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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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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





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





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

)(rELH

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

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

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





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)( sTdE

sT

s

s

TdsT

dEsd0

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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:

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*

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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

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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

|)(

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)