Lecture 4: Link Characteristics

Anish Arora

CIS788.11J

Introduction to Wireless Sensor Networks

Material uses slides from Alberto Cerpa, ZhaoGovindan, WooCuller, ZhangArora

2

References

• Temporal Properties of Low Power Wireless Links: Modeling and Implications on Multi-Hop Routing, Alberto Cerpa, Jennifer L. Wong, Miodrag Potkonjak and Deborah Estrin Mobihoc 2005

• Understanding Packet Delivery Performance In Dense Wireless Sensor Networks Jerry Zhao and Ramesh Govindan, Sensys03

• Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks, Alec Woo, Terence Tong, and David Culler, SenSys 2003 Los Angeles, California

• Statistical Model of Lossy Links in Wireless Sensor Networks, Alberto Cerpa, Jennifer L. Wong, Louane Kuang, Miodrag Potkonjak and Deborah Estrin, IPSN'05

• Impact of Radio Irregularity on Wireless Sensor Networks Gang Zhou, Tian He, Sudha Krishnamurthy, and John A. Stankovic, ACM MOBISYS 2004

• LOF, Hongwei Zhang and Anish Arora

3

Outline

• Link Characterization

results

summary

• Why?

reality guides algorithm development & protocol parameter tuning

data for better propagation models used in simulations

6

Noise Variability Across Nodes

7

Radio Channel Features*

• Asymmetrical links:  connectivity from node a to node b might differ significantly from b to a

• Non-isotropical connectivity:  connectivity need not be same in all directions (at same distance from source)

• Non-monotonic distance decay:  nodes geographically far away from source may get better connectivity than nodes that are geographically closer

*Ganesan et. al. 02; Woo et. al. 03; Zhao et. al. 03; Cerpa et. al. 03; Zhou et. al. 04

8

Parameters

• Transmission gain control:  most WSN low power radios have

some form TX gain control

• Antenna height: relative distance of antenna wrt reference ground

• Radio frequency and modulation type

• Packet size:  # bits per packet can affect likelihood of receiving the

packet with no errors

• Data rate: # packets transmitted per second

• Environment type:  e.g., indoors or outdoors, with or w/o LOS,

different levels of physical interference (furniture, walls, trees, etc.),

and different materials (sand, grass, concrete, etc.)

9

Non-isotropic connectivity*

*Zhou et. al. 04

16

Explanation of Transitional Region

distance (m)

rece

ive

d p

ow

er

(dB

m)

Observations

• σ ↑ → TR ↑

• η ↑ → TR ↓

*Krishnamachari et. al.

17

Reception vs RSS

18

Links from A Given Source (1)

19

Links from A Given Source (2)

• Good link receives a packet from source (whp) all other links will as well

• Good link does not receive packet (whp) all other links will not as well

• Medium/bad links receive a packet from source (whp) good links will receive packet whp

• Medium/bad links do not receive a packet from source good links may still receive packet whp

little incentive to exploit multiple paths concurrently

* Cerpa et al Mobihoc05

20

Spatial Characteristics

• Great variability over distance (50 to 80% of radio range)

Reception rate not normally distributed around the mean and std.

dev.

Real communication channel not isotropic

• Low degree of correlation between distance and reception probability;

lack of monotonicity and isotropy

• Region of highly variable reception rates can be 50% or more of the

radio range, and not confined to limit of radio range

• From a given source, reception on good links is correlated to reception

on other links

24

Main cause of asymmetric links?

• When swapping asymmetric links node pairs, the asymmetric links are inverted (91.1% ± 8.32)

• Claim: Link asymmetries are primarily caused by differences in hardware calibration

25

Bidirectional Link Correlation

Conclusion: Send ack immediately after receiving When sending acks immediately, sum of link RNP in both directions is highly

correlated with actual link cost, i.e., almost always a good indicator of link quality* Cerpa et al Mobihoc05

Large Distance/RNP ratio

Time before sending ack after receiving a packet

26

Empirical study of link asymmetry

Many links are asymmetric Traditional techniques tend

to ignore asymmetric links

Lower transmission power --> more asymmetric links

symmetric asymmetric unidirectionalsymmetric asymmetric unidirectional

Symmetric links: short asymmetric links: long

Exploiting asymmetric links can lead to more efficient routing

27

Reliability of synchronous ACKs

Significant improvement of using sync ACK over async messages, especially in the presence of interference

Improvement occurs on both short and long links

=> Norm of estimating link quality in both directions via async beacons underestimates the link reliability of asymmetric links

28

Asymmetric Links

• Found 5 to 30% of asymmetric links

• Claim: No simple correlation between asymmetric

links and distance or TX output power

• They tend to appear at multiple distances from the

radio range, not at the limit

29*Cerpa et. al. 03

Temporal Variation

30

Temporal Consistency of Links

L1 norm indicates that good links and links with high distance/RNP ratio are temporally stable; so are bad links

* Cerpa et al Mobihoc05

34

Temporal Characteristics Summary

• Time variability is correlated with mean reception rate

• Time variability is not correlated with distance from the

transmitter (especially for “useful” links)

40

Summary

• Great variability over distance (50 to 80% of radio range)

Reception rate is not normally distributed around the mean

and std. dev.

