an empirical characterization of radio signal strength variability in 3-d ieee 802.15.4 networks...
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An Empirical Characterization of Radio Signal Strength Variability in 3-D IEEE 802.15.4 Networks Using
Monopole Antennas
Dimitrios Lymberopoulos, Quentin Lindsey and Andreas Savvides
Embedded Networks and Applications Laboratory (ENALAB)
http://www.eng.yale.edu/enalab
Yale University
Can RSSI provide reliable distance estimation?
More than 150.000 measurements were acquired
40 wireless sensor nodes were used
Acquired data along with ground truth data are available at: http://www.eng.yale.edu/enalab/rssidata
Quantify variability in typical office environments
3-D deployments
Low power radios
What other type of information can RSSI provide?
EWSN 2006 February 15th Dimitrios Lymberopoulos
Background
MAP based approaches (RADAR, Bahl et. al)
Create a database of RSSI fingerprints: < [RSSI], Position >
Find the fingerprint with the minimum distance to the recorded RSSI array
15ft error using 802.11 wireless radios
RSSI distance prediction (Ecolocation, Yedavalli et. al)
Use ordering or triangulation to refine the initial estimates
10ft error in a small indoor experiment with CC1000 wireless radios
Probabilistic approaches (Madigan et. al)
Every node computes a belief about its location
A probabilistic signal propagation model is assumed
20ft error using 802.11 wireless radios
EWSN 2006 February 15th Dimitrios Lymberopoulos
Infrastructure
XYZ sensor node designed at Yale (http://www.eng.yale.edu/enalab/XYZ)
CC2420 wireless radio from Chipcon
2.4 GHz IEEE 802.5.14/Zigbee-ready RF transceiver
DSSS modem with 9 dB spreading gain
Effective data rate: 250 Kbps
8 discrete power levels: 0, -1, -3, -5 , -7, -10, -15 and -25 dBm
Power consumption: 29mW – 52mW
Monopole antenna with length equal to 1.1inch.
EWSN 2006 February 15th Dimitrios Lymberopoulos
Received Signal Strength Indicator (RSSI)
P = RSSI + RSSIOFFSET [dBm]
The power P at the input RF pins can be obtained directly from RSSI:
RSSI is an 8-bit value computed by the radio over 8 symbols (128μs)
RSSIOFFSET is determined experimentally based on the front-end gain. It is equal to -45dbm for the CC2420 radio
Sources of RSSI Variability
Intrinsic
Radio transmitter and receiver calibration
Extrinsic
Antenna orientation
Multipath, Fading, Shadowing
EWSN 2006 February 15th Dimitrios Lymberopoulos
Path Loss Prediction Model
Log-normal shadowing signal propagation model:
RSSI(d) = PT – PL(d0) – 10ηlog10(d/d0) + Xσ
0 5 10 15 20 25-45
-40
-35
-30
-25
-20
Distance(feet)
RS
SI
(db
m)
Averaged RSSI valueslog-fit
RSSI(d) is the RSSI value recorded at distance d
PT is the transmission power
PL(d0) is the path loss for a reference distance d0
η is the path loss exponent
Xσ is a gaussian random variable with zero mean and σ2 variance
Model verification using data from a basketball court
EWSN 2006 February 15th Dimitrios Lymberopoulos
Radio Calibration
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
Receiver
1.31ftTransmitter
For each location and orientation 20 packets were sent @ -15dBm
EWSN 2006 February 15th Dimitrios Lymberopoulos
Tra
nsm
itter
Radio Calibration
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
Receiver
1.31ft
For each location and orientation 20 packets were sent @ -15dBm
EWSN 2006 February 15th Dimitrios Lymberopoulos
Radio Calibration
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
Receiver
1.31ft
Transmitter
For each location and orientation 20 packets were sent @ -15dBm
EWSN 2006 February 15th Dimitrios Lymberopoulos
Radio Calibration
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
0-90-180-270-
Receiver
1.31ft
Tra
nsm
itter
For each location and orientation 20 packets were sent @ -15dBm
EWSN 2006 February 15th Dimitrios Lymberopoulos
Radio Calibration
Experiment in an empty room
TX calibration: 9 different transmitters
RX calibration: 6 different receivers
1 2 3 4 5 6 7 8 90
5
10
15
20
25
30
350 Degrees
Transmitter ID
RSSI(
dbm
)
1 2 3 4 5 6 7 8 90
5
10
15
20
25
30
3590 Degrees
Transmitter ID
RSSI(
dbm
)
1 2 3 4 5 6 7 8 90
5
10
15
20
25
30
35180 Degrees
Transmitter ID
RSSI(
dbm
)
1 2 3 4 5 6 7 8 90
5
10
15
20
25
30
35270 Degrees
Transmitter ID
RSSI(
dbm
)
1 2 3 4 50
5
10
15
20
25
300 Degrees
Receiver ID
RSSI(
dbm
)
1 2 3 4 50
5
10
15
20
25
3090 Degrees
Receiver ID
RSSI(
dbm
)
1 2 3 4 50
5
10
15
20
25
30180 Degrees
Receiver ID
RSSI(
dbm
)
1 2 3 4 50
5
10
15
20
25
30270 Degrees
Receiver ID
RSSI(
dbm
)
TX Standard Deviation: 2.24dBm RX Standard Deviation: 1.86dBm
EWSN 2006 February 15th Dimitrios Lymberopoulos
Antenna Characterization
Side View
8ft6.5ft
3.5ft1.25ft
Top View
2ft
2ft
2ft
: measurement point
