1. RSSI is Under-Appreciated
Good Morning.
In this talk, we take the position that RSSI is Under Appreciated.
Kannan Srinivasan & Philip Levis
Stanford Information Networking Group (SING)

2. What is RSSI? Before going anywhere: what is RSSI?
RSSI is Received Signal Strength Indicator
It’s an estimate of average received signal power
EmNets 2006

3. The Buzz about RSSI RSSI is a bad indicator of link quality
Why is it believed so?
Many studies on wireless platforms
Zhao et al.
Ganesan et al.
Son et al.
Aguayo et al. ( Roofnet nodes)
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4. Plot of Aguayo et al. PRR SNR (dB) Little correlation with PRR
SNR averaged over 1 sec
SNR (dB)
(Aguayo et al. SIGCOMM 2005)
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5. A New Parameter By Newer Radios
CC2420 provides a new parameter, LQI for every successful packet
LQI from a single (previous) packet is believed to be a good indicator
Many protocols have adopted single packet LQI
No extensive evaluation to support this claim
New radios based on CC2420 provide another parameter called link quality indicator (LQI) which can be thought of as a measure of chip error rate is believed to be a good indicator of link quality.
Despite its wide adoption in make recent protocols there is so far no extensive evaluation to our knowledge to support this claim.
We carried out such an evaluation and present our preliminary results in order to address the following question:
Is hardware miscalibration still an issue in new radios?
If not, is RSSI a good indicator?
Is LQI a good indicator at all?
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6. Why Evaluate CC2420?
Several motes are based on it: Micaz, Telos and Intel2
Based on IEEE standard
Operates in 2.4 GHz ISM band
High data rate ~ 250 Kbps
Different modulation – OQPSK
Older WSN radios used OOK (mica1) and FSK (mica2)
Uses Direct Sequence Spread Spectrum
Why evaluate CC2420?
CC2420 has more advanced radio architecture than its ancestors. Basically it has packet buffers unlike older motes that need to be issued 1 bit at a time.
CC2420 is based on IEEE , the emerging WSN standard.
Many motes such as micaZ, telos and intel2 motes use them.
It operates in a different band from older motes: 2.4 GHz ISM band.
It supports high data rate of upto 250 Kbps.
While older motes used OOK or FSK, CC2420 uses OQPSK modulation.
It is based on direct sequence spread spectrum.
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7. OOK, FSK & OQPSK 1 1 1 Data mica1 OOK/ASK mica2 FSK telos QPSK1 1
Data
mica1
OOK/ASK
mica2
FSK
telos
QPSK
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8. DSSS in CC2420
Robust against multipath fading and inter-symbol interference
Good autocorrelation and cross-correlation properties of chip sequences
Robust against narrowband noise
Well understood and published in the Wireless Communications community
Several chip errors can still result in a successful decoding of a symbol
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9. LQI (~ CER) between 50 & 110 (8 Symbols)
CC2420 RSSI & LQI
LQI (~ CER) between 50 & 110 (8 Symbols)
Preamble
SFD
Frame Length/ Rsrvd
PHY Payload 4 1 1
variable
RSSI (8 symbols)
Frame Control
Seq No.
Addressing Fields
Frame Payload
FCS
Just describe ….. 2 1
0-20
variable 2
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10. Experimental Methodology1 41 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 3 2 A D F H J L N O P C E G I K M B
All nodes sent packets to all other nodes.
Transmit power level was changed.
Repeated experiment.
Our evaluation was carried out on mirage testbed provided by Intel Research, Berkeley with micaZ motes.
We chose 30 nodes from different parts of the testbed.
We first chose a transmit power level of 0 dBm and programmed all the 30 motes.
The mirage server chose a pair of nodes and instructed one to send 200 unicast packets to the other at 100 pkts/sec.
We chose unicast because it is the common kind of WSN traffic we observe and so we wanted to see its performance.
After sending 200 packets the transmitter node reported back to the server. The server then collected statistics from the receiver on the packets it received. The statistics we collected were sequence number, RSSI and LQI.
After collecting the stats, the server chose another link for evaluation.
After all the links were chosen, the server reprogrammed the motes at a different power level and repeated the evaluation.
We did these evaluations at 5 different power levels: 0, -3, -7, -15 and -25 dBm.
Mirage, Intel Research, Berkeley
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11. Distribution of RSSI for a link
Results: RSSI
Distribution of RSSI for a link
Transmit Power Level: 0 dBm
Outliers
Narrow cliff => Difference in noise floor
A natural thing to do is to see if RSSI is a good estimate or not. Instead of plotting PRR and RSSI over distance separately and trying to if they have any correlation, we plotted one against the other as it would reveal any pattern more easily.
