EFFECT OF RAINFALL ON LINK QUALITY IN AN OUTDOOR

FOREST DEPLOYMENT

Andrew Markham, Niki Trigoni

Oxford University Computing Laboratory, Oxford, U.K.

Stephen Ellwood

Wildlife Conservation Research Unit, Oxford University, Oxford, U.K.

Keywords:

Wireless sensor network, Experimental, Link quality, RSSI.

Abstract:

Existing work has shown that rainfall has an effect on link quality. Some authors report a positive effect in

moist conditions, whereas others demonstrate a signiﬁcant decrease in link throughput as a result of rainfall or

fog. The precise cause of these variations has not yet been conclusively established. This paper reports on long

term (26 day) link quality results from 12 nodes deployed in a forest. We found that rainfall has the effect of

decreasing the performance of 28% of good links (classiﬁed as those having above 90% packet reception), but

simultaneously increasing the performance of 34% of poor links (those having below 50% packet reception).

In addition, it was found that variations in link quality persisted for a few days after rainfall. This suggests that

link variations are not a result of rain induced fading, but rather due to water sitting on node packaging. We

present experimental evidence which demonstrates that changes in link quality (both positive and negative)

are indeed due to the presence of water, capacitively loading the antenna, altering its radiation pattern.

1 INTRODUCTION

Although there have been a number of experimental

studies of link quality variation over time in a wire-

less sensor network, most have been over a very short

time period (typically a few hours) (Srinivasan and

Levis, 2006; Becker et al., 2009). Furthermore, many

have involved indoor deployments, where the effects

of weather are minimal (Becker et al., 2009; Wang

et al., 2007; Cerpa et al., 2005). Accurate estimates

of link quality are essential for the deployment of a

long-lived and robust sensor network. By using link

estimates, bad links can be avoided. Overall network

performance can be greatly improved by choosing re-

liable links (Wang et al., 2007).

In particular, we are concerned with the impact

that rainfall has on link performance. Some re-

searchers have reported an improvement in link qual-

ity during moist periods (Thelen et al., 2005), whereas

others have reported a detrimental effect (Anastasi

et al., 2004). It is thus not clear whether rainfall is

an environmental factor that should be welcomed or

not.

This study was conducted in order to assess the

best strategy to adopt for forwarding bulk data bun-

dles (i.e. hundreds of kilobytes) with varying delay

requirements. For bulk transfer of information, what

is important is that data is sent over reliable, shortest

path links to the end destination. This means avoid-

ing retransmissions and asymmetry where possible by

building and maintaining a reliable tree. Changes to

the network topology can incur a very large energy

cost if a node has a large volume of data that has to

‘backtrack’ through the tree. If a link which was pre-

viously good fails, the question is whether to delay

transmission or to reroute to another node. In order

to decide these questions, it is necessary to know how

links vary over time, and what level of stability can be

expected.

In this study, we attempt to investigate the causes

behind link variation in an outdoor deployment due

to rainfall. This paper presents results from an out-

door deployment in a forest environment over a pe-

riod of 26 days. The quality (both RSSI and LQI)

of all possible links was measured every 5 minutes.

Correlations were performed with data provided by

an advanced meteorological station, part of a system

measuring climate change, located only 500 m away.

148

Markham A., Trigoni N. and Ellwood S. (2010).

EFFECT OF RAINFALL ON LINK QUALITY IN AN OUTDOOR FOREST DEPLOYMENT.

In Proceedings of the International Conference on Wireless Information Networks and Systems, pages 148-153

DOI: 10.5220/0002959201480153

 SciTePress

0 20 40 60 80 100 120

X distance [m]

10 11 12

100

Y distance [m]

Figure 1: Layout of nodes within the deployment area.

Unlike other work, the sole purpose of this work was

to monitor link quality. Thus, there are no interfering

effects from network trafﬁc or MAC layer dependen-

cies.

2 EXPERIMENTAL SETUP

A total of 12 T-Mote SKY nodes were deployed in

Wytham Woods, Oxford, from the period 17/02/2009

to 14/03/2009 (26 days). The nodes were powered by

two Energizer lithium AA batteries and placed within

a waterproof, protective plastic enclosure. Metal

shelving brackets were used to afﬁx the nodes to tree

trunks, at an approximate height of 1.8 m above the

ground.

Nodes transmit short beacons (10 bytes, includ-

ing CRC) with an average period of 3 s, uniformly

dithered within this period. The random number gen-

erator was seeded with the node’s unique MAC ID so

that nodes would not be synchronized with one an-

other. The beacons contain the node’s ID and its cur-

rent timestamp. Nodes transmit the beacons at the

highest power level (+0 dBm). Every 5 minutes (uni-

formly dithered), nodes switch to receiving mode and

listen to incoming beacons for a total of 6 s. They

record their own timestamp, the incoming node’s ID

and its timestamp, and also the LQI and RSSI as pro-

vided by the CC2420 radio. This information is stored

in the 1 Mbyte external ﬂash, and is uploaded at the

end of the study. When not transmitting or receiving,

nodes power down their radio modules and enter low

power sleep mode.

The radio modules were conﬁgured to use Zigbee

Channel 24. In the deployment area, there were no

interfering WiFi signals. The layout of nodes in the

deployment area is shown in Figure 1.

3 EXPERIMENTAL RESULTS

3.1 Long-term Link Stability

In this section we examine the stability of links, i.e.

if a link that is good on a certain day, will remain

good for the duration of the study. This is impor-

tant for MAC protocols as it indicates the necessary

frequency of route repair. The performance of every

(unidirectional) link is shown in Figure 2. The av-

erage packet reception rate is indicated by the inten-

sity of each cell, with good links represented by white

and bad links represented by black. Links have been

sorted according to their initial reception rates.

Figure 2 demonstrates that the majority of links,

in general, show little long term variation. Some

links do show signiﬁcant variation, changing from

very high throughput to very low throughput (e.g. link

number 70). However, what is interesting about the

diagram of evolution is that it shows that there are cor-

related variations in link performance, e.g. on Febru-

ary 23 there is signiﬁcant variation across all links.

A similar pattern is seen around March 4. This sug-

gests that there is some common effect which is af-

fecting all links. Note however that the variation is not

all in the same direction – some links have improved

whereas others have become worse. In the following

sections we investigate causes for these variations.

3.2 Correlation with Environmental

Variables

In this section, we examine the correlation of the

number of good links in the network (those with over-

all throughput greater than 90%) with a variety of en-

vironmental variables. Data for this study were col-

lected by an automated weather monitoring system

located approximately 500 m from the deployment

area. Table 1 shows the linear (Pearson’s product mo-

ment) correlation coefﬁcients for the various parame-

ters. The results show that the two parameters which

show negative signiﬁcant correlation (at the 95% con-

ﬁdence level) are rainfall and ambient temperature.

Surface wetness, which indicates the presence of wa-

ter droplets also shows a negative correlation. Corre-

lations for the other measured variables are all low.

3.3 Variation in Link Performance with

Rainfall

The previous section indicated that rainfall was the

physical variable which had the strongest impact on

link performance. In order to quantify the effect of

EFFECT OF RAINFALL ON LINK QUALITY IN AN OUTDOOR FOREST DEPLOYMENT

149

Figure 2: Evolution of links over time, using a three hour binning. The intensity of each cell represents the packet reception

rate, with white representing 100% and black 0%. Links have been sorted according to their packet reception rate in the ﬁrst

three hours of operation, with bad links at the top of the ﬁgure and good links at the bottom of the ﬁgure.

Table 1: Correlation coefﬁcients.

Solar radiation 0.056

Net radiation -0.033

Relative Humidity -0.068

Ambient Temperature -0.247

Wind Speed -0.008

Rainfall -0.291

Albedo (Sky) 0.056

Albedo (Ground) 0.069

Surface wetness -0.154

rainfall on link stability, we characterized links on

March 1 into categories based on their throughput.

Good links are those with a throughput of over 90%,

whereas poor links are those with a throughput lower

than 50%. Fair links are deﬁned as those with a

throughput between 50% and 90%. The purpose of

this investigation is to determine the effect of rain on

different link categories.

The variation of good links over time is shown in

Figure 3. This shows that on the onset of rainfall on

March 3, 27.5% of links which were good on March 1

became poor or fair. Note that links did not instantly

become good again once the rain had stopped. By

March 6, all links are back to being good again. When

it rains again on March 7, 10% of links degrade in per-

formance to become fair.

The variation of poor links over time is shown in

Figure 4. This shows that rainfall has the effect of

actually improving link throughput, as evidenced by

the fact that 37.5% of poor links have become fair in

performance. Similar to the behaviour of good links,

by the 6 March, all but one link has returned to poor

performance.

3.4 An Investigation into Rain-induced

Fading

The correlation results show that there is a negative

correlation between precipitation and link success.

The results from Section 3.3 demonstrate that some

links improve, whereas others become worse. We

ﬁrst investigate whether this variation could be due to

fading caused by rainfall itself. An approximate em-

pirical relationship between attenuation A and rainfall

rate R is given by

A = αR

(1)

where α and β are parameters that are dependent on

frequency and temperature (Olsen et al., 1978). A

number of models provide values for these parame-

ters depending on weather conditions. The path loss

at 2.5 GHz under extreme rain conditions of R = 250

mm/hr, using the worst case thunderstorm distribution

of Joss et al. (J-T), is calculated to be 0.124 dB/km

(α = 1.63 × 10

−4

;β = 1.2). With a maximum link

range of 100 m, the expected path loss due to rain-

fall is negligible at 0.01 dB. It should be noted that

higher frequencies (e.g. 10 GHz and above) suffer

heavily from rain fading and this attenuation is actu-

ally used in weather radars to measure precipitation

rate and rainfall drop size (Meneghini and Nakamura,

1990).

However, there is a clear rain related variation in

link quality, one which has been reported in other

work (Thelen et al., 2005). As this loss is not due

to the channel itself, there has to be another cause. As

we were using the onboard antenna, we conjectured

that this variation in link performance could be due to

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150

Mar 1 Mar 3 Mar 5 Mar 7 Mar 9

Number of links

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

Peak rainfall rate [mm/hr]

Poor Fair

Good Peak Rainfall Rate (mm/hr)

Figure 3: Evolution of links which were classiﬁed as good

on March 1. Notice how the number of good links decreases

greatly with the heavy rain on March 3.

water pooling on top of the plastic enclosure, creating

a reﬂective plane. Water has a relative static permit-

tivity (ε

) of approximately 80. This would have the

effect of altering the antenna radiation pattern which

could account for these changes.

In an attempt to determine if this could indeed ex-

plain the signiﬁcant variations in link quality, we con-

ducted a simple experiment, the details of which are

described in the following section.

4 IMPACT OF SURFACE

WETNESS ON RADIO

PROPAGATION

The deployment demonstrated that there was a causal

link between precipitation and link quality. However,

this effect could not be ascribed to path loss, as atten-

uation by rainfall at 2.4 GHz was shown to be negligi-

ble. To investigate the effect of water droplets present

on the plastic enclosure, two nodes were placed 10 m

apart in a laboratory environment. One node (within a

plastic enclosure) was placed on the ground and con-

ﬁgured to transmit beacons every 0.5 s. The other

node was connected directly to a PC and used to log

the RSSI of the incoming beacons. Water was sprayed

gradually onto the plastic housing of the transmitting

node over a period of 10 minutes.

In order to indicate moisture presence on the en-

closure, a simple moisture sensor was fabricated from

a small piece of perforated prototyping board. The

resistance of this device indicates the presence (low

resistance) or absence of water (high resistance). The

output from a potential divider with the sensor con-

nected to ground and a 10 kΩ resistor connected to

the 3V supply was applied to the ADC0 input of the

T-Mote SKY. A threshold ADC reading was chosen

which allowed the sensor to report the presence or ab-

sence of moisture.

The results from this experiment are shown in Fig-

Mar 1 Mar 3 Mar 5 Mar 7 Mar 9

Number of links

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

Peak rainfall rate [mm/hr]

Poor Fair

Good Peak Rainfall Rate (mm/hr)

Figure 4: Evolution of links which were classiﬁed as poor

on March 1. Notice how the number of fair links rises with

the heavy rain on March 3.

ure 5. Initially, the RSSI level is -77 dBm. At time t

= 100 s, water was sprayed on the plastic enclosure.

Every further 60 s, more water was added. The RSSI

plot demonstrates that the addition of water ﬁrst has

the effect of increasing the received RSSI by approx-

imately +4 dB. However, as more water is added, the

RSSI decreases below its original value by approx-

imately -6 dB. Overall, this is a 10 dB variation in

signal strength.

5 DISCUSSION OF RESULTS

These results show that the presence of water on the

plastic enclosure has the effect of altering the received

signal strength. However, this variation can be posi-

tive or negative, depending on the amount of water

present. Water, with its high dielectric constant, alters

the radiation pattern of the antenna, essentially acting

as capacitive loading. Large amounts of water in close

proximity to the antenna have the effect of detuning

it, reducing its efﬁciency. These results demonstrate

that signal strength variation is due to the presence of

water on the enclosure, not channel attenuation due

to precipitation. As condensation can occur on nodes

without rain falling (e.g. dew), precipitation is thus

not the only indicator of channel variation.

Due to the causal link between water on the en-

closure and channel variation, it would be beneﬁcial

for nodes to be able to measure this presence. Nodes

could then take informed decisions as to the cause of

a link failure/improvement. We presented a simple

moisture sensor based on measuring the resistance be-

tween two electrodes. This method however has the

drawback that the sensor needs to be mounted on the

exterior of the box. Due to the presence of water, cor-

rosion of the electrodes will occur over time. To some

degree this can be lessened by applying an AC drive

current to the sensor. A better method of measuring

water presence would be to place two parallel plates

to the interior of the enclosure and measure the ca-

EFFECT OF RAINFALL ON LINK QUALITY IN AN OUTDOOR FOREST DEPLOYMENT

151

0 100 200 300 400 500 600 700

Time [s]

ABSENT

PRESENT

-90

-85

-80

-75

-70

RSSI [dBm]

RSSI

Water Sensor

Figure 5: Experimental results demonstrating variation in

RSSI with water presence on plastic enclosure. At 100 s,

water was sprayed on the plastic housing. Approximately

every 60 s thereafter, more water was added. Note how

the received signal strength initially increases and then de-

creases once a large amount of water is present. Node sep-

aration was approximately 10 m in a laboratory setting.

pacitance. Water present on the exterior of the box

will increase the capacitance. This would be a much

more robust technique, as the sensor element is not

exposed. Some microcontrollers even incorporate a

module for measuring capacitance e.g. the Charge

Time Measurement Unit (CTMU) of the PIC24F se-

ries of microcontrollers (Microchip, 2010), allowing

accurate measurements to be made without requiring

external components.

As identiﬁed above, links vary in different ways

according to the presence of water. In a delay-tolerant

network setting, nodes can defer exchange of bulk

data until conditions are favourable for network trans-

mission. This can save considerable energy, by avoid-

ing lossy links. To learn this information, periodic

probe packets can be sent, and the relationship be-

tween link quality and surface wetness modelled at

each node. This could easily be done using a simple

histogram, but a polynomial model relating link qual-

ity to wetness could also be utilized.

6 RELATED WORK

One of the major investigations into channel propa-

gation in outdoor wireless sensor networks was con-

ducted in potato ﬁelds (Thelen et al., 2005). Mica2dot

nodes were used, with external whip antennas with

a transmission frequency of 433 MHz. The authors

found that transmission was better during conditions

of high humidity, such as overnight and during rain-

fall. They speculated that the cause for this could be

reﬂection from the vegetation canopy, but did not con-

duct further experiments to investigate whether this

was in fact the cause for variation. Another study

which used both Mica and Mica2dot motes, this time

operating at 868 MHz, found very signiﬁcant reduc-

tions in transmission range, a reduction from 55 m

to 10 m, during rain and fog (Anastasi et al., 2004).

However, no details were given on the amount of rain-

fall or the duration of the reduction. The authors state

that attenuation of the electromagnetic wave is due to

absorption by water particles, but provide no valida-

tion of this claim.

There is a much larger body of work which re-

ports on link quality in an indoor setting. The work

in this paper has considered link variations over long

periods of time (hours), whereas short term link esti-

mation (milliseconds) is considered by (Becher et al.,

2008) and in (Cerpa et al., 2005). Short term estima-

tion also requires frequent probe packets. An alter-

native approach to link estimation is link characteri-

zation which uses bounds on link metrics instead of

instantaneous measurements (Lin et al., 2008). In a

much more detailed investigation, a similar approach

is speciﬁed in order to estimate link quality based

on probe packets sent at the start of the deployment

(Meier et al., 2008). Over 8 million packets were

recorded for each trial. A supervised learning tech-

nique for classifying links is presented in (Wang et al.,

2007).

7 CONCLUSIONS

In summary, we have demonstrated that there is a rela-

tionship between rainfall and link performance. This

is a result which has been reported on in other work,

but the causes of this variation have not yet been ad-

equately explained. We showed that the actual fac-

tor which alters link performance is not rainfall itself,

but the presence of water on the exterior of the enclo-

sure. This explains why links take time to recover

from a precipitation event as they slowly dry over

time. Water capacitively loads the antenna, which al-

ters its radiation pattern. In some instances links be-

come worse, but poor links can also improve in per-

formance. It is a simple matter to equip a node with

a sensor that can measure the presence of water. This

information can be used by the MAC and networking

layers to take informed decisions about routing and

link management.

Based on our initial investigation, we plan to in-

corporate capacitance based moisture sensors in a fur-

ther deployment, and to use knowledge of moisture

to inﬂuence routing. We also intend to investigate

whether external antennas suffer from water presence

to the same degree.

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152

ACKNOWLEDGEMENTS

This work was supported by EPSRC WildSens-

ing grant (EP/E013678/1). Meteorological data for

Wytham Woods was kindly supplied by Michele Tay-

lor, Environmental Change Network.

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