An Empirical Characterization of Radio Signal Strength Variability in 3-D IEEE 802.15.4 Networks Using Monopole Antennas Dimitrios Lymberopoulos, Quentin

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


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.


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


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


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


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



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



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



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


Antenna Characterization

Side View

8ft6.5ft

3.5ft1.25ft

Top View

2ft

2ft

2ft

: measurement point


Experiment took place in a basketball court

Minimize multipath effect

At each measurement point 20 packets @ -15dBm were received


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



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


1.25ft 3.5ft 6.5ft


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


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


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


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


Large Scale Indoors Experiment

RSSI does not change linearly with the log of the distance

Multipath

3-D antenna orientation


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


What else can we do?

More than 30% of the links are affected by human presence or motion

Detection of:

Human presence

Human motion


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


Useful Lessons Learned


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!

Documents

An Empirical Characterization of Radio Signal Strength Variability in 3-D IEEE 802.15.4 Networks Using Monopole Antennas Dimitrios Lymberopoulos, Quentin