AIRWISE
An Airborne Wireless Sensor Network for Ambient Air Pollution Monitoring
Orestis Evangelatos and Jos
´
e D. P. Rolim
Department of Computer Science, University of Geneva, Geneva, Switzerland
Keywords:
Airborne Systems, Wireless Sensor Networks, Air Quality, Pollution Monitoring.
Abstract:
Over the last decades with the rapid growth of industrial zones, manufacturing plants and the substantial
urbanization, environmental pollution has become a crucial health, environmental and safety concern. In par-
ticular, due to the increased emissions of various pollutants caused mainly by human sources, the air pollution
problem is elevated in such extent where significant measures need to be taken. Towards the identification
and the qualification of that problem, we present in this paper an airborne wireless sensor network system for
automated monitoring and measuring of the ambient air pollution. Our proposed system is comprised of a
pollution-aware wireless sensor network and unmanned aerial vehicles (UAVs). It is designed for monitoring
the pollutants and gases of the ambient air in three-dimensional spaces without the human intervention. In
regards to the general architecture of our system, we came up with two schemes and algorithms for an au-
tonomous monitoring of a three-dimensional area of interest. To demonstrate our solution, we deployed the
system and we conducted experiments in a real environment measuring air pollutants such as: NH
3
, CH
4
,
CO
2
, O
2
along with the temperature, relative humidity and atmospheric pressure. Lastly, we experimentally
evaluated and analyzed the two proposed schemes.
1 INTRODUCTION
The atmospheric composition has been continuously
changing over the past thousands of years but it is
just after the industrial revolution of the 18th cen-
tury when the atmosphere started to be significantly
effected. The huge growth of urbanization and the
massive construction of polluting factories and indus-
trial cities, coupled with the lack of legislation and
standards for the atmospheric pollutants, led to a pro-
gressively increase of the concentrations of danger-
ous gases in the air. As the atmosphere is essential
to support life on our planet, air pollution has long
been recognized as a serious threat to human health
and to the whole ecosystem. In that context, over the
last few decades, governments and NGO’s have set
rules in the emissions of harmful substances in the
atmosphere. Since the early 1970s the EU Air Qual-
ity Directive (EUA, ) and the U.S National Ambient
Air Quality Standards (NAAQS) (Chow et al., 2007)
have been working on improving the air quality by
controlling those emissions and define maximum at-
mospheric concentrations.
Due to the hazardous effects of the air pollution
to the people and to the environment, air quality eval-
uation is playing an important role in the assessment
of the limits in the exposure of the population and the
minimization of health impacts. Human exposure to
air pollutants may have serious health effects depend-
ing on several factors such as: duration, magnitude
and frequency of the exposure. People in their ev-
ery day life come in contact with various pollutants in
the air both indoors and outdoors. As a matter of fact,
air quality monitoring is crucial not only for assessing
the exposure of the population to the air pollution but
it can also be proven extremely useful for scientists in
improving the pollution prediction models. In addi-
tion it can be used to provide emergency information
in the cases of unpredictable disasters. Taking into ac-
count the importance of the air pollution monitoring,
it is very challenging to monitor how the ambient pol-
lutants are dispersed and diluted in the air both hori-
zontally and vertically. In particular is at high interest
a fine grained monitoring in different spatial and tem-
poral distributions.
In relation to the ambient air quality monitoring,
several methods and techniques have been developed.
Traditionally, the monitoring is done with the use of
large monitoring stations placed in static locations
such as on top of towers and buildings. However, due
231
Evangelatos O. and D. P. Rolim J..
AIRWISE - An Airborne Wireless Sensor Network for Ambient Air Pollution Monitoring.
DOI: 10.5220/0005203302310239
In Proceedings of the 4th International Conference on Sensor Networks (SENSORNETS-2015), pages 231-239
ISBN: 978-989-758-086-4
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
to their large size and cost of maintenance, these sta-
tions are deployed in relatively spatial areas and con-
sequently they can not act as mobile stations. One
of the main contributions of our work is the solution
towards this problem; the development of a mobile
monitoring system for the monitoring of the ambient
air pollution.