EWSN 2006 February 15th Dimitrios Lymberopoulos
Experiment took place in a basketball court
Minimize multipath effect
At each measurement point 20 packets @ -15dBm were received
Antenna Characterization
0 2 4 6 8 10 12 14 16-50
-45
-40
-35
-30
-25
-20
-15
-10
-5
Distance (ft)
RS
SI
(db
m)
Optimal AntennaSuboptimal Antenna
Optimal antenna length-1.1inch
Random RSSI values due to multipath
Large communication range
Suboptimal antenna with 2.9inch length
EWSN 2006 February 15th Dimitrios Lymberopoulos
Antenna Characterization
5 10 15 20 25 30-48
-46
-44
-42
-40
-38
-36
-34
Distance(feet)
RS
SI
(db
m)
04590135180225270315
0 5 10 15 20 25 30-48
-46
-44
-42
-40
-38
-36
-34
-32
-30
Distance(feet)
RS
SI
(db
m)
04590135180225270315
0 5 10 15 20 25-45
-40
-35
-30
-25
-20
Distance(feet)
RS
SI
(db
m)
04590135180225270315
Similar distances (<1ft difference) can produce very different RSSI values (even up to 11dBm)
Very different distances ( even >18ft) can produce the same RSSI values
EWSN 2006 February 15th Dimitrios Lymberopoulos
1.25ft 3.5ft 6.5ft
Antenna Characterization
0 5 10 15 20 25-45
-40
-35
-30
-25
-20
Distance(feet)
RS
SI
(db
m)
6.5ft3.5ft1.5ft
0 5 10 15 20 25-50
-45
-40
-35
-30
-25
Distance(feet)
RS
SI
(db
m)
6.5ft3.5ft1.5ft
Best antenna orientation Worst antenna orientation
EWSN 2006 February 15th Dimitrios Lymberopoulos
Antenna orientation effect
For a given height of the receiver very different RSSI values are recorded for different antenna orientations
Radiation PatternSide View Top View
Communication rangeSymmetric Region Antenna orientation
independent regions
Communication range
EWSN 2006 February 15th Dimitrios Lymberopoulos
Antenna Effects in Indoor Environments
The basketball court experiment was performed inside our lab
We focused on the best antenna orientation
0 2 4 6 8 10 12 14 16-50
-45
-40
-35
-30
-25
-20
Distance (ft)
RS
SI
(db
m)
6.17ft 5.65ft 4.6ft 1.25ft
EWSN 2006 February 15th Dimitrios Lymberopoulos
Large Scale Indoors Experiment
40 nodes were placed on the testbed (15ft (W) x 20ft(L) x 10ft(H)) installed in ENALAB
Each node transmitted 10 packets at each one of the 8 power levels. The recorded RSSI values were transmitted to a base station for logging.
01
23
45
6
0
1
2
3
4
50
0.5
1
1.5
2
2.5
7
8
9
6
27
10
5
28
12
X coordinate
42
33
4
30
35
29
11
13
14
3
34
32
37
36
17
31
Connectivity at Power Level 7
41
25
15
1
2
3839
18
26
24
16
Y coordinate
40
19
22
23
20
21
Z c
oo
rdin
ate
01
23
45
6
0
1
2
3
4
50
0.5
1
1.5
2
2.5
7
8
9
6
27
10
5
28
12
X coordinate
42
33
4
30
35
29
11
13
14
3
34
32
37
36
17
31
Connectivity at Power Level 4
41
25
15
1
2
38
39
18
26
24
16
Y coordinate
40
19
22
23
20
21
Z c
oo
rdin
ate
Placement and Connectivity
EWSN 2006 February 15th Dimitrios Lymberopoulos
Large Scale Indoors Experiment
RSSI does not change linearly with the log of the distance
Multipath
3-D antenna orientation
EWSN 2006 February 15th Dimitrios Lymberopoulos
Maximum (0dBm) Medium (-5dBm) Low (-15dbm)
Link Asymmetry
Asymmetric link between nodes A and B
RSSI(A) ≠ RSSI(B)
1 2 3 4 5 6 7 820
22
24
26
28
30
32
34
36
Power Level (1= Maximum)
Per
cen
tag
e o
f O
ne-
Way
Lin
ks
One way Links
1 2 3 4 5 6 7 820
25
30
35
40
45
50
55
Power Level (1 = Maximum)
Per
cen
tag
e o
f as
sym
etri
c li
nks
>=2 >=3 >=4 >=5 >=6
One way links Asymmetric links
EWSN 2006 February 15th Dimitrios Lymberopoulos
What else can we do?
More than 30% of the links are affected by human presence or motion
Detection of:
Human presence
Human motion
EWSN 2006 February 15th Dimitrios Lymberopoulos
Conclusions
3-D space is very different that 2-D space
Antenna orientation effects are dominant in 3-D deployments
3-D deployments are a more realistic for evaluating RSSI localization methods
RSSI distance prediction in 3-D deployments is almost impossible
Ordering of the RSSI values is not helpful
Even if antenna orientation is known!
Probabilistic approaches
A probabilistic model of RSSI exists for the symmetric region of the antenna
Generalizing this model to 3-D deployments is extremely difficult if not impossible.
Radio calibration has minimal effect on localization
EWSN 2006 February 15th Dimitrios Lymberopoulos
Useful Lessons Learned
EWSN 2006 February 15th Dimitrios Lymberopoulos
AKW #000ENALAB
Becton Center
To Davies Auditorium
Professor’s KucLab
LoadingDock
MTC LAB
MTC LAB
Ed Jackson
ITsupport
Machinery Room
Outdoorspace Corridor Lab Offices Other
XYZ
Hardware Abstraction
Module
Communication MemoryManager
Static SOS Kernel
Dynamic LoadableBinary Modules
Dynamic LoadableBinary Modules
Matlab interface to the network to:
Wire up multiple services to create user specific services
Log data from the network
Push data to the network
THANK YOU!