topology buried by the
network variance
Geospatial Layout
 Temporal Expansion Model
– Leverage the feature of sensor
data delivery: synthesized at the
central sink node
– Key innovation: split the physical
sensor node into virtual nodes
according to their delivery paths
– Advantages:
• Transformed into a tree
• Identify topology dynamics
• Possible to display a single
physical node’s behavior
Graph-aesthetic Layout
TEM Graph Layout
2
1
Original
Dynamic
Sensor
Data
Delivery
Graph
T0,T1
T1
T1
4
1
Node Split
sink
T1
T0
Reroute
3
2
Reroute
T0
4
2
2
1
1
4
Native
Packets
4
1
TEM
Graph
With
GrowthRing
Renderer
sink
sink
3
2
3
1
4
Normal
Node
TEM
Graph
Temporal
Rendering
Abnormal
Node
2
4
T0,T1
T0
3
2
T1
sink
T0,T1
T0
TEM Graph
Counting
Sensor Packets
Generated at
Each Node
T0,T1
4
TEM Graph Visualization
 Semantic “overview + detail” approach in the
Graph
overview
TEM graph visualization
– “Detail” shows the specific paths from one physical
node to the sink node
– GrowthRing glyphs visualize the packet
forwarding/initiation temporal dynamics
– Visual alerts show topology anomalies: loops,
major/minor paths, temporal change rings
Loops
Forwarding
dynamics
Minor
path
Sending
dynamics
Node path
to sink
Temporal
anomaly ring
Group
re-routing
Correlation Graph
 Observations
– Sensor data dimensions (system status, routing status, sensor readings) are correlated
– These correlations can be a measure of system dynamics and anomalies
 Correlation Graph (CG)
– Compute the Pearson’s product moment coefficient given the two dimension vectors
– Two major type of CG: among sensor reading dimensions, among sensor counter
dimensions
Sensor
Readings
Sensor Status
Counters
Mixed Correlation
Graph
CG Visualization
 Raw CG
– Layout: basic force-directed KK layout model, optimal distance inversely proportional to
the correlation coefficient
– Link thickness: indicate the correlation coefficient
– Allow filtering of the graph by a correlation threshold
 Comparative CG
– Delta CG – change from the last time slot; Anomaly CG – change from the average CG
– Link thickness: indicate the change of the correlation coefficient between two dimensions
– Link color: green indicates the increased correlation, red indicates the decreased
– Node color: indicate the increase/decrease of a dimension’s overall correlation to others
Raw CG
Delta CG
Anomaly CG
Sensor Reading
CG
High Dimensional Sensor Data Projection
 Dimension Projection View
Design
– The dimension anchors are placed uniformly in a circle
– The data plots are placed inside the circle
• Each plot indicates the high dimensional sensor
reading/status in a particular time
• The plot is placed according to a spring force model, the
values of each dimension is normalized to [0,1]
– Show temporal dynamics of the sensor data
• Plots of the same sensor node are connected to the path
• Time position in the selected range are encoded by color
Basic Projections
Drill-down to Values
Temporal Dynamics
View Coordination
 Data filtering through the time
Coordinated Multiple View
range selection on the slider
– The TEM graph and the dimension
projection view are filtered to the
graphs in the selected time range
 Node Selection
 Data brushing through the node
and data dimension selection
– Node selection:
 Dimension Selection
• The TEM graph and dimension
projection view are brushed
• The detailed path and correlation
graph view are created
• The time range slider are brushed
with bars, indicating the number of
packets transmitted in a particular
time on the selected node
– Dimension selection:
• The correlation graph and dimension
projection view are brushed
• The detailed value graph are created
 Time Selection
A Case on Sensor Failure Root Cause Analysis
 Identify an anomaly on Node 543
 Double-check another possible root cause
 Check the cause of this anomaly
 Check the symptom of the parent node
User Feedbacks and Discussions
 Pros from the user’s perspective
– Visibility of the salient sensor data
– Ability to drill-down to the source data to discover new type of failures
– Dimension projection view that displays the distribution of all the sensor
dimensions, and the interactions to show the detailed value upon hovering the
plot
– TEM graph is an intuitive radial way to describe the topology
 Cons/suggestions from the user’s perspective
– Graphs like TEM is a little complicated and need some time to understand
– Add a report view to automatically display the faults that can be detected
routinely
– Issues to work under low sensor data quality assumption
Application in SmartMTS
 Application in SmartMTS solution
– Enable support, management and
optimization of large scale & complex
Smart Grid IoT Infrastructures.
– Infuse new, smarter services and
management processes that are vital for
• Real-time operations visibility
• Quick & precise response to outages
• Smart asset performance optimization
Conclusion
 We have designed and implemented the SAVE system
– Leverage the visual analytics technology to solve the sensor network
diagnosis problem
– Focus on the detect and root cause analysis of sensor data anomalies and
failures
 Several novel visualization metaphors are designed, some are
generic techniques
– TEM graph for the dynamic network visualization
– CG graph for the monitoring of temporal dimension correlations
– A new dimension projection view for the presentation of the spatiotemporal
dynamics of the high dimensional data
 SAVE is shown to be useful in the scenario through
– A real-life case study for the sensor failure root cause analysis
– Domain user feedbacks
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Thank You
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