delay analysis of large-scale wireless sensor networks jun yin, dominican university, river forest,...
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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
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
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Internet phones
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.
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
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.
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
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
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
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
)(rE
LH
0r
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
ddrP
rE
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
Hop count simulations
Hop count between various S/D distanceIt shows that our analysis can provide a better approximation on hop count than .
r
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
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)( sT
dE
sT
s
s
Tds
TdEsd
0
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1)]([)(
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Random source/dest traffic
Hop count between random S/D pairs
22
2
4)(
22
4/
LLL
P DS
Distance distribution between random S/D pairs in a square area of L*L:
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:
,*
11 LL
N
LL
N
*2
2
2
!
)(),,(
2r
m
em
rrmP
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
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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
|)(
rF
T
dvrF
vvP
T
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)