delay analysis of large-scale wireless sensor networks
DESCRIPTION
Delay Analysis of Large-scale Wireless Sensor Networks. Jun Yin, Dominican University, River Forest, IL, USA, Yun Wang, Southern Illinois University Edwardsville, USA Xiaodong Wang, Qualcomm Inc. San Diego, CA, USA. Outline. Introduction Delay analysis Hop count analysis - PowerPoint PPT PresentationTRANSCRIPT
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Delay Analysis of Large-scale Wireless Sensor Networks
Jun Yin, Dominican University, River Forest, IL, USA, Yun Wang, Southern Illinois University Edwardsville, USA
Xiaodong Wang, Qualcomm Inc. San Diego, CA, USA
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Outline
IntroductionDelay analysis
– Hop count analysis One –dimensional Two –dimensional
– Source – destination delay analysis Random source –destination Delay from multi-source to sink
– Flat architecture– Two-tier architecture
Conclusion
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“Cool” internet appliances
World’s smallest web serverhttp://www-ccs.cs.umass.edu/~shri/iPic.html
IP picture framehttp://www.ceiva.com/
Web-enabled toaster +weather forecasterhttp://news.bbc.co.uk/2/low/science/nature/1264205.stm
Internet phones
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Wireless Sensor network : The next big thing after Internet
Recent technical advances have enabled the large-scale deployment and applications of wireless sensor nodes.
These small in size, low cost, low power sensor nodes is capable of forming a network without underlying infrastructure support.
WSN is emerging as a key tool for various applications including home automation, traffic control, search and rescue, and disaster relief.
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Wireless Sensor Network (WSN)
WSN is a network consisting of hundreds or thousands of wireless sensor nodes, which are spread over a geographic area.
WSN has been an emerging research topic– VLSI Small in size, processing capability– Wireless Communication capability– Networking Self-configurable, and coordination
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WSN organization
Flat vs. hierarchical Homogenous vs. Heterogeneous
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Delay is important for WSN
It determines how soon event can be reported.
Delay is determined by numerous network parameters: node density, transmission range; the sleeping schedule of individual nodes; the routing scheme, etc.
If we can characterize how the parameters determine the delay, we can choose parameters to meet the delay requirement.
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Outline
IntroductionDelay analysis
– Hop count analysis One –dimensional Two –dimensional
– Source – destination delay analysis Random source –destination Delay from multi-source to sink
– Flat architecture– Two-tier architecture
Conclusion
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Our approach
Firstly, we try to characterize how network parameters such as node density, transmission range determine the hop count;
Then we consider typical traffic patterns in WSN, and then characterize the delay.
Random source to random destinationData aggregation in two-tier clustering architecture
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Outline
IntroductionDelay analysis
– Hop count analysis One –dimensional Two –dimensional
– Source – destination delay analysis Random source –destination Delay from multi-source to sink
– Flat architecture– Two-tier architecture
Conclusion
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Modeling
Randomly deployed WSN is modeled as:– Random geometric graph– 2-dimensional Poisson distribution
Nodes are deployed randomly. The probability of having k nodes located with in
the area of around the event :2sr
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Shortest path routing: One dimensional case
At each hop, the next hop is the farthest node it can reach.
0rL
0][1][ rerPrP
0][ rerP
01][ 0
rerrE
:Transmission ranger: per-hop progress
)(rELH
0r
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Two-dimensional case
Per-hop progress
0r
1r
1
2
2r
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Average per-hop progress in 2-D case
220][1][ rePP
2202][ reP
0 0
0
cos][][
r
ddrPrE
Average per-hop progress as node density increases
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Numeric and simulation results
Hop count between fixed S/D distance under various transmission rangeIt shows that our
analysis can provide a better approximation on hop count than .
0r
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Hop count simulations
Hop count between various S/D distanceIt shows that our analysis can provide a better approximation on hop count than .
r
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Outline
IntroductionDelay analysis
– Hop count analysis One –dimensional Two –dimensional
– Source – destination delay analysis Random source –destination Delay from multi-source to sink
– Flat architecture– Two-tier architecture
Conclusion
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Per-hop delay and H hop delay
In un-coordinated WSN, per-hop delay is a random variable between 0 and the sleeping interval (Ts).
Per-hop delay is denoted by d:
2)( sTdE
sT
s
s
TdsT
dEsd0
22
121)]([)(
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Random source/dest traffic
Hop count between random S/D pairs
22
24)(
22
4/
LLL
P DS
Distance distribution between random S/D pairs in a square area of L*L:
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Heterogeneous WSN
Sensor nodes might have different capabilities in sensing and wireless transmission.
http://intel-research.net/berkeley/features/tiny_db.asp
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Random deployment of heterogeneous WSN
N1 = 100N2 = 300L = 1000m
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Modeling
The deploying area of WSN: a square of (L*L).
The probability that there are m nodes located within a circular area of is:
Node density of Type I and Type II nodes:,
*1
1 LLN
LL
N*
22
2
!)(),,(
2r
m
emrrmP
2r
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2-tier structure
Clusterhead
Type II node chooses the closest Type I node as its clusterhead:
Voronoi diagram
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Distance distribution
PDF of the distance to from Type II sensor node to its clusterhead
21
12)( evP
Distance distribution between a Type II sensor node to its closest Type I sensor node:
1
2)(
vE
Average distance:
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Average delay in 2-tier WSN
120
2
0 20
),,(
),,()(
2
|)(
rFT
dvrFvvPT
hHdEEDE
s
Ls
Average delay:
Per-hop progress
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Summary on delay analysis
The relationship between node density, transmission range and hop count is obtained.
Per-hop delay is modeled as a random variable.
Delay properties are obtained for both flat and clustering architecture.
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Conclusion
Analysis delay property in WSN;It covers typical traffic patterns in
WSN;The work can provide insights on
WSN design.
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Thanks.
Questions?
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Random source to central sink node
Laptop computer
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Incremental aggregation tree
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Hop count analysis (Key assumptions)