April 11, 2023

Modeling the Performance of Wireless Sensor Networks

Carla Fabiana ChiasseriniMichele Garetto

Telecommunication Networks GroupPolitecnico di Torino, Italy

INFOCOM 2004 – Hong Kong

Outline

Network Scenario Our contribution Modelling approach

Sensor model Network model Interference model

Numerical results Conclusions and future work

Network scenario

Large number of self organizing, unattended micro-sensors

Short radio range multi-hop wireless communications towards a common gateway

Energy-limited (battery operated) Sleep/active dynamics

Energy efficiency is the crucial design criterion

Our contribution

Analytical model to predict the performance of a wireless sensor network

Responsiveness (data transfer delay) Energy consumption Network capacity

Our model combines together Sleep / active sensor dynamics Channel contention and interference Traffic routing

An analytical approch to understand fundamental trade-offs and evaluate different design solutions

Modelling approach

Sensed information is organized into data units of fixed length

Time is slotted slot = time needed to transfer a data unit between

two nodes (including channel access overhead) discrete time model

Data units are generated by each sensor at a given rate (during active period)

Data units can be buffered at intermediate nodes (infinite buffers)

Reference scenario

N = 400 sensors

gateway

randomly placed (uniformly) in the disk of unit radius

sensor

System solution

SENSOR MODEL

NETWORKMODEL

INTERFERENCEMODEL

Iterate with a Fixed Point Solution

Model decomposition

SLEEP

ACTIVE

TIME

Buffer

SLOTS

S

R

N

Generation of new data units

Transmission of data units

Reception of data units

Transmission of data units

S R S SR N

empty

Buffernot empty

~ geom(p) ~ geom(q)

Sensor model: assumptions

Sensor model

Unknown parameters: : probability to receive a data unit in a time slot : probability to transmit a data unit in a time slot

Computes: Probabilities of

phases R,S,N Average data

generation rate Sensor throughput Average buffer

occupancy

System solution

SENSOR MODEL

NETWORKMODEL

INTERFERENCEMODEL

Iterate with a Fixed Point Solution

Model decomposition

Network model: assumptions

Each node A maintains up to M routes (according to some routing protocol)

Each route is associated to a different next-hop (a neighbor of A within radio range)

To forward a data unit, node A selects the best next-hop currently available to receive

1

2

3

…zzz…

AExample: M = 3

Network model

The sensor network can be modelled as an open queuing network

Locally generated traffic (computed by the Sensor Model)

Total traffic forwarded by the sensors

Routing matrix

The routing matrix is computed according to routing policy of each sensor, and the sleep/active dynamics of its neighbors

System solution

SENSOR MODEL

NETWORKMODEL

INTERFERENCEMODEL

Iterate with a Fixed Point Solution

Model decomposition

Wireless channel : assumptions

Common maximum radio range r

Ideal CSMA/CA protocol with handshaking (RTS-CTS)

No collisions No wasted slots

Error-free channel At each time slot, all feasible transmissions occur successfully

The only constraint is interference (channel contention)

Interference model

A

B

C

G

I

D

E

F

H

Total interferer Partial interferer

Probability that A can transmit a data unit in a time slot

(parameter of the sensor model)

Analysis of data transfer delay

A separate markov chain is build to compute the transfer delay distribution for each sensor node

The state represents the location of a data unit while moving towards the gateway

Numerical results

N = 400 sensorsRadio range r = 0.25

Number of routes M = 6

Energy consumption:

active mode : 0.24 mJ/slot

sleep mode : 300 nJ/slot

sleep active transition : 0.48 mJ

transmission/reception of data units:

0.24 mJ/slot 0.24 mJ/slot0.057 mJ/slot

0

2

4

6

8

10

12

14

16

18

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

data

delivery

dela

y (

slo

ts)

distance from sink

sim - load = 0.4

mod - load = 0.4

sim - load = 0.9

mod - load = 0.9

Average transfer delayfor 40 different sensors (p = q = 0.1)

0.001

0.01

0.1

0 10 20 30 40 50 60

pd

f

data delivery delay (slots)

sim - load = 0.4mod - load = 0.4

sim - load = 0.9mod - load = 0.9

Transfer delay distribution for the farthest sensor (p = q = 0.1)

1

10

100

0.1 1 100

0.05

0.1

0.15

0.2

0.25

0.3

q/p

energycons. [mJ]

sim

mod

delay[slot]

simmod

qp

SLEEP

ACTIVE

Energy / delay trade-off (1)

(load = 0.4)

10

15

20

25

30

0.025 0.05 0.1 0.2 0.40.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

p ( = q )

delay[slot]

sim

mod

simmod

Energy / delay trade-off (2)

energycons. [mJ]

(load = 0.9)

Conclusions and future work

We have developed an analytical model of a wireless sensor network, capable of predicting the fundamental performance metric and trade-offs

Many possible extensions: Introduction of hierarchy (clusters) Finite buffers and channel errors Congestion control mechanisms More details at the MAC level Impact of node failures network lifetime

References

Carla Fabiana Chiasserini, Michele Garetto, “Modeling the Performance of Wireless Sensor Networks”, IEEE INFOCOM, Hong Kong, March 7-11, 2004

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Average Buffer Occupancy

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Probability of Phase N

y = x

Sensor model validation

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sensor throughput

Network model validation

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Interference model validation

Assumptions - CSMA/CA (RTS/CTS)

…zzz…

…zzz…

…zzz…A

B

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DE

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RTS

CTS

Modern Sensor Nodes

UC Berkeley: COTS Dust

UC Berkeley: COTS DustUC Berkeley: Smart Dust

UCLA: WINS Rockwell: WINS JPL: Sensor Webs

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Interference model validation

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Interference model validation