Clearly, if RSSI > -87 dBm, the PRR is > 85%. Note that this threshold is close to the general receiver sensitivity value of -91 dBm. However around this receiver sensitivity the PRR is rather varying radically.
Another important observation is that the RSSI has little variation over time for a link suggesting that may be a single RSSI value may be enough to identify a link with PRR > 85%.
Note the outliers that have an average RSSI above -87 dBm but have a PRR < 85%. Also note that they have larger variance than other links. This suggests that a variation in RSSI may render our PRR estimate to be incorrect.
If you observe the RSSI for link 13->14, it is about -84 dBm with PRR ~ 100%. The same link at lower transmit power level has an RSSI of -92 dBm with PRR ~ 55%. This clearly illustrates that variation in RSSI may cause a link to go from “good” region to this transitional region thus supporting our speculation for our outliers.
We the looked at LQI vs PRR. Every link has a high variance in LQI suggesting that a single LQI value may not be a good indicator of PRR. A single packet LQI of 80 could mean a PRR of 10 or even 90%. However the average LQI seems to have some correlation with PRR suggesting that the LQi, when averaged may be a more accurate estimator of PRR.
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12. Noise Floor at Nodes Noise (dBm) -98 -97 -96 -95 -94 -93 -92
# of Nodes 5 8 4 3 2 1
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13. Results: LQI Transmit Power Level: 0 dBm Large variation over time
Single LQI could mean many things
Large variation over time
Transmit Power Level: 0 dBm
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14. Results: Average LQI Transmit Power Level: 0 dBm EmNets 2006
A natural follow up question is average LQI over how many packets will yield a good estimate?
To answer this we first fit a curve to the average LQI-PRR plot using a nonlinear curve fit toolbox. The curve gives a good fit at power level 0 dBm. There are some outliers. It gives a better fit at lower power level -7 dBm.
We then calculated the average of single packet LQI over windows of different sizes and calculated the absolute average error in this windowed average and the actual LQI average over all 200 packets.
For every absolute LQI error value, based on our curve fit, we calculated the possible maximum error in estimating PRR.
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15. What Window Size To Use?
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16. Results: Average LQI Single LQI is not fine
LQI > 100: Single value is fine
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17. Smaller Mean PRR Error due to averaging
Results: Average LQI
Smaller Mean PRR Error due to averaging
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18. Going back to Aguayo et al
Look at the SNR variation
If SNR varies so does the PRR
What matters is how signal and noise look like when you receive a packet: not before not after.
Averaging SNR maps to different PRR
(Aguayo et al Data)
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19. Conclusion RSSI is NOT a bad indicator
Above -87 dBm and stable => good link
Below -87 dBm or unstable => don’t know
Single packet LQI is NOT a good indicator of intermediate links
LQI, when averaged, has potential
Just talk ….
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20. Some Open Questions
What is going on with the outliers in RSSI and LQI plots?
Can combined knowledge of RSSI, average LQI and noise estimate PRR accurately?
Good Morning.
In this talk, we take the position that RSSI is Under Appreciated. Just talk ….
In this talk, we take the position that RSSI is Under Appreciated.
First, I will the necessary background.
I will then talk briefly about the new WSN radio chip from chipcon, CC2420.
After that, I will introduce the experimental methodology we adopted in our evaluation and discuss our preliminary results.
I will then conclude my talk with implications of our results.
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21. Thank You! Questions? Kannan Srinivasan (srikank@stanford.edu)
Philip Levis
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22. Backup Slides
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23. The Buzz about RSSI RSSI is a bad link quality indicator
Why is it believed so?
After many evaluations on older radios
Zhao et al
SS > 550
PLR ~ 80%
PLR < 5% had SS > 550 but converse not true
(Zhao et al ENSS 2003)
EmNets 2006

24. The Buzz about RSSI RSSI is a bad link quality indicator
Why is it believed so?
After many evaluations on older radios
Zhao et al
(PLR < 5%) => (SS > 550) but converse not true
Son et al (evaluated concurrent transmissions)
SINR threshold: SINR with PRR > 0.9
Many researchers have carried out extensive evaluations of older radios that are based on TR1000 and CC1000. I will briefly talk about a few of them.
Ganesan et al, from their experiments in an unobstructed outdoor environment:
(i)the percentage of asymmetric links increases with distance; especially at lower transmit power levels.
(ii) Asymmetries are due to small differences between the nodes in transmit power and reception sensitivity.
