Contour Map Matching for Event Detection in Sensor Networks

Wenwei XueJoint Work with Qiong Luo, Lei Chen

and Yunhao Liu

Department of Computer Science and EngineeringHong Kong University of Science and Technology

SIGMOD 2006 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

• 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

Our Proposal

a spatio-temporalpattern

an event

Pattern MatchingEvent Detection

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 or contour

• Map Snapshot Instance of the contour map at a specific time

SIGMOD 2006 6

Contour Mapping on a 2x2 Grid

contours in a map snapshotSpatial pattern

evolution of contours along timeTemporal pattern

contour mapsSpatio-Temporal pattern

Partial map

Map snapshot

Contour region unit

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 mapping

Contour map matching

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

0

2 3

1

temp = 40 C

temp = 30 C

temp = 30 C

temp = 30 C 4

5 7

6

temp = 40 C

temp = 30 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

• On each non-leaf node: The error bound ij of merging a pair of adjacent or

overlapping regions (Ri, Rj) is computed as:

Estimation of Merging Error Bound

)1(*)"'( ijijijij

Ri Rj

ji

Rj

jij

Ri

iij

ij

dσyxfdσyxf

dσyxfyxfdσyxfyxf

),(),(

)),(),(()),(),((

'

Ri Rj

ji

Ri Rj

jjii

ij

dσyxfdσyxf

dσyxfdσyxf

),(),(

),(),("

**

)*

*),min(,0max(

grid

gridjiij

σp

σpσσ

Region area

Regression function

Error bound

Penalty factor

SIGMOD 2006 14

Algorithm for Contour Region Merging

ij

Ri Rj Rk

fk(x,y): incremental recomputation

k ij

RlRm

Rn

lm / lm > ln / ln

Merge (Rl, Rm) first

Two non-mergeable, overlapping regions

Remove Rw from Ru

Ru

Rv

sizeof(Ru – Rw) + sizeof(Rv) <

sizeof(Rv – Rw) + sizeof(Ru)

Rw

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%

0

10

20

30

40

QC1 QC2 QC3 QC4Query Class

Net

wo

rk T

raff

ic (M

B)

ORI

CCS

SNP

IUS

INLR

SIGMOD 2006 18

Accuracy of Three Approaches

• All approaches achieve 100% precision consistently

• INLR achieves comparable accuracy to SSLR and outperforms INEB

0%

20%

40%

60%

80%

100%

0% 10% 20% 30% 40%

Link Loss Rate

Re

ca

ll INLR

INEB

SSLR

SIGMOD 2006 19

Network Traffic of Three Approaches

0

2

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8

10

0% 10% 20% 30%

Event Frequency

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two

rk T

raff

ic (

MB

)

INLR

INEB

SSLR

0

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8

0% 10% 20% 30% 40%

Link Loss Rate

Net

wor

k Tr

affic

(MB

)

INLR

INEB

SSLR

(a) (b)

(c) (d)

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4

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12

16

0 10 20 30 40

Transmission Range (meter)

Net

wo

rk T

raff

ic (M

B)

INLR

INEB

SSLR

0

20

40

60

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100

0 100 200 300

Network Diameter (meter)

Ne

two

rk T

raff

ic (

MB

)

INLR

INEB

SSLR

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