Real communication channel is not isotropic

• Found 5 to 30% of asymmetric links

Not correlated with distance or transmission power

Primary cause: differences in hardware calibration (rx

sensitivity, energy levels)

• Time variability is correlated with mean reception rate and not

correlated with distance from the transmitter

• Possible to optimize performance by adjusting the coding

schemes and packet sizes to operating conditions

41

Link Quality Estimation

• Estimate rate of successful reception from neighboring nodes

RSSI may not work well

Neighbors exchange estimations to derive bi-directional link

quality

• 2 Techniques: Passive vs. Active

Key decision factor: broadcast medium

Passive: snoop on neighbor packets

Active: broadcast beacons

42

Passive Estimation

• Link sequence number snooping

Estimate inbound reception quality

• Key issue

Cannot infer losses until next packet reception

E.g. dead node or mobility

• Solution

With a minimum data rate, infer losses based on time

Likely to be true in periodic data collection

• Asymmetric links

Require outbound transmission quality estimation

Exchange reception quality over local broadcast

43

A Good Link Estimator

• Accurate• Agile yet stable

Agility and stability are at odds with each other

• Small memory footprint• Simple

* Woo et al

44

WMEWMA Estimator

• Compute an average success rate over time, T, and smoothen with an exponentially weighted moving average (EWMA)

• Average calculation Packets Received over T divided by

Max of Number of packets expected over T

Number of packets sent over T suggested by sequence number

• Tuning parameters: T and history size of EWMA

• Performance Yields agile and stable estimations

Uses constant memory, and is simple

45

WMEWMA better than other Link Estimators

• Woo et al studied 7 estimators by tuning to yield the same error bound

• Results WMEWMA(T, ) Estimator

Stable, simple, constant memory footprinto Compute success rate over non-overlapping window (T)

o Average over an EWMA()

Key: 10% |error| requires at least 100 packets to settle

Limits rate of adaptation

46

Agility and Error Bound

• Simulation worst case: 10% error ~ 100 packet time

• Assuming IID Binomial model, by the central limit theorem

Worst case (p = 0.5) requires

10% error with 90% confidence requires ~100 packets to learn

For example: at 30sec/packet

50 minutes for 100 packets

forwarding traffic helps to reduce this time but potentially a long delay

• Major disadvantage

47

Link Estimation Metric - ETX

• ETX of a link: Predicted number of data transmissions required to send

a packet over a link, including retransmissions Calculated using forward and reverse delivery ratios of a

link How to measure: Broadcast probe packets and derive link

quality information from each direction

• ETX of a route: Sum of ETX for each link in the route

48

Link Estimation Metric - ETX

• Forward delivery ratio: df Probability that a data packet delivered at recipient

• Reverse delivery ratio: dr

Probability that ACK packet is delivered

• Expected probability that a transmission is delivered and acknowledged is df X dr

• ETX = 1 / (df X dr)

49

ETX Example

50

ETX Example

Each node’s ETX value is the sum of the link ETX value along the lowest-ETX path to the destination node E

51

Cross Layer Link Estimation

Better estimator with information from different layers?

• Physical Layer

• Packet decoding quality

• Link Layer

• Packet Acknowledgements

• Slow to adapt

• Network Layer

• Relative importance of links

• Keep useful links in table

52

Example: Physical Layer Information alone Insufficient

Unacked

PRR

LQI

53

Four Bit Interface

• Physical Layer Sets white bit to denote that each symbol in received

packet has a very low probability of decoding error

• Link Layer Sets ack bit on a transmit buffer when it receives a

layer 2 ack for that buffer

• Network Layer Sets pin bit on a link table entry so link estimator

cannot remove it from the table until the bit is cleared

Sets compare bit to indicate whether route provided by sender of packet is better than route provided by one or more of the entries in the link table

54

Four Bit Interface Details

WHITEPackets on this channel experience few errors

ACKA packet transmission on this link was acknowledged

PINKeep this link in the table

COMPAREIs this a useful link?

55

On the impact of link estimation via Broadcast versus Unicast messages

An 802.11b study

Zhang et al Infocom 06

56

Difference in Broadcast vs Unicast Reliabilty

Broadcast has longer comm range - lower transmission rate for broadcast- no RTS-CTS handshake for broadcast

Mean delivery rate of unicast is higher, variance is lower- retransmissions- RTS-CTS

57

Impact of Interference on Difference between Broadcast and Unicast

• Estimation in the presence of unicast data traffic is dependent on whether we use broadcast or unicast messages

58

• When calculating packet delivery rate, “granularity” matters

• Delivery rate cut-off threshold is high: different from shorter inter-node separation and more hops

59

interferer-free vs. with-interferers

• More variance “with-interferer”

• Delivery rate smaller “with-interferer”

60

• Mac-latency is larger “with-interefer”

61

• Almost isotropic, especially in inner-band• “granularity” of DR matters

62

- isotropyinterferer-free vs. with-interferers

63

• Isotropy pattern not changed significantly “with-interferer”

72

Cross-interference

73

Interference studies: Main findings

• Single Interferer effects Capture effect is significant SINR threshold varies due to hardware SINR threshold does not vary with location SINR threshold varies with measured RSS Groups of radios show ~6 dB gray region New SINR threshold (simulation) model

• Multiple interferer effects Measured interference is not additive Measured interference shows high variance SINR threshold increases with more interferers

74

Capture effect

Finding: Capture effect is significant & SINRθ is not constant• Concurrent packet transmission does not always means packet

collision (capture effect: recently studied by Whitehouse et al.)

• Systematically study capture effects and quantify the SINRθ value

White

White

Black

Gray

Gray