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
Thai
Korean
Traditional Chinese
Russian
Gracias
Thank You
English
Spanish
Obrigado
Brazilian Portuguese
Arabic
Danke
German
Grazie
Italian
Simplified Chinese
Merci
French
Japanese
Tamil
18
Hindi
0
