1 Impact of Radio Irregularity on Wireless Sensor Networks Gang Zhou, Tian He, Sudha Krishnamurthy, John A. Stankovic Computer Science Department,University

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Impact of Radio Irregularity on

Wireless Sensor Networks

Gang Zhou, Tian He, Sudha Krishnamurthy, John A. Stankovic

Computer Science Department,University of VirginiaJune 2004

2

Outline

Motivation, State of Art and Contributions Analyze Radio Irregularity Radio Irregularity Model (RIM) Impact on Routing and MAC Layer Solutions for Radio Irregularity Conclusion and Future Work

3

Motivation

Evidence of radio irregularity of low power wireless devices in physical environment

Need for models to regenerate radio irregularity in simulations

Need for better protocols to address irregularity in running systems

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State of Art

Spherical radio range assumption in current research

Localization, Sensing Coverage, Topology Control Experiments Related to Radio Irregularity

Deepak Ganesan, etc., “Complex Behavior at Scale: An Experimental Study of Low-Power Wireless Sensor Networks” , UCLA/CSD-TR 02-0013, 2002

Alberto Cerpa, etc., “SCALE: A Tool for Simple Connectivity Assessment in Lossy Environments”, CENS-TR 03-0021, 2003

Jerry Y. Zhao, etc., “Understanding Packet Delivery Performance in Dense Wireless Sensor Network”, ACM SenSys, 2003

Alec Woo, etc., “Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks”, ACM SenSys, 2003

DOI Concept (our previous work) Tian He, etc., “Range-Free Localization Schemes in Large Scale

Sensor Networks”, MobiCom, 2003

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Contributions

RIM: a new radio energy model that considers irregularity

Implemented in GlomoSim Review the impact of radio irregularity on

MAC layer Routing layer

Solutions to deal with radio irregularity Symmetric Geographic Forwarding Bounded Distance Forwarding Bidirectional Flooding Learning Function RTS Broadcast High Energy Asymmetry Detection

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Motivation, State of Art and Contributions Analyze Radio Irregularity Radio Irregularity Model (RIM) Impact on Routing Layer Impact on Routing and MAC Layer Solutions for Radio Irregularity Conclusion and Future Work

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Radio signal properties - 1

Non-isotropic Path Loss: The radio signal from a transmitter has different path losses in different directions.

-65-64-63-62-61-60-59-58-57-56-55

0 25 50 75

Beacon SeqNo

South NorthWest East

Figure 1: Signal Strength over Time in Four Directions

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Non-isotropic Path Loss

Figure 2: Signal Strength Values in Different Directions

-60

-58

-56

-54

-52

-50

1 48 95 142 189 236 283 330

Direction in Degree ( 10 feet)

-65

-60

-55

-50

-45

0 41 82 122 163 204 245 285 326

Direction in Degree (20 feet)

Reasons: Reflection, diffraction and scattering in environment Hardware calibration differences (non-isotropic antenna

gain)

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Radio signal properties - 2

Continuous variation: The signal path loss varies continuously with incremental changes of the propagation direction from a transmitter.

Figure 2: Signal Strength Values in Different Directions

-60

-58

-56

-54

-52

-50

1 48 95 142 189 236 283 330

Direction in Degree ( 10 feet)

-65

-60

-55

-50

-45

0 41 82 122 163 204 245 285 326

Direction in Degree (20 feet)

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Radio signal property - 3

Heterogeneity: Different nodes have different signal sending powers

-60

-59.5

-59

-58.5

-58

-57.5

-57

0 25 50 75

Beacon SeqNo

1.58V 1.4V1.32V 1.18V

(a) One mote with different battery status

-60-59.5

-59-58.5

-58-57.5

-57-56.5

-56-55.5

-55

0 25 50 75

Beacon SeqNo

Mote A Mote BMote C Mote D

(b) Different motes with the same battery status

Reasons: Different battery status Different hardware calibration

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Motivation, State of Art and Contributions Contributions Analyze Radio Irregularity Radio Irregularity Model (RIM) Impact on Routing Layer Impact on Routing and MAC Layer Solutions for Radio Irregularity Conclusion and Future Work

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RIM - DOI

Degree of Irregularity (DOI): Definition: the maximum received signal strength

percentage variation per unit degree change in the direction of radio propagation.

Account for non-isotropic path loss

Figure 4: Degree of Irregularity

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RIM - VSP

Variance of Sending Power (VSP): Definition: the maximum percentage variance of the

signal sending power among different devices. Account for heterogeneous sending power

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RIM – propagation formula Signal receiving power = signal sending power - path loss + fading

Signal receiving power = signal sending power – DOI adjusted path loss + fading

DOIK-K Where

onDistributi Weibull RandomNum Where

DOI) * RandomNum(1*K2 K3

DOI)*RandomNum(1 * K1 K2

DOI)*RandomNum (1* K0 K1

1 K0

3590

DOI adjusted path loss = path loss* KD

Signal receiving power = VSP adjusted signal sending power – DOI adjusted path loss + fading

VSP adjusted signal sending power =

onDistributi Normal RandomNum Where

VSP)*RandomNum (1 *power sending signal

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Analyze the Impact

Impact on: Path-Reversal technique Multi-Round technique Used in AODV, DSR, LAR

Source A

B Dest.RREQ

RREQ

RREP

RREP

Figure 5: Impact on Path-Reversal Technique

S DX

X

RREQ

RREP

Figure 6: Route Discovery Using Multi-Round Technique

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Analyze the Impact

Impact on: Neighbor-Discovery

technique Used in GF, GPSR, SPEED

AC

D

Bbeacon

Xdata

beacon

data

beacon data

Figure 7: Impact on Neighbor Discovery Technique

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Simulation Configuration

Components Setting

Simulator GloMoSim

Terrain (150m,150m)

