the cougar approach to in-network query processing in sensor networks

Presented By: Dilini A. MuthumalaSupervised By: Dr. Jeevani Goonetillake

The Cougar Approach to In-Network Query Processing

in Sensor Networks

Authors

Yong Yao

• Software Engineer at Google

• Ph.D., Computer ScienceCornell University (2000 – 2007)

• Research Interests– Databases– Sensor Networks– Distributed Systems

Johannes Gehrke

• University ProfessorDepartment of Computer Science

Cornell University

• Research Interests

–Scalability in Computer Games and Simulations–Data Privacy

–Data Mining

Motivation

• “Database Abstraction Layer” for Sensor Networks

• Most popular sensor data management middleware

• Introduces Database Abstraction Layer Concept

• Cited by 1185 (source: Google Scholar)No. of citations

Year

Presentation Outline

• Introduction

• Database Abstraction Layer

• Architecture

• Research Problems

• Conclusion

Introduction

Wireless Sensor Network (WSN)

Limitations

• Communication

• Power Consumption

• Computation

• Uncertainty in Sensor Readings

WSN Applications

• Smart Buildings, Smart Homes

WSN Applications

• Wild Life Monitoring

WSN Applications

• Monitoring Vineyards

Future of WSN

Is Johannes in his

office?

Internet

Future of WSN

Temperature

Humidity

Light

Motivation

1) Declarative queries are suited for WSN interaction

SELECT TempFROM sensors

Complex Network

Motivation

2) Increasing network lifetime is the major goal of any WSN application

WSNData Repository for

offline analysis

Database Abstraction Layer

Database Abstraction Layer

WSNWSNBase Station

SELECT Temp, Humid, NodeIDFROM sensorsSAMPLE PERIOD 5s

Node ID Temperature Humidity

1 127 44

2 119 47

3 120 45

4 123 40

5 120 46

Database Abstraction Layer

• Local computations are much cheaper than communication– Pushing partial computations out into the

network

Database Abstraction Layer

• Retrieves data only upon user demand

• No offline data storage

• Energy Efficient

Architecture

Architecture - Overview

Query OptimizerQuery Proxy Layer

Query Proxy Layer

Application Layer

Query Proxy Layer

Routing Layer

Other Layers

Query Optimizer• Generates “Query Processing Plans”

• Refers to– Catalog Information– Query Specification

• Specifies– Data Flow between sensors– Computation Plan

• Finally, plan is disseminated to all sensors

Example

User Query

“Notify when the average temperature

exceeds 35 °C”

WSNWSN

Query Optimizer

“Notify when the average temperature

exceeds 35 °C”

Query Optimizer

Query Plan

Query Plan (QP)

• Designates the Leader node– Where average value will be finalized

Query Plan

Leader Node

Query Plan (QP)

• Two computation plansi. Leader Node

ii. Non-Leader Nodes

Query Plan

QP for Non-Leader Node

Non-Leader Node

QP for Non-Leader Node

Sensor Scan

Network Interface

In-network Aggregation

QP for Non-Leader Node

Sensor Scan

Network Interface

In-network Aggregation

1

Temperature = 38 °C

QP for Non-Leader Node

Sensor Scan

Network Interface

In-network Aggregation

2

Temperature = 38 °C

QP for Non-Leader Node

Sensor Scan

Network Interface

In-network Aggregation

QP for Non-Leader Node

Sensor Scan

Network Interface

In-network Aggregation

2

Temperature = 38 °C

AVG Temperature = 35 °CContributor Count = 1AVG Temperature = 36 °CContributor Count = 1

QP for Non-Leader Node

Sensor Scan

Network Interface

In-network Aggregation

Temperature = 38 °C

AVG Temperature = 35 °CContributor Count = 1AVG Temperature = 36 °CContributor Count = 1

In-Network Aggregation

Total Temperature = 35*1 + 36*1 + 38

= 109

No of Contributors = 3

AVG Temperature = 109 / 3

= 36.33

Temperature = 38 °C

AVG Temperature = 35 °CContributor Count = 1

AVG Temperature = 36 °CContributor Count = 1

AVG Temperature = 36.33 °CContributor Count = 3

QP for Non-Leader Node

Sensor Scan

Network Interface

In-network Aggregation

AVG Temperature = 36.33 °CContributor Count = 3

Towards the Leader

QP for Leader Node

Leader Node

QP for Leader Node

Aggregate Operator (AVG)

Network Interface

Select AVG > threshold

Towards the Leader

Average Value

Partially aggregated results

QP for Leader Node

Aggregate Operator (AVG)

Network Interface

Select AVG > threshold

Towards the Leader

Average Value

Partially aggregated results

1

QP for Leader Node

Leader Node

AVG Temperature = 36.33 °CContributor Count = 3

AVG Temperature = 39 °CContributor Count = 2

QP for Leader Node

Aggregate Operator (AVG)

Network Interface

Select AVG > threshold

Towards the Leader

Average Value

Partially aggregated results

AVG Temperature = 36.33 °CContributor Count = 3

AVG Temperature = 39 °CContributor Count = 2

Aggregate Operator

Total Temperature = 39*2 + 36.33*3 = 186.99

No of Contributors = 5

AVG Temperature = 186.99 / 5

= 37.40

AVG Temperature = 36.33 °CContributor Count = 3

AVG Temperature = 39 °CContributor Count = 2

AVG Temperature = 37.40 °C

QP for Leader Node

Aggregate Operator (AVG)

Network Interface

Select AVG > threshold

Towards the Leader

Average Value

Partially aggregated results

AVG Temperature = 37.40 °C

QP for Leader Node

Aggregate Operator (AVG)

Network Interface

Select AVG > threshold

Towards the Leader

Average Value

Partially aggregated results

AVG Temperature = 37.40 °C

Threshold = 35 °C“Notify when

the average temperature exceeds 35 °C”

WSNWSN

User Query Result

ALERT!Temperature exceeds 35 °C

Research Problems

1. Aggregation

• Most popular computation and communication pattern

• Two important issues– Leader Selection– Data Delivery

Leader Selection

Requirements for the policyi. Dynamically-maintained Leader

ii. Physically advantageous location

Leader Selection

Requirements for the policyi. Dynamically-maintained Leader

ii. Physically advantageous location

Data Delivery

“How should the data be delivered from source nodes to the leader?”

– Send all data to leader?– Should intermediate nodes participate?

2. Query Language

“What types of queries should be supported?”

3. Query Optimization

• Cost of query plan has changed

• Energy should be the focus

• Reactive to changes in catalog information– Changes in topology– Power level at sensor nodes

4. Catalog Management

• Maintained at the server

• Provides Meta Data about the network

• Question: What is the best way to main the catalog?

5. Multi-Query Optimization

• Occurs when the WSN is shared

• Users may pose similar queries

• Share common data among the users

Conclusion

• Interacting with a WSN is made easy

• Database Abstraction layer provides– Friendly Interface

– Efficient scheme to reduce energy consumption

• Research problems need to be carefully addressed

My Views on the Paper

• Presents a concept

• Easy-to-understand

• Flow of the paper sometimes confuse the reader

Q & A