Lecture 4:
Link Characteristics
Anish Arora
CIS788.11J
Introduction to Wireless Sensor Networks
Material uses slides from Alberto Cerpa,
ZhaoGovindan, WooCuller, ZhangArora
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
2
Outline
•
•
Link Characterization

results

summary
Why?

reality guides algorithm development & protocol parameter tuning

data for better propagation models used in simulations
3
Noise Variability Across Nodes
6
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
7
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.)
8
Non-isotropic connectivity*
*Zhou et. al. 04
9
received power (dBm)
Explanation of Transitional Region
Observations
distance (m)
*Krishnamachari et. al.
•σ ↑
→
TR ↑
•η ↑
→
TR ↓
16
Reception vs RSS
17
Links from A Given Source (1)
18
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
19
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
20
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
24
Bidirectional Link Correlation
Large Distance/RNP ratio
Conclusion:
Time before sending ack after
receiving a packet
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
25
Empirical study of link asymmetry
symmetric
asymmetric
unidirectional

Many links are asymmetric



Traditional techniques tend
to ignore asymmetric links
Lower transmission power
--> more asymmetric links
Symmetric links: short
asymmetric links: long
 Exploiting asymmetric
links can lead to more
efficient routing
26
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
27
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
28
Temporal Variation
*Cerpa et. al. 03
29
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
30
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)
34
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
40
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
41
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
42
A Good Link Estimator
•
•
Accurate
Agile yet stable
 Agility and stability are at odds with each other
•
•
Small memory footprint
Simple
* Woo et al
43
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
44
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 footprint
o
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
45
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
46
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
47
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)
48
ETX Example
49
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
50
Cross Layer Link Estimation
Better estimator with information from different layers?
•
Physical Layer
•
Link Layer
•
Network Layer
• Packet decoding quality
• Packet Acknowledgements
• Slow to adapt
• Relative importance of links
• Keep useful links in table
51
Example: Physical Layer Information alone Insufficient
PRR
LQI
Unacked
52
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
53
Four Bit Interface Details
COMPARE
PIN
Is this a useful link?
Keep this link in the
table
ACK
WHITE
A packet transmission
on this link was
acknowledged
Packets on this
channel experience
few errors
54
On the impact of link estimation via Broadcast
versus Unicast messages
An 802.11b study
Zhang et al Infocom 06
55
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
56
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
57
•
When calculating packet delivery rate, “granularity” matters
•
Delivery rate cut-off threshold is high: different from shorter inter-node separation and more hops
58
interferer-free vs. withinterferers
•
•
More variance “withinterferer”
Delivery rate smaller
“with-interferer”
59
•
Mac-latency is larger
“with-interefer”
60
•
Almost isotropic, especially in inner-band
•
“granularity” of DR matters
61
- isotropy
interferer-free vs. withinterferers
62
•
Isotropy pattern not changed significantly “with-interferer”
63
Cross-interference
72
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
73
Capture effect
White
Gray
Black
Gray
White
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
74