SAVE: Sensor Anomaly Visualization Engine Lei Shi 1 Qi Liao 2 Yuan He 3 Rui Li 4 Aaron Striegel 2 Zhong Su 1 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 Deployments in Zhejiang Forest University, China Packaging & Enclosure

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  Related work –Network simulators MOTE_VIEW, TOSSIM, NetTopo, TinyViz –Sensor network tools SNA, Surge, SpyGlass, SNAMP –Sensor fault classifications Outliers, spikes, stuck-at, noise –Relevant visualizations GrowthRingMap, SpiralGraph, StarCoordinate Mote View TOS SIM GrowthRingMapStarCoordinate

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, CO 2 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 Geospatial Layout Graph-aesthetic Layout TEM Graph Layout  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  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

T 0,T 1 T1T1 T0T0 T0T0 T1T T1T1 T0T0 T0T0 4 2 T1T sink Original Dynamic Sensor Data Delivery Graph TEM Graph TEM Graph With GrowthRing Renderer TEM Graph Counting Sensor Packets Generated at Each Node Re- route Re- route Node Split Temporal Rendering Native Packets Normal Node Abnormal Node

TEM Graph Visualization  Semantic “overview + detail” approach in the 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 Graph overview Node path to sink Loops Forwarding dynamics Sending dynamics Temporal anomaly ring Minor path 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 CGDelta CG Anomaly CG Sensor Reading CG

High Dimensional Sensor Data Projection  Dimension Projection View –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 Design Basic Projections Drill-down to ValuesTemporal Dynamics

View Coordination  Data filtering through the time range selection on the slider –The TEM graph and the dimension projection view are filtered to the graphs in the selected time range  Data brushing through the node and data dimension selection –Node 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  Node Selection  Dimension Selection Coordinated Multiple View

A Case on Sensor Failure Root Cause Analysis  Identify an anomaly on Node 543  Check the cause of this anomaly  Check the symptom of the parent node  Double-check another possible root cause

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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