Transcript Slide 1

CC2420 Channel and RSSI Evaluation
COnnecti vity Lab
Dept. of EECS, UC Berkeley
Nov/22/2006
Contents
Introduction
Chipcon CC2420 Radio
CC2420 RSSI Evaluation
Introduction
The Comparison of Different Methods
RSS values measured by COTS wireless systems are sufficiently
rich in information to permit a mobile device locates itself,
reliably and accurately
The most important trade off is between Offline Calibration and accuracy
The CDF of Different RSS Based Indoor Positiong Methods (Note: Different Experiment Environments)
1
0.9
0.8
0.7
CDF
0.6
0.5
0.4
MP&AP Perspective + Kalman Filter
Histogram Bayseian Approach
Adaptive Temporal
Novel ToF(Steven Lanzisera - UC Berkeley)
Hours (Cluster&Cluster Key)
Ekahau System
Particle Filter (Median error)
A System for LEASE (Median error)
ECOLOCATION (Max error)
0.3
0.2
0.1
0
0
1
2
3
4
Error (m)
5
6
7
Last Final Conclusion
Next Steps
Continuing The Project
Sensors Evaluation
+
Fingerprinting
Distributed Estimation
(Study, Modeling,
Simulation,
Implementation)
Radio Map Independent
Algorithm (Study, Modeling
Simulation,
Implementation)
Tx1
Rx3
Rx1
Rx2
Last Final Conclusion
The Goal of this Part of the project
Become familiar with our sensors and evaluate them as much as
possible
Understand the behavior of RSSI in indoor environments
Measure the effect of different parameters on RSSI
Survey the existence of correlation in RSSI Samples and base
band received signals
Performed Works
Study the theory of indoor wireless channels (as much as it was
needed)
Gathering a lot of data in different situations and environment
Analysis of these data
Chipcon CC2420 Radio
The Chipcon CC2420 Radio Chip Characteristics
The CC2420
The CC2420 is an IEEE 802.15.4 compliant RF transceiver
designed for low-power and low-voltage wireless applications
Frequency: 2.4 GHz
Communication Technology: Direct Sequence Spread Spectrum
Total BW: 83 MHz
Data Rate: 250 kbps
Output power: Programmable (Max Power = 0 dBm)
Range (Indoor) : 30 m
Chipcon CC2420 Radio
The Chipcon CC2420 Radio Chip Characteristics
The CC2420
IF: 2 MHz
Each Byte
2 Sym: 4 Bits
32 Chips
Tc = 0.5 us
Pulse Shaping: Half Sine
Chipcon CC2420 Radio
The Chipcon CC2420 Radio Chip Characteristics
RSSI and LQI in The CC2420
The RSSI value is always averaged over 8 symbols periods (128 us) and digitized by
an 8 bit ADC
The Link Quality Indication (LQI) measurements is a characterization of the strength
and/or quality of received packet
RSSI and LQI values are not necessarily linked; but Low LQI
Invalid RSSI
Chipcon CC2420 Radio
The Chipcon CC2420 Radio Chip Characteristics
Telos Mote Experiment, Stockholm, 2005
The Effect of Radio Interference
The values of RSSI could really affected by other people working with motes
Although there is no direct interference with the Wi-Fi channels, the
channel quality can be affected by the overload and thus distort some
of the packets
The Effect of the Motes Themselves
Measurements with the same motes at the same location, same
batteries show a difference up to a few dBm (from new motes to
old motes)
Chipcon CC2420 Radio
CC2420 Wireless Channel Characteristics
RMS Delay Spread
Delay Spread
Td  max i (t )   j (t ) , i, j : longestand shortest path
i, j
Td 
30 m
1m

 100 ns  3.3ns  97 ns
8
8
3 *10 3 *10
WC 
1
 5.7 MHz
2Td
WSignal BW  2 MHz  5.7 MHz 
Channel is flat fading and a single channel filter tap is
sufficient to model the channel
RMS Delay Spread
F=2.4 GHz
  30 ns
 1
 50  660KHz