In this paper we present a WSN system for au-
tomated ambient air quality monitoring. Air qual-
ity sensors integrated with embedded devices enable
the measurement of the air pollution in a very ef-
ficient and low-cost way. Our proposing system is
able to measure with the use of unmanned aerial ve-
hicles (UAVs) and WSNs and without the need of hu-
man intervention, the concentrations of several pol-
lutant, gases and environmental parameters, in three-
dimensional environments. We name our system:
AIRWISE.
The paper is organized as follows: in Section II
the related work and motivation is presented. In Sec-
tion III we propose the theoretical schemes and algo-
rithms as well as the implementation of the AIRWISE.
In Section IV we present the system development to-
gether with our experimental results and their evalu-
ation. Conclusions and future work are presented in
Section V.
2 RELATED WORK
The significant advantages in distributed sensor net-
work systems including but not limited to reliability,
scalability, dynamicity and efficiency, have brought
the WSN systems into the next generation. WSN sys-
tems play an inevitable role in our everyday life and
they have been widely adopted in sensing and moni-
toring applications. In (Evangelatos et al., 2013) we
have proposed a framework with which we can sense,
monitor and control an environment by using WSNs.
Apart from the use of WSNs in the area of smart en-
vironments, lately they have been used also in the
context of air sensing and monitoring. Such a sys-
tem for example, is described in (Hu et al., 2011),
where sensors have been placed on top of cars form-
ing a vehicular WSN dedicated to measure the pol-
lutants’ concentrations. In addition, the authors in
(Yaacoub et al., 2013) have developed a monitoring
system for ground level air quality analysis in Qatar
using a WSN. A system using WSN devoted to the
monitoring of particular pollutants has been proposed
in (Wang et al., 2010), where carbon monoxide (CO)
sensors were used for the monitoring of the CO lev-
els in the premises of a university campus area. Other
similar systems that have been developed for air qual-
ity monitoring using WSN are proposed in (Chen
et al., 2013) where the authors have designed a WSN
node for remote monitoring of CO and in the (Kavi
K. Khedo, 2010) where it is proposed a simulation
system for air pollution monitoring using WSNs.
Previous work regarding the air quality and the as-
sessment of health impacts near the airports of UK
(Yim et al., 2013) showed that high amounts of pollu-
tants such as CO and NO
x
are emitted in the air dur-
ing the take off and the approach of a plane in an air-
port. Similar works such as the (Solazzo et al., 2013)
and (Lee et al., 2013) are presenting models and es-
timations on the concentrations and behaviour of the
pollutants in the air. In these regards we believe that
those models and estimations could be verified and
improved with the help of a WSN which would mea-
sure those pollutants in real environments. The au-
thors in (Barakeh et al., 2014) are proposing a frame-
work with which they can monitor in real time partic-
ulate matter evolution in construction sites in order to
assess the air quality, but although such a system can
provide a lot of important information on air quality,
it is static and bound to the ground.
Due to the recent advancements in robotics, aviation
and material sciences, the gap between airborne sys-
tems and WSNs has started to be shortened. Drones
are being used in a great variety of applications rang-
ing from supporting search and rescue operations
(Waharte and Trigoni, 2010) to aerial robotic con-
structions (Willmann et al., 2012). In addition, with
the technological advancements in 3D printing and
laser-cutting technologies, it is possible to manufac-
ture low-cost drones with individual features (Nickel
et al., 2014). The prior work of (Valente et al.,
2011) has used a quadrocopter-drone for implement-
ing a cropping monitoring system in the research
field of precision agriculture using WSNs. In (Jude
et al., 2007) the authors have developed a WSN com-
posed of bird-sized micro aerial vehicles and ground
nodes in which they have analyzed networking perfor-
mances, such as RSSI behaviour and packet loss rates.
Experimental results on the integration of UAVs and
WSNs have been presented in (Teh et al., 2008).
Systems and deployments that have been pro-
posed so far are mainly investigating individually, or
in the most relevant works two out of the three follow-
ing domains: air quality monitoring, WSN and UAV.
