Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of

Dynamic Multi-resolution Data Dissemination in Storage-centric

Wireless Sensor Networks

Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia

Department of Computer Science

City University of Hong Kong

2

Agenda

Storage-centric wireless sensor networks Formulation of multi-resolution data

disseminationOnline tree construction and adaptationPerformance evaluationConclusions

3

Storage-centric Sensor Nets

Many applications are data-intensive [Ganesan03]Structure health monitoring

Accelerometer@100Hz, 30 min/day, 80Gb/yearMicro-climate and habitat monitoring

Acoustic & video, 10 min/day, 1Gb/year

Store most data in networkStorage has low cost and power consumption16~512 MB/sensor is recently demoed

Answer user queries on demandEach storage node creates a data dissemination tree

4

Dynamic Multi-resolution Data Dissemination

Requests have different temporal resolutions"report temperature readings every 1 minute""report light readings every 2 minutes"

Requests are dynamicNew requests can arrive anytimeData rates of existing requests can change

Optimal dissemination tree is not fixed!

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Why Are Data Rates Important

Data rate determines total power cost Radio power cost varies in different states

TX: 21.2~106.8 mW, RX and idle: 32 mW, Sleeping: 0.001 mW

Total energy cost is sum of power in each state weighted by the working time

Exploring diversity of rates reduces power due to broadcast wireless channel

6

Agenda



7

An Example of Minimizing Total Radio Power

a sends to c at normalized rate of

r = data rate/bandwidthTwo network configurations

a →c, b sleeps a → b → c

AssumptionsOnly source and relay nodes remain activea→c has the worst quality

c(a,c) > c(a,b) and c(b,c)c(x,y) is expected num of TXs from node x to y

a

c

b

8

rxrxtx PPcacrPcacrcaP )),(1(),()(

Average Power Consumption

zcbbarcbaP 3]),(),([)(

a

b

c

a’s avg. power c’s avg. power

Configuration 1: a → c, b sleeps

zcar 2),(

rx

rxtx

Pz

cacPPca

),()(),(

Configuration 2: a → b → c

θ(a,c)

θ(b,c)

θ(a,b)z

z

z

9

Optimal Network Configuration

bandwidth

ratedata

Transmission power dominates: use short and reliable links

Idle power dominates:use long (but lossier) links since more nodes can sleep

)( caP

)( cbaP

3z

2z

Pow

er C

onsu

mpt

ion

r0 1

),(),(),(0 cbbaca

zr

10

Modeling Broadcast Advantage

source

t1, r1

t2, r2

u

),(max)( iis

turzuPi

Considering both ut1 and ut2

z is only counted onceTake the max of riθ(u,ti) for all sinks

θ(u,v1) θ(u,v2)

Considering us1 only

),()( 11 turzuP

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Min-power Multi-resolution Data Dissemination (MMDD)

Given traffic demands I={(ti , ri )} and G(V,E), find a tree T(V´, E´) minimizing

zV |'|

Sleep scheduling + power-aware multicastMMDD is NP-Hard

node cost, independent of data rate

),(max)(

)(),(vuri

ududtucv i

d(u): set of decedents of u

c(u): set of children of u

12

Agenda



13

Online Incremental Tree Algorithm

When a new sink t with rate r comesAssign each edge (u,v) a cost

z+r θ(u,v), if (u,v) not on existing tree

(r θ(u,v) - max riθ(u,vi))+, otherwise

Find the shortest path from source to t

Theorem: total power cost ≤ |D| times of power cost of optimal tree found offlineD is num of requests arrived so far

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Lightweight Tree Adaptation

When data rates of existing requests change Power efficiency of a tree degradesConstructing a new tree is expensive

Path-quality based tree adaptationMonitor the quality of each pathFind a new path if quality drops below a threshold

Reference-rate based tree adaptationMonitor the reference of all data ratesFind a new tree if reference exceeds a threshold

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Path Quality Estimation with Increased Data Rate

Yl and Yh are min power from s to t under rl and rh

Found under cost metric z+r θ(u,v)

Theorem I: If the rl drops to rh, then power cost of Yl is no more than the min power under rh by:

Significance: path quality degradation can be estimated solely by known information

zYr

rl

h

l )1( all symbols are known!

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Path Quality Estimation with Increased Data Rate

Theorem II: If rl increases to rh, then power cost of Yl is no more than the min power under rh by

all symbols are known!

),()(),(

vurrlYvu

lh

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Path-quality based Tree Adaptation

Suppose sink ti changes rate from ri to ri*

Computes ∆P, the difference between current power and the min power under ri*

If ∆P×Ti > β, find a new path using ri*, otherwise, continue to use the existing pathβis the energy cost of finding a shortest pathTi is the duration of new rate ri*

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Reference-rate based Tree Adaptation

Find paths using same rate r for all sinksSignificantly reduces the overhead

Theorem: for a set of requests D with rates in [rmin, rmax], the performance ratio is (rmax/rmin)|D|, if rmin ≤ r ≤ rmax holds

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Reference-rate based Tree Adaptation Logic

Source keeps max, min, and avg. rates of all existing requests: rmin, rmax, ravg

When a new request arrives Update rmin, rmax to r’min and r’max

If ravg not in [r’min, r’max], compute new avg. rate r’avg and find a new tree using r’avg

20

Agenda



21

Simulation Environment

Prowler simulator extended by Rmase projectProwler: http://www.isis.vanderbilt.edu/projects/nest/prowler/

Rmase: http://www2.parc.com/spl/projects/era/nest/Rmase/

Implemented USC model [Zuniga et al. 04] to simulate lossy links of Mica2 motes

40 Kbps bandwidth, transmission power of 11.6 mA, idle power of 8 mA

Routing nodes keep active 50s in every 500sSimulated different workload patterns

High, low, mixed, busty data rates

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Simulation I: Fixed Data Rates

Three baseline algorithmsMin transmission count tree (MTT)

Shortest-path tree of expected # of TXs

Transmission count Steiner tree (TST)Approx. min Steiner tree of expected # of TXsSimilar to power-aware multicast algorithms

Data rate Steiner tree (DST)Approx. min Steiner tree based on data ratesSimilar to data dissemination algorithm SEAD [kim03]

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Fixed Data Rates

Low-rate case: each request is randomly chosen within 0.5~2 packets per active window

Mixed-rate case: 1/3 requests are randomly chosen within 20~40 packets per active window

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Rate- vs. Path-based Adaptation

Bursty-rate case: each request alternates bw high (120~200 pkts) and low (120~200 pkts) rates 10 times

Unknown rate duration: Each request randomly changes its rate 10 times; Duration of each rate is randomly chosen from100~1000s

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Conclusions

Multi-resolution data disseminationModels all states of radio, link quality, data

rates, broadcast advantage

An online tree construction algorithmHandles dynamic arrivals of data requests

Two lightweight tree adaptation heuristicsMaintain power-efficiency under dynamic rates

Documents

Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of