Zhao et al:
More than 10% of link pairs have packet loss difference > 50%, even for light loads where one expects fewer collisions contributing to packet loss.
Asymmetry is possibly caused due to the difference in transceiver calibration (slightly different transmit powers, or differences in receiver circuitry).
Cerpa et al:
no clear correlation between PRR and distance in an area of more than 50% of the total communication range.
The percentage of link asymmetries has no correlation with distance and/or transmission power levels.
Link asymmetries are due to hardware calibration differences and not due to the environment.
Son et al:
i. SINR threshold as the minimum SINR which guarantees a reliable packet communication with PRR > 0.9.
ii. Single RSSI is not a good indicator as it varies a lot in a concurrent transmission case.
iii. SINR threshold is a function of hardware and RSSI.
iv. SINR threshold increases with number of interferers.
Aguayo et al:
Links with intermediate levels of loss are the common case; there is no clear distinction between “working” and “non-working” links.
Link distance and S/N ratio do have an effect on loss rates, but the correlation is weak.
Experiments using a hardware channel emulator suggest that an important cause of intermediate loss rates is multi-path fading due to reflections in the radio environment.
Overall, link asymmetries were common in older radios and hardware miscalibration was thought of as the cause.
(Son et al ISI-TR-2005)
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25. Results: RSSI Transmit Power Level: 0 dBm EmNets 2006
A natural thing to do is to see if RSSI is a good estimate or not. Instead of plotting PRR and RSSI over distance separately and trying to if they have any correlation, we plotted one against the other as it would reveal any pattern more easily.
Clearly, if RSSI > -87 dBm, the PRR is > 85%. Note that this threshold is close to the general receiver sensitivity value of -91 dBm. However around this receiver sensitivity the PRR is rather varying radically.
Another important observation is that the RSSI has little variation over time for a link suggesting that may be a single RSSI value may be enough to identify a link with PRR > 85%.
Note the outliers that have an average RSSI above -87 dBm but have a PRR < 85%. Also note that they have larger variance than other links. This suggests that a variation in RSSI may render our PRR estimate to be incorrect.
If you observe the RSSI for link 13->14, it is about -84 dBm with PRR ~ 100%. The same link at lower transmit power level has an RSSI of -92 dBm with PRR ~ 55%. This clearly illustrates that variation in RSSI may cause a link to go from “good” region to this transitional region thus supporting our speculation for our outliers.
We the looked at LQI vs PRR. Every link has a high variance in LQI suggesting that a single LQI value may not be a good indicator of PRR. A single packet LQI of 80 could mean a PRR of 10 or even 90%. However the average LQI seems to have some correlation with PRR suggesting that the LQi, when averaged may be a more accurate estimator of PRR.
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26. Results: RSSI
Transmit Power Level: -7 dBm
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27. Results: RSSI
Transmit Power Level: 0 dBm
Outliers
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28. Results: RSSI Transmit Power Level: 0 dBm
Narrow cliff => Difference in noise floor
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29. Results: RSSI
Wide cliff due to miscalibration
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30. Results: LQI
Transmit Power Level: -7 dBm
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31. Results: Average LQI Nicer curve fit may be due to difference in time
Transmit Power Level: -7 dBm
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32. CC2420 RSSI and LQI RSSI calculated over 8 symbols
From analog signal
LQI (Link Quality Indicator):
2 ways to calculate (Chipcon)
From RSSI
Chip correlation in a byte (can be looked at as Chip Error Rate) – provided by CC2420
Statistical in nature
Remember: many chip errors can still lead to a correct symbol decoding
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33. Plot for Son et al
Difference in distortion of signal from different interfering nodes
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34. Results: Average LQI
Maximum Absolute
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35. Results: Average LQI Average Window Size = 5 Still a mess
LQI > 100: Not hard to find
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36. Results: Average LQI
Average Window Size = 10
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37. Results from another evaluation
LQI
In our later work we looked at asymmetries, RSSI and LQI plots for evaluation with inter packet time of about 15 seconds and about 800 packets per node.
Links involved in asymmetry varied over time. Only 5 links showed consistent asymmetries out of about 650 possible links. The RSSi however had larger variation than for our earlier evaluation with burst of 200 packets as the packets were far apart. This variation caused more outliers from our earlier observation.
The LQI still had large variation over time but the mean LQI still had some correlation with the PRR.
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38. Results: Average LQI Average Window Size = 1 Single LQI is not fine
Due to outliers or change in “quality”??
LQI > 100: Single value is fine
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39. Results: Average LQI
Average Window Size = 20
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