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![Page 1: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/1.jpg)
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
![Page 2: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/2.jpg)
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Agenda
Storage-centric wireless sensor networks Formulation of multi-resolution data
disseminationOnline tree construction and adaptationPerformance evaluationConclusions
![Page 3: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/3.jpg)
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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
![Page 4: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/4.jpg)
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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!
![Page 5: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/5.jpg)
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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
![Page 6: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/6.jpg)
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Agenda
Storage-centric wireless sensor networks Formulation of multi-resolution data
disseminationOnline tree construction and adaptationPerformance evaluationConclusions
![Page 7: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/7.jpg)
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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
![Page 8: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/8.jpg)
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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
![Page 9: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/9.jpg)
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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
![Page 10: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/10.jpg)
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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
![Page 11: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/11.jpg)
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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
![Page 12: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/12.jpg)
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Agenda
Storage-centric wireless sensor networks Formulation of multi-resolution data
disseminationOnline tree construction and adaptationPerformance evaluationConclusions
![Page 13: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/13.jpg)
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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
![Page 14: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/14.jpg)
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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
![Page 15: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/15.jpg)
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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!
![Page 16: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/16.jpg)
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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
![Page 17: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/17.jpg)
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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*
![Page 18: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/18.jpg)
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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
![Page 19: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/19.jpg)
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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
![Page 20: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/20.jpg)
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Agenda
Storage-centric wireless sensor networks Formulation of multi-resolution data
disseminationOnline tree construction and adaptationPerformance evaluationConclusions
![Page 21: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/21.jpg)
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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
![Page 22: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/22.jpg)
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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]
![Page 23: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/23.jpg)
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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
![Page 24: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/24.jpg)
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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
![Page 25: Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks Hongbo Luo; Guoliang Xing; Minming Li; Xiaohua Jia Department of.](https://reader036.fdocuments.us/reader036/viewer/2022062719/56649eec5503460f94bfd2d0/html5/thumbnails/25.jpg)
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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