WC  
 1  6.6 MHz
 5 
Corry Hall Corridor
FCF  0.9
Frequency
Selective
FCF  0.5
Flat Fading
Chipcon CC2420 Radio
CC2420 Wireless Channel Characteristics
Coherence Time
TCohe
17.6ms v  3 m / s
.423
C
3 108

 .423
 .423


fd
vf c
v  2.4 109 176ms v  .3 m / s

 137
TCohe
  
NoRSSIS round
RSSI

128

s
MeasTime

 1375
The maximum number of
stable RSSI samples
Coherence Dis tan ce  CohTime *UserVeloci ty  5 cm 

2
In our system, there is one RSSI sample per packet
In this network configuration the RSSI rate is 26.3 sample/second
per device
CC2420 RSSI Evaluation
Path Loss Model
Indoor Path Loss Model
Test Scenario
One sensor put at different distances (at the ~same time/Corridor)
Rx
Tx
d1
d2
Run: RSSI_FigAvgVarHist.m at
C:\ZigBeeNodes\CollectedData\Corridor_072106\Test1_QP
CC2420 RSSI Evaluation
Path Loss Model
Indoor Path Loss Model
Test Scenario
One sensor put at different distances (at the ~same time/Corridor)
Worth Case
Conclusions
PathLoss vs Distance in Quiet Corridor
0
-17 dbm
-20
Path Loss (dB)
Theoritical PL
Practical PL
Real
RSSI(d) = Pt(dBm) - PL(D0) - 10nlog10(d/D0) + Noise
- Constant offset is not -45 dBm
- Path Loss almost follows the LogNormal Model
-40
RSSI(d) = -17.112 - 36.3236 - 1.85*10log10(d/3(ft))
-60
-80
-100
- The effect of multipath is
remarkable
RSSI(d) = -17.112 - 57.0773 - 1.69*10log10(d/3(ft))
0
2
4
6
8
10
Distance (ft)
12
14
16
18
20
- Something is wrong in these
sensors (Interference/Sensors
Board)
PathLoss vs Distance in the Presense of People at Corridor
0
-20
Path Loss (dB)
Theoritical PL
Practical PL
Real
RSSI(d) = Pt(dBm) - PL(D0) - 10nlog10(d/D0) + Noise
-40
 4d 0 
PLd0  