Node Number 100

Node Placement Uniform

Payload Size 32 Bytes

Application Many-to-one CBR streams

Routing Protocol AODV, DSR, GF

MAC Protocol CSMA, 802.11 (DCF)

Radio Model RIM

Radio Bandwidth 200Kb/s

Runs 140

Confidence Intervals The 95% confidence intervals are within 0~25% of the mean

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E2E Loss Ratio

0%

10%

20%

30%

40%

50%

60%

70%

0 0.2 0.4 0.6 0.8 1

VSP-FACTOR

AODVDSRGF

0%

10%

20%

30%

40%

50%

60%

70%

0 0.002 0.004 0.006 0.008 0.01

DOI-FACTOR

AODVDSRGF

Increase DOI Increase VSP

GF has rapidly increasing E2E loss ratio AODV and DSR have low E2E loss ratio

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Average E2E Delay

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 0.002 0.004 0.006 0.008 0.01

DOI-FACTOR

AODVDSRGF

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 0.2 0.4 0.6 0.8 1

VSP-FACTOR

AODVDSRGF


GF has constant E2E delay AODV and DSR have increasing E2E delay

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# of Control Packets

0

200

400

600

800

1000

1200

0 0.002 0.004 0.006 0.008 0.01

DOI-FACTOR

AODVDSRGF

0

100

200

300

400

500

600

700

0 0.2 0.4 0.6 0.8 1

VSP-FACTOR

AODVDSRGF


GF has constant # of control packets AODV and DSR have increasing # of control packets

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Energy Consumption

0

1

2

3

4

5

6

7

8

9

0 0.002 0.004 0.006 0.008 0.01DOI-FACTOR

AODVDSRGF

0

1

2

3

4

5

6

7

8

0 0.2 0.4 0.6 0.8 1

VSP-FACTOR

AODVDSRGF


GF has decreasing energy consumption AODV and DSR increasing energy consumption

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Solutions

Symmetric Geographic Forwarding Bounded Distance Forwarding Bidirectional Flooding Learning Function RTS Broadcast High Energy Asymmetry Detection

Symmetric Geographic Forwarding Detect and block asymmetric channels Only use symmetric channels for geographic forwarding Implementation: Add all neighbors’ IDs in beacon messages Optimization: estimate the channel quality statistically

Currently implemented in a tracking system [MobiSys 2004]

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SGF --- E2E Loss Ratio

0%

10%

20%

30%

40%

50%

60%

70%

0 0.002 0.004 0.006 0.008 0.01

DOI-FACTOR

AODVDSRGFSGF

0%

10%

20%

30%

40%

50%

60%

70%

0 0.2 0.4 0.6 0.8 1

VSP-FACTOR

AODVDSRGFSGF


SGF has constantly low E2E loss ratio

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SGF --- Average E2E Delay

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 0.002 0.004 0.006 0.008 0.01

DOI-FACTOR

AODVDSRGFSGF

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 0.2 0.4 0.6 0.8 1

VSP-FACTOR

AODV DSRGF SGF


SGF has almost constant E2E delay

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SGF --- # of Control Packets

0

200

400

600

800

1000

1200

0 0.002 0.004 0.006 0.008 0.01

DOI-FACTOR

AODVDSRGFSGF

0

100

200

300

400

500

600

700

0 0.2 0.4 0.6 0.8 1

VSP-FACTOR

AODV DSRGF SGF


SGF has the same # of control packets as that of GF

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SGF --- Energy Consumption

0

1

2

3

4

5

6

7

8

9

0 0.002 0.004 0.006 0.008 0.01

DOI-FACTOR

AODV DSRGF SGF

0

1

2

3

4

5

6

7

8

0 0.2 0.4 0.6 0.8 1

VSP-FACTOR

AODV DSRGF SGF


SGF has a little increasing energy consumption

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Bounded Distance Forwarding Bounded Distance Forwarding restricts the distance over

which a node can forward a message in a single hop. An add-on rule Tested in a running system with 60 MICA2 motes

60%

65%

70%

75%

80%

85%

90%

95%

100%

8 16 24 32 40 48 100

Bounded Fowarding Distance(feet)

Figure 7: Percentage of Reporting Nodes

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Conclusion - 1 The first effort to bridge the gap:

between isotropic radio energy models assumed by most simulators in WSN and the real non-isotropic

radio properties

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Conclusion - 2 Review the impact of radio irregularity on Routing

and MAC layers Radio irregularity has a greater impact on the routing layer

than on the MAC layer. Routing protocols, such as AODV and DSR, that use multi-round

discovery technique, can deal with radio irregularity, but with high overhead.

Routing protocols, such as geographic forwarding, which are based on neighbor discovery technique, are severely affected by radio irregularity.

Solutions for radio irregularity SGF has as low loss ratio as that of AODV and DSR, but much

lower control overhead and energy consumption.

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Future work

To evaluate and further refine the RIM model Experiments in more types of environments Experiments with different types of devices and

different types of antennas Radio pattern variation with system aging and

environment changes Analyze the impact of radio irregularity on other

protocols Localization, Sensing Coverage, Topology Control

Analyze and evaluate the remaining four solutions Bidirectional Flooding Learning Function RTS Broadcast High Energy Asymmetry Detection

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The End!

Thanks to the MobiSys Shepherd and anonymous reviewers for their valuable

criticisms!

Thanks to the MobiSys Shepherd and anonymous reviewers for their valuable

criticisms!

Documents

1 Impact of Radio Irregularity on Wireless Sensor Networks Gang Zhou, Tian He, Sudha Krishnamurthy, John A. Stankovic Computer Science Department,University