Energy Efficient Data Collection In Distributed Sensor Environments

Qi Han, Sharad Mehrotra, Nalini Venkatasubramanian

{qhan, sharad, nalini} @ics.uci.edu

QUASAR Project University of California, Irvine

School. of Information & Computer Science

2

Ubiquitous Sensor Environments

Sensor Networks

Battlefield MonitoringHabitat Monitoring

Earthquake Monitoring

Oceanographic current monitoring

Medical Condition Monitoring

Traffic Congestion Detection

Target Tracking Intrusion Detection

Video Surveillance

• Generational advances to computing infrastructure– sensors will be everywhere

• Continuous monitoring and recording of physical world and its phenomena– limitless possibilities

• New challenges – limited bandwidth & energy – highly dynamic systems

• System architectures are due for an overhaul– at all levels of the system

networks, OS, middleware, databases, applications

3

Quasar (Quality Aware Sensing Architecture)

• Hierarchical architecture– data flows from producers to

server to clients periodically– queries flow the other way:

• if client cache does not suffice:– query routed to appropriate

server• if server cache does not suffice:

– access current data at producer

– this is a logical architecture• producers could also be clients• a server may be a base station

or a (more) powerful sensor node

• servers might themselves be hierarchically organized

• the hierarchy might evolve over time

server

clientclient cache

server cache and archive

producer & its cacheQ

UE

RY

FL

OW

DA

TA

FL

OW

4

Quasar: Observations & Approach

• Applications can tolerate errors in sensor data– applications may not require exact answers:

• small errors in location during tracking or error in answer to query result may be OK

– data cannot be precise due to measurement errors, transmission delays, etc.

• Communication is the dominant cost – limited wireless bandwidth, source of major energy drain

• Quasar Approach– exploit application error tolerance to reduce communication

between producer and server and/or to conserve energy

– two approaches • Minimize resource usage given quality constraints • Maximize quality given resource constraints

5

This Paper…

• Explore data collection protocols for sensor environments that exploits the natural tradeoff between application quality and energy consumption at the sensors– Consider a series of sensor models that progressively

expose increasing number of power saving states

– For each of the sensor models considered, develop quality-aware data collection mechanisms that ensure quality requirements of the queries while minimizing the resource consumption

6

Data Collection Framework

• If query quality tolerance satisfied at server

– Answer query at the server

• Else

– Probe the sensor

– Sensor guaranteed to respond within a bounded time D

consumer-initiated update

…source-initiated update

consumer-initiated request

query Q1

(A1,D)

query Qm

(Am,D)

i=[li,ui]sensor si

Imprecise data representation

7

Abstract Sensor States

radio mode sensor state

1-radio node 2-radio node

Tx on, Rx off Tx on, Rx on active (a)

Tx off, Rx on listening (l)

Tx off, Rx off sleeping (s)

8

Problem Statement

• Objective: minimize sensor energy consumption in the process of answering all queries– Given user queries with varying accuracy constraints and latency

bound• Formally stated:

• Issues– How to maintain the precision range r for each sensor

• Larger r increases possibility of expensive probes• Small r wastes communication due to source-initiated updates

– When to transition between sensor states • Powering down might not be optimal if we have to power up immediately• Powering down may increases query response time

met) is bound(latency (2)t

met) is constraint(accuracy (1)a S.T.

states)different in idling toduen consumptio(energy

update) initiated-consumer toduen consumptio(energy

updates) initiated-source toduen consumptio(energy

minimize

i

i

D

A

E

PE

PE

E

i

extra

cucu

susu

9

Our Approaches

• We solve the energy optimization problem by solving two sub-problems– Optimize energy consumption by adjusting range

size under the assumption that the state transition is fixed

– Optimize energy consumption by adapting sensor states while assuming that the precision range for sensor is fixed

• Progressively expose increasing number of sensor power saving states– AA: Always Active– AL: Active-Listening– AS: Active-Listening– ALS: Active-Listening-Sleeping

10

The AL(Active-Listening) model

listening activeTa after processing last source-initiated update or probe

Upon first source-initiated update or probe

11

Analysis of the AL Model

re-write sensor energy consumption equation: )(rfEal

sensor state transition probabilities

steady state probabilities:

la PP ,

sensor energy consumption is minimized when

2su

cu

P

P

normalized sensor energy consumption:

),,,( cusulaal PPPPfE

updates initiated-consumer of prob.:

updates initiated-source of prob.:

listening'' beingsensor of prob. :

active'' beingsensor of prob. :

cu

su

l

a

P

P

P

P

probabilities of source- or consumer-initiated updates: )(, rfPP cusu

size interval :r

12

Range Size Adjustment for the AA/AL Model

• Optimal range can be realized by maintaining the probability ratio

• Can be done at the sensor• Assuming that is the ratio of consumer-

initiated update probability to source-initiated update probability:

for source-initiated update:

with probability min{,1}, set r’= r(1+);

for consumer-initiated update:

with probability min{1/,1}, set r’=r/(1+ );

su

cu

P

P

13

The AS Model (Active-Sleeping)

sleeping active

Upon first source-initiated updateor after Ts without traffic

Ta after processing last source- or consumer-initiated update

14

The ALS Model (Active-Listening-Sleeping)

sleeping

listening

active

Upon first source-initiated updateor after Ts

After Tl without traffic

Upon first source-initiated update or probe

Ta after processing last source-initiated update or probe

15

Range Size Adjustment for the AS/ALS Model

• Not possible to express the ratio in terms of other parameters– Need to monitor parameters such as K1, K2 etc.

• Sensor side– Keep track of the number of state transitions of the last k

updates– Piggyback the probability of state transitions with the Kth

update• Server side

– Keep track of the number of sensor-initiated updates and probes of the last k updates

– Upon receiving the Kth update from the sensor• Compute the optimal precision range r• Inform the sensor about the new r

16

Adaptive Sensor State Management

• Consider the AS model for derivation of optimal Ta to minimize energy consumption– Assuming (t) is the probability of receiving a request at

time instant t, the expected energy consumption for a single silent period is

– E is minimized when Ta=0 if requests are uniformly distributed in interval [0, Ta+Ts].

• In practice, learn (t) at runtime and select Ta adaptively– Choose a window size w in advance– Keep track of the last w silent period lengths and

summarizes this information in a histogram– Periodically use the histogram to generate a new Ta

sa

a

a TT

T saasaa

T

a dtETtPCTPCttdtPCtE ])()[()(0

17

Adaptive State Management (Cont.)

• ci : the number of silent periods for bin i among the last w silent periods

• estimate by the distribution which generates a silent period of length ti with probability ci/w

• Ta is chosen to be the value tm that minimizes the energy consumption as follows:

bin 0bin 1

bin 2bin n-1

t0 t1 t2 t3…… tn-1 tn=Ta+Ts

c0

c1

c2

cn-1

samjsma

n

mj

jja

m

j

j

tEttPCtPC

w

ctPC

w

c

m

)(min1

1

18

Performance Study

• Modeling sensor– Sensor values:

• uniformly from the range [-150, 150]; • perform a random walk in one dimension: every second,

the values either increases or decreases by an amount sampled uniformly from [0.5,1.5].

• Modeling queries– query arrival times at the server are Poisson

distributed • mean inter-arrival time = 2 seconds.

– each query is accompanied by an accuracy constraint A

• A=uniform( Aavg(1- Avar ), Aavg(1+ Avar ))

• Aavg =20 (average accuracy constraint)

• Avar=1 (accuracy constraint variation)

19

System Performance Comparison of Proposed Sensor Models

Query Response Time Comparison

0

100

200

300

400

500

600

700

800

AA AL AS ALS

av

era

ge

qu

ery

re

sp

on

e t

ime

(u

s)

Sensor Energy Consumption Comparison

0

2

4

6

8

10

12

14

16

AA AL AS ALS

no

rma

lize

d s

en

so

r e

ne

rgy

c

on

su

mp

tio

n(u

J)

20

Impact of Ta adaptation on System Performance

Impact of Ta Selection on Query Response Time

700

720

740

760

780

800

820

840

static Ta(0) adaptive Taaver

age

qu

ery

resp

on

se t

ime(

us)

Impact of Ta Selection on Sensor Energy Consumption

0

1

2

3

4

5

6

7

8

9

static Ta(0) adaptive Ta

no

rmal

ized

sen

sor

ener

gy

con

sum

pti

on

(uJ)

21

Impact of Range Size Adaptation on System Performance

Impact of Range Size Adjustment on Query Response Time

0

500

1000

1500

2000

2500

fixed(0) average accuracyconstraint

adaptiveadjustment

fixed(large)

av

era

ge

qu

ery

re

sp

on

se

tim

e (

ms

)

Impact of Range Size Adjustment on Sensor Energy Consumption

0

0.01

0.02

0.03

0.04

0.05

fixed(0) average accuracyconstraint

adaptiveadjustment

fixed(large)

no

rma

lize

d s

en

so

r e

ne

rgy

co

ns

um

pti

on

(uJ

)

22

Conclusions

• Explored the tradeoff between sensor data accuracy and energy consumption for sensor data collection in distributed sensor environments

• Both theoretical analysis and experimental results validated the effectiveness of our approaches– The AS model consumes the least amount of sensor energy– Our proposed strategies of adaptive sensor state transition

reduce energy consumption to a great extent– Optimized range size adjustment works effectively with

corresponding sensor models and saves more energy than using static range or instantaneous values