  
RSSI(d) = -17.112 - 34.9492 - 1.78*10log10(d/3(ft))
-60
-80
-100
RSSI(d) = -17.112 - 50.016 - 1.64*10log10(d/3(ft))
0
2
4
6
8
10
Distance (ft)
12
14
16
18
20
n
CC2420 RSSI Evaluation
Path Loss Model
Indoor Path Loss Model
Test Scenario
One sensor put at different distances (at the ~same time/Corridor)
RSSI Mean vs Distance In The Corridor
20
Max Err = 9.6015
&
Mean Err = 3.1511
RSSI Mean
0
-20
Quiet Env
People
|Mean Err|
-40
-60
-80
0
2
4
6
8
10
Distance (ft)
12
14
16
18
20
16
18
20
RSSI SD vs Distance In The Corridor
5
Quiet Env
People
RSSI Var
4
3
2
1
0
0
2
4
6
8
10
Distance (ft)
12
14
CC2420 RSSI Evaluation
Path Loss Model
Indoor Path Loss Model
Test Scenario
One sensor put at different distances (at the ~same time/Corridor)
Box-Whisker Plot, Env: Quiet Corridor, Black Circle: Mean Value
-20
200 RSSI
-30
RSSI (dBm)
-40
-50
-60
-70
-80
1
2
3
4
5
6
7
8
9
10 11 12
Distance (ft)
13
14
15
16
17
18
19
20
21
CC2420 RSSI Evaluation
Path Loss Model
Indoor Path Loss Model
Test Scenario
One sensor put at different distances (at the ~same time/Corridor)
Box-Whisker Plot, Env: Not Quiet Corridor, Black Circle: Mean Value
-20
200 RSSI
-30
RSSI (dBm)
-40
-50
-60
-70
-80
1
2
3
4
5
6
7
8
9
10 11 12
Distance (ft)
13
14
15
16
17
18
19
20
21
CC2420 RSSI Evaluation
Path Loss Model
Indoor Path Loss Model
Test Scenario
One sensor put at different distances (at the ~same time/Corridor)
Variance Test
Mica Z
Min Var
Max Var
Our
Sensors
(Outdoor)
0.20
0.05
1.90
9.17
0.91
1.60
Our
Sensors
(Indoor)
0.02
0.20
2.17
19.20
2.40
7.68
Stanford
Sensors
0.0
13.77
.02822
(Indoor & 100
Sensors )
Avg Var
dBm
Worth Case
Conclusions
-Something is wrong in these sensors
- The effect of interference
CC2420 RSSI Evaluation
Path Loss Model
Outdoor Path Loss Model
Test Scenario
Two sensors put at different distances (at the ~same time/Soccer Court)
Conclusions
RSSI Mean vs Distance In Large Outdoor Env
0
Device 1
Dev1-LogNrmPL
Device 2
Dev2-LogNrmPL
RSSI Mean (dB)
-20
-40
- Constant offset is not -45 dBm
- Path Loss almost well follows the
Log-Normal Model
-60
- In the outdoor these sensors
have almost same behavior
-80
-100
0
0.5
1
1.5
2
2.5
Distance (ft)
3
3.5
4
4.5
RSSI Mean vs Distance In Large Outdoor Env After Offset Compensation, Avg-PrcThrDiff ~ 2.5 dB
20
Device 1
Dev1-LogNrmPL
Device 2
Dev2-LogNrmPL
RSSI Mean (dB)
0
-20
-40
-60
-80
0
0.5
1
1.5
2
2.5
Distance (ft)
3
3.5
4
4.5
- There is some offset between
sensors, so calibration is necessary
CC2420 RSSI Evaluation
Path Loss Model
Outdoor Path Loss Model
Test Scenario
Two sensors put at different distances (at the ~same time/Soccer Court)
Box-Whisker Plot, Dev 1, Env: OutDoor
RSSI (dBm)
-20
400 RSSI
-40
-60
-80
1
2
3
4
Distance (ft)
5
6
7
Box-Whisker Plot, Dev 2, Env: OutDoor
RSSI (dBm)
-20
200 RSSI
-40
-60
-80
1
2
3
4
Distance (ft)
5
6
7
CC2420 RSSI Evaluation
The Effect of Different Parameters on RSSI
The Effect of Height
Test Scenario
Two different sets of data at two different height with 1.22 m height difference was
compared (Test1 & Test2 was done at two different time/Env-Conditions)
Height : 1.22 m height difference
6 dBm power difference
Test 1
AvgErr
AbsMeanErr
4.5767 dBm
Test 2
AvgErr
AbsMeanErr
7.4928 dBm
Reported Error for the Same Chip is 10
dBm for 1m height difference
S1: 4.6022
S2: 1.9371
S3: 0.0090 All in dBm
S4: 11.2586
S5: 7.6377
S6: 2.0158
S1:
S2:
S3:
S4:
S5:
S6:
4.1134
7.8446
12.3849 All in dBm
12.9487
0.4060
7.2592
CC2420 RSSI Evaluation
The Effect of Different Parameters on RSSI
The Effect of Window
Test Scenario
Two different sets of data at two different situations (at the same place, time and Envcondition) was compared
Window : Open/Closed
0.6 dBm power difference
Test
AbsMeanErr





(All in dBm)
S1: 1.9241
S2: 0.0140
S3: 0.9735
S4: 0.5581
S5: 0.0630
S6: 0.2506
Close to Window
CC2420 RSSI Evaluation
The Effect of Different Parameters on RSSI
The Effect of Door
Test Scenario
Two different sets of data at two different situations (at the same place, time and Envcondition) was compared
Door : Open/Closed
1.5 dBm power difference
Test
AbsMeanErr





