Contour Map Matching for Event Detection in Sensor Networks

1. Contour Map Matching for Event Detection in Sensor Networks Wenwei Xue Joint Work with Qiong Luo, Lei Chen and Yunhao Liu Department of Computer Science and Engineering Hong Kong University of Science and Technology

2. Surveillance Applications of WSNs • Monitor physical-world events of interest • Examples: gas leakage, object appearance • A case study: coal mine surveillance • Hundreds of sensor nodes deployed in a coal mine • Measure the density of gas, dust and oxygen • Measure temperature, humidity and structural integrity • Two common classes of event detection tasks: • Gas, dust and water leakage detection • Oxygen density monitoring SIGMOD 2006

3. Threshold-Based Event Detection • Typical in recent work on sensor databases • Set thresholds in query predicates • An event is regarded as occurred when: • Sensor readings exceed a threshold value • Example query: • SELECT nodeid FROM sensors WHERE gas_density > 20% • Defects • Unable to fully express many events • Difficult to specify suitable threshold values SIGMOD 2006

4. an event a spatio-temporal pattern Event Detection Pattern Matching Our Proposal • Pattern-based event detection • Based on a main observation obtained from: • Various field studies • Analysis of real-world datasets collected • Integrated with distributed sensor query processing SIGMOD 2006

5. Contour Map • Topographic map over the whole sensor network • Display value distribution of an attribute • E.g., temp, gas_density, oxy_density • Partition of geometric space occupied by the network • Consist of disjoint contour regions • A contour region: • Contain adjacent nodes of similar readings • Bounded by contour line orcontour • Map Snapshot • Instance of the contour map at a specific time SIGMOD 2006

6. Spatial pattern contours in a map snapshot Temporal pattern evolution of contours along time Spatio-Temporal pattern contour maps Contour Mapping on a 2x2 Grid Contour region unit Map snapshot Partial map SIGMOD 2006

7. Pattern-Based Event Specification • Definition of general events • A time series with equal time interval between elements • Each element is a user-specified partial map • Definition of three common types of events • Derived from the coal mine surveillance application Gas leakage Water leakage Place with dense oxygen SIGMOD 2006

8. Event-Driven Queries • Extension of SQL-based sensor query language • Adopted in TinyDB, Cougar • Encapsulate events as Boolean methods • Query predicates in the WHERE clause • Encapsulate contour mapping as table-valued functions • Virtual tables in the FROM clause • Example: SELECT alarm() FROM contour_map(gas_density, 0.3, 0.5) c WHERE pyramid(c.snapshot, “gas_leakage.xml”) SAMPLE PERIOD 2 min SIGMOD 2006

9. Event-Oriented Query Processing Server Query Contour map matching Contour mapping SIGMOD 2006

10. In-Network Map Construction • Motivation • Communication dominates power consumption • Centralized data collection is energy-inefficient • Assumptions: [Hellerstein et al. 2003] • Static sensor network with known node locations • A rectangular m*n grid with cell length l • At most one node inside each cell • A special kind of data aggregation • Data aggregated on a node: partial map • Contour map of the sub-network rooted at the node • Multi-path, ring-based routing SIGMOD 2006

11. Partial Map Aggregation temp = 30 C temp = 30 C 0 4 temp = 30 C 1 6 temp = 40 C 3 7 2 5 temp = 30 C temp = 40 C temp = 40 C temp = 40 C SIGMOD 2006

12. Contour Region Merging • Core of partial map aggregation • Previous criterion: equi-width bucket • Our criterion: • Combine attribute value with region area • Mapping accuracy vs. communication cost • Involve two user-specified parameters: • Error bound:   (0, 1) • Merging limit: p  (0, 1] • Associate two variables to each region Ri • Error bound: i • Linear regression model: fi(x, y) = w0 + w1 *x + w2 *y • Merge regions that result in a merging error smaller than  • According to a kind of regression-based error estimation SIGMOD 2006

13. Estimation of Merging Error Bound • On each non-leaf node: • The error bound ij of merging a pair of adjacent or overlapping regions (Ri, Rj) is computed as: Region area Penalty factor Regression function Error bound SIGMOD 2006

14. Algorithm for Contour Region Merging ij   fk(x,y): incremental recomputation Ri Rj Rk k ij lm /lm> ln / ln Two non-mergeable, overlapping regions Rw Rm Ru Rl Remove Rw from Ru Rv Rn sizeof(Ru – Rw) + sizeof(Rv) < sizeof(Rv – Rw) + sizeof(Ru) Merge (Rl, Rm) first SIGMOD 2006

15. Schemes for Communication Saving • Contour compression • Eliminate inner region boundaries • Store vertices on each outer boundary interleavingly • Optimization of map transmission • Based on packet snooping • Suppress the transmission of redundant regions • Incremental map update • Cache old maps used in previous sample period • Construct new map based on cached and delta data SIGMOD 2006

16. Experimental Setup • Homegrown sensor network simulator • Simulated application scenario: coal mine surveillance • Data generation: synthetic datasets • Three attributes: gas_density, oxy_density, humidity • Preserve characteristics of a real-world dataset • Query workload • Four classes of queries: QC1-QC4 • Represent the four types of events we define • Approach compared • INLR (In-Network Linear Regression) • INEB (In-Network Equi-width Bucket) • SSLR (Server-Side Linear Regression) SIGMOD 2006

17. Efficiency of Our Approach • Network traffic saving achieved by individual schemes: • CCS: 25%, SNP: 55%-70%, IUS: 65%-70% • Total saving of network traffic by combining all three: • INLR: 90% SIGMOD 2006

18. Accuracy of Three Approaches • All approaches achieve 100% precision consistently • INLR achieves comparable accuracy to SSLR and outperforms INEB SIGMOD 2006

19. Network Traffic of Three Approaches (a) (b) (c) (d) SIGMOD 2006

20. Conclusion • Pattern-based event detection for WSNs • Matching user-specified patterns with contour maps • Energy-efficient in-network contour mapping • Pattern-based definitions to events • Integration with distributed sensor query processing • Future work • Real prototype implementation and evaluation • Revision of pattern-based event specification • Evaluation with patterns generated by real-world events SIGMOD 2006