To the best of our knowledge there has not been yet
proposed a system that combines WSNs, drones and
air pollution monitoring systems. Our work presents
a low-cost, automated pollution monitoring system
which is comprised of a wireless network with sensors
dedicated for measuring the concentration of air pol-
lutants and UAV for performing the measurements in
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different altitudes, latitudes and longitudes. We came
up with two schemes and algorithms resulting in a
system’s application that can monitor in fine-grained
resolution and in near-real time, the ambient air qual-
ity in real three-dimensional spaces using WSNs. The
information acquired from the system regarding the
the pollutants’ concentrations in the ambient air could
be provided as profitable resource data to air quality
scientists for improving their environmental models,
to governments as prerequisite information for index-
ing the air quality of their districts and last but not
least as influential dissemination information to the
people in order to uphold their environmental aware-
ness.
3 ARCHITECTURE OF AIRWISE
In our paper we present an Airborne WSN system
with which we can monitor the ambient air pollution
in three-dimensional real space environments. The
measurement of the pollutants in the air is being done
by pollution sensors which are placed on top of un-
manned aerial vehicles (drones). Drones have the
ability to fly and hover in the air both manually and
automatically. The general design, algorithms and ar-
chitecture of the AIRWISE system is divided in the
following two categories: A. the theoretical models
and B. the implementation design. In the following
subsections we present, firstly the theoretical models
and afterwards the system’s implementation design.
3.1 Theoretical Models, Schemes and
Algorithms
In our work, in order to deal with the measurement
of the three-dimensional air space environment, we
propose the following general approach to facilitate
exposition: we divide the three-dimensional area we
want to investigate (denoted hereinafter as D) into
”small” equally tessellated cubic-subareas (named as
monitor-cubes). The three-dimensional area D with
its monitor-cubes is depicted in Figure 1. By dividing
the whole area of interest D, into these monitor-cubes,
we are able to distributively monitor the concerned
environment and extract individual pollution data for
each of them separately. This allows us to create sep-
arate ”heat” and history pollution maps for each dif-
ferent physical subareas as well as of the whole area
D. The size of each subarea (monitor-cube) can be
defined by the user in accordance with the location
and the circumstances of the monitoring area. At the
same time, this tessellation gives us the possibility of
conducting both fine-grained and macro-scaled mea-
surements. We designate that the measurements in
each monitor-cube regarding the pollutants, are taken
from their center. Our approach, definitions, schemes
and algorithms described below hold for both types of
measurements; fine-grained and macro-scaled.
3.1.1 General Definitions
Prior to the description of our approach and models,
we need make the following general definitions:
Monitor-Cubes (Subareas S
(x,y,z)
). To facilitate the
exposition of our schemes and algorithms, we as-
sume without loss of generality, that the area D is cu-
bic. As described above, the three-dimensional area D
for monitoring the air quality, is tessellated into sev-
eral ”small” cubic subareas S, which we denote as:
S
(x,y,z)
, where x | x [0, k] (respectively y | y [0, l]
and z | z [0, m]) and k+1 (respectively y+1 and z+1)
is the number of division of the first dimension (re-
spectively of the 2nd and the 3rd) of D. The area D
and its tessellation into the subareas S is depicted in
Figure 1.
Concentration of Pollutant a (
CPa): There are
several pollutants existing in the air such as: Nitro-
gen Oxides (NO
x
), Carbon Oxides (CO
x
), Ammonia
(NH
4
) etc, and their concentrations vary depending
on a number of several parameters such as the: loca-
tion, altitude, temperature etc. We define the vector:
Concentration of Pollutant ”a” (
CPa) which repre-
sents the measured concentration of a pollutant ”a in
the air. The value of this parameter is obtained by the
pollution sensor and its metric is usually in ppm (parts
per million). Subsequently, the
CPa is normalized
between [0,1] in respect to the minimum and maxi-
mum concentration values the pollution sensor is able
to measure.
Weight (
W
(x,y,z,i)
). For each subarea S
(x,y,z)
we define
a weight W
(x,y,z,i)
where i N. The weight W
(x,y,z,i)
represents the arithmetic mean of the measured con-
centration of the pollutant
CPa in a specific subarea
S
(x,y,z)
of the iteration (monitoring) cycle i. The term
iteration cycle represents one completed monitoring
of the whole area D and its value i represents the i-th
cycle.
Measuring Rate (MR). As MR we define the con-
stant value which represents the measuring rate with
which the pollution sensor is collecting pollution data
from its nearby environment. The MR can be defined
as MR = Samples / Second.
Duration of Measurement (DM). As the pollutants
in the air some times could be burdensome to mea-
sure, long time measurements might be required to be
collected. Therefore, we define the value: Duration of
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233
Figure 1: Area D for monitoring and its tessellation to Sub-
areas S
(x,y,z)
.
Measurement (DM), to represent the duration of the
measuring process. Depending on the environmen-
tal variables of the specific time and location, short
time measurements might suffice to collect trustwor-
thy data. However in situations such as toxic or harsh
environments, long time measurements might be re-
quired to obtain more accurate results.
3.1.2 Schemes
In this section we present two different schemes with
which we approach the problem of monitoring the
ambient air pollution in 3-D spaces. For each of them
we present as well their respective algorithms.
Sequential Monitoring Scheme In the Sequential
Monitoring Scheme, the routing of the drone and sub-
sequently the collection of the pollution data by the
sensors it carries on, are done in a sequential man-
ner. This means that the drone is routed in a static
and predefined trajectory whereas the sensors are col-
lecting data systematically from the center of each
subarea S. The sensing process and hence the routing
pattern starts from the subarea S
(0,0,0)
and it covers
progressively all the subareas until it will arrive to the
subearea S
(x,y,z)
. At that point one iteration cycle (i)
will have been completed. Then the flying and sens-
ing process will restart from the subarea S
(0,0,0)
.
Sequential Monitoring Algorithm (SMA). The
pseudo-code of the Sequential Monitoring Algorithm
(Algorithm 1) representing the sequential monitor-
ing scheme is presented below. For each iteration
cycle and for each subarea, the algorithm measures
the concentration of the pollutants and calculates their
Weight W .
Dynamic Monitoring Scheme. In order to use
more efficiently the limited and constrained resources
of the airborne systems and the WSNs, we propose
another monitoring scheme which acts in a dynamic
Algorithm 1: Sequential Monitoring Algorithm
(SMA).
Input: Values of: MR, DM, k,l,m
Output: The Weight W
(x,y,z,i)
MR default Measuring Rate
DM default Duration of Measurement
k, l, m size of each axis of area D
max i maximum iteration cycles
x, y, z, i 0
begin
while i < max i do
for z 0 to z = m do
for y 0 to y = l do
for x 0 to x = k do
CPa Take MR · DM
samples of pollutant a
W
(x,y,z,i)
Arithmetic mean
of
CPa
x + + %next subarea of x
axis
y + + %next subarea of y axis
z + + %next subarea of z axis
i + + %next iteration cycle
x, y, z 0 %restart from S
(0,0,0)
return W
(x,y,z,i)
end
way. In this scheme the subareas are given a potential
of being monitored or not, depending on their previ-
ous weight values. We consider a subarea as stable
when its most recent weights W do not alter ”much”
during a specific time frame. In that case, we can
avoid visiting and consecutively avoid monitoring a
stable subarea. As a result we can use more efficient
the limited energy of both the drone and the sensors
whilst increasing the time efficiency of the system as
well. Lower energy consumption could be translated
into monitoring of larger areas and for longer periods.
In order to better describe the dynamic monitor-
ing scheme, some further definitions in extension to
the general ones (mentioned for the sequential moni-
toring scheme), are needed to be made;
Minimum Iteration Cycles (min i). The parameter
min i is used to define the number of minimum iter-
ation cycles (monitoring cycles) for which the algo-
rithm will keep collecting data from all the subareas,
before it will enter into the dynamic mode.
Threshold (Thr). The Thr, threshold parameter is
an upper bound of the mean accumulated difference
between subsequent weights over a specific number
of consecutive iteration cycles. Subareas for which
their most recent weights are remaining ”almost” in-
variable, are deliberated as stable subareas. More-
over, the Thr describes the sensitivity of the algo-
rithm. With the term sensitivity we refer to the degree
of the pollution variation each subarea is allowed to
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sustain in order to be considered as stable. It is an
important parameter, as it allows the adjustment of
the tradeoff between the sensitivity of the monitoring
process versus the time and the energy needed to com-
plete an iteration cycle i.
Last iteration Cycles to Compare (LiC): The pa-
rameter LiC delineates the number of the most recent
iteration cycles whose W will be used in the compar-
ison with the threshold Thr.
Idle value (Idle
(x,y,z)
). The Idle
(x,y,z)
is a parameter
which represents the number of iterations for which a
subarea remains in stable mode and thus is not being
monitored.
Maximum Idle state (maxId). The maxId bounds
the maximum iteration cycles for which a subarea is
allowed to stay inj Idle considered as stable subarea.
It is used to ensure the reliability of the algorithm in
terms of avoiding the formation of holes and to guar-
antee the refreshness rate of each subarea. It assures
that there will not exist any ”ghost-subareas” i.e. ar-
eas which might remain unmonitored for a ”long” pe-
riod of time.
Dynamic Monitoring Algorithm (DMA). In this
subsection we present the dynamic monitoring al-
gorithm which represents the dynamic monitoring
scheme. In this algorithm, for each subarea and for
each iteration cycle, the concentration and the weights
of their pollutants are measured. The same concep-
tion holds for the SMA with the main difference that
the DMA takes into consideration the property that a
subarea might be monitored or not depending on its
stability parameter. Initially the algorithm will moni-
tor the area D for a minimum iteration cycles (min i)
before it will start taking into account the stability
parameter of each subarea. The maximum iteration
cycles for which the algorithm will be executed is
set by max i and the maximum idle iteration cycles
(Idle
(x,y,z)
) for which a subarea can remain at stable
is set by maxId. The algorithm 2 is presented below.
3.1.3 Complexity
In this section we present and compare the time com-
plexity of our two proposed algorithms. In the first
scheme (SMA), the visiting pattern of the subareas by
the drone and hence their monitoring by the sensors,
is done in a continuous-sequential way, in which all
the subareas are monitored in every monitoring cy-
cle. To measure the time complexity of the two algo-
rithms, we consider the number of measurements per-
formed assuming the following: k=l=m=n-1 (in par-
ticular the x axis is tessellated in n equal parts and the
same holds for the y and z axis); the transportation
Algorithm 2: Dynamic Monitoring Algorithm
(DMA).
Input: The values:MR, DM, k,l,m, min i, Thr, LiC,
maxId
Output: The Weight W
(x,y,z,i)
MR default Measuring Rate
DM default Duration of Measurement
k, l, m size of each axis of area D
min i minimum iteration cycles
max i maximum iteration cycles
T hr Threshold defining an subarea as ”stable”
LiC Last iteration Cycles to Compare
maxId Maximum Idle-state value
x, y, z,i 0
begin
while i < max i do
for z 0 to z = m do
for y 0 to y = l do
for x 0 to x = k do
if i > min i and
i
iLiC
|W
(x,y,z,i1)
W
(x,y,z,i)
|
LiC
<
T hr
and Idle
(x,y,z)
< maxId then
W
(x,y,z,i)
W
(x,y,z,i1)
Idle
(x,y,z)
+ +
else
CPa Take MR · DM
samples of pollutant a
W
(x,y,z,i)
Arithmetic mean
of
CPa
Idle
(x,y,z)
0
x ++ %next subarea of x axis
y ++ %next subarea of y axis
z ++ %next subarea of z axis
i ++ %next iteration cycle
x, y, z 0 %restart from S
(0,0,0)
return W
(x,y,z,i)
end
time Tt needed to move from one subarea to a neigh-
boring one in comparison to the monitoring time
needed to monitor a subarea (Tm = MR·DM) is neg-
ligible, i.e. Tt<<Tm. Therefore the time complexity
of the SMA algorithm is: n
3
·MR·DM . In the second
scheme (DMS), the visiting pattern of the drone and
the monitoring of the area D, is done in a dynamic
way based on the decision of whether a subarea is
stable or not. Considering a subarea as stable allows
the system to bypass it and move to the next subarea.
The efficiency of this algorithm lies in the fact that
some subareas