S1:
S2:
S3:
S4:
S5:
S6:
(All in dBm)
3.5996
0.0005
0.7787
1.9850
1.9377
0.0354
Close to Door
- Due to the need of calibration, error should be lower that this
CC2420 RSSI Evaluation
The Effect of Different Parameters on RSSI
The Effect of Lights
Test Scenario
Two different sets of data at two different situations (at the same place, time and Envcondition) was compared
Lights : On/Off
1.7 dBm power difference
Test
AbsMeanErr





S1:
S2:
S3:
S4:
S5:
S6:
(All in dBm)
7.7846
0.0319
1.0982
0.8703
0.5434
0.0461
In the order of several
0.1 dBm
- Due to the need of calibration, error should be lower that this
CC2420 RSSI Evaluation
The Effect of Different Parameters on RSSI
The Effect of Antenna Direction
Test Scenario
Two different sets of data at two different antenna directions (at the same place, time
and Env-condition) was compared
Test
AbsMeanErr
(All in dBm)
5.5 dBm power difference
- Due to the need of calibration, error should be lower that this
Light Frame1
1
2
6
Mobile Node (Rx)
4
5
Light Frame2
Sensors (Tx)
3
?
Win
Wal
Brd
Dor
Radiated pattern Vertical mounting
Radiated pattern horizontal mounting
CC2420 RSSI Evaluation
Feasibility Test
Power Distribution
Test Scenario
Data Received by coordinator located under each sensor (at the time and Envcondition) was compared
100
50
0
10
5
2
4
100
- Due to the need of calibration,
there is some uncertainty in these
results.
50
0
10
5
100
50
2
0 0
Device 5 & Direction: Brd
4
100
-RSSI (dB)
-RSSI (dB)
5
50
0
10
4
5
0
2
0
2
4
0 0
Device 2 & Direction: Brd
-RSSI (dB)
-RSSI (dB)
0 0
Device 4 & Direction: Brd
0
10
Conclusions
Device 1 & Direction: Brd
-RSSI (dB)
-RSSI (dB)
Device 3 & Direction: Brd
100
50
0
10
5
2
0 0
Device 6 & Direction: Brd
5
2
4
100
50
0
10
4
0
0
CC2420 RSSI Evaluation
The Effect of Different Parameters on RSSI
Basic Fingerprinting
Radio Map Elements
48 runs in different situations (Quiet and Non-Quiet)
[ Positioni (Cell Index) ( SensorID j , Direction k , TxPwl ) mean([ RSS Vector)]
Static Test
Expected Position
1
Estimated Position (SQR) 3
Estimated Position (ABS) 3
2
6
6
3
3
3
4
4
4
5
5
5
6
6
6
3
4
4
1
1
3
4
3
3
3
2
3
2
2 24
1 6
1 6
4
5
5
6
2
2
6
5
5
5
5
5
3
2
2
2
6
2
1
3
4
3
4
4
Max Error
2.44m
MobileTest
Expected Position:
3 4(2) 5(6) 6(5) 2 1 3 4(2) 5(6) 6(5) 2 1
Estimated Position:
4
2
4
2
4
6
4
6
4
6
4
6
3
6
3
2
2
2
2
2
2
5
3
2
3
6
1
2
3
2
1
2
2
2
5
2
5
2
Sensors
By the offset compensation of sensors we can
improve the performance
Light Frame2
5
Mobile Node
6
4
3
2.44 m
1.65 m
2
1
Light Frame1
2
2
2
2
2
2
NI RSSI Evaluation
NI Power samples
n=2.2, AvgVar= 0.05 & MaxVar= 0.25
5
0
-5
Received Power (dBm)
-10
Practical PL
Theortical PL
|Thr-Prc|
-15
-20
-25
-30
-35
-40
-45
0
0.1
0.2
0.3
0.4
0.5
Distance (m)
0.6
0.7
0.8
0.9
Thank You
The End
Box Whisker Plot (N(0,sigma))