Fuzzy Data Collectionin Sensor Networks

Lee CranfordMarguerite Doman

July 27, 2006

Overview

Overview of Sensor Networks Sensor Network Applications Research Objective Prototype Platform Proposals Our Modifications Ongoing Work

Wireless Sensor Networks

A collection of small hardware devices that collect data from their environment

Research challenges Energy efficiency Data collection Communications

overhead

Wireless Sensor Networks

Common application:Environmental monitoring Example: Controlled

prairie burning

Sensors can reportmajor temperaturechanges

The spread of a firecan be monitored

Research Objective

Long-term: Use fuzzy query and database management approach to data collection

This summer: Modify the operating system of prototype sensor motes to support an approximate (“fuzzy”) attribute

(value ± margin)

Prototype Hardware: MICA Motes

Prototype sensors developed by UC Berkeley to support sensor networking research

Sensors: Light, temperature, barometric pressure, seismic, sound, magnetic, GPS, and others

RF Communications

TinyOS: “Lite” embedded OS

TinyDB: “Lite” DBMS

Prototype Platform: TinyDB

TinyDB is a sensor network data collection system Allows for polling of sensors through Structured

Query Language (SQL) The sensor network is therefore abstracted to resemble a

relational database in its interface to the user

TinyDB's SQL dialect is in a very stripped-down, “working proof of concept” form called TinySQL

Benefits: Ease of use, eliminates the “API approach” sensor polling, can poll the whole network easily

Problem

In the prairie fire scenario, we want to know where dramatic rises and falls in temperature occur

TinySQL supports polling of a mote's temperature and network averaging

However, it relies oncentral processing to identify local trends

The result is unnecessary transmission of data from areas not undergoing a change

Proposals

What if we could tell the network to only return results that were outside of ordinary trends? Push data processing to the mote

Develop local “threshold” values based on long-term node measurements

Extend TinySQL to support fuzzy queries This allows us to ask the network, “Where is it

hotter than usual? Where is it cooler?”

Development and Simulation

Installed and customized TinyOS on a Linux platform

Installed and evaluated six simulators Selected PowerTOSSIM Set up a simulation environment to

evaluate the energy efficiency of queries

Designed TinyFSQL’s syntax and methods of operation

Code Modifications

Operating system additions

Utilized data storage at the mote level

Implemented mote routines to return data only if present values are outside the current range

Code Modifications

Extended TinyDB Added an attribute to TinySQL to

interface with local mote trends

Implemented the “UPDATE” keyword to force changes of local averages

Added the “fuzzy equal” operator

Work in Progress

Completion of TinyFSQL Add a greater range of fuzzy operands to

the TinyDB parser generator source

Modification to the Java GUI to include user-friendly selection of fuzzy attributes

Extensive tests using the PowerTOSSIM simulator Compare the energy efficiency of TinyFSQL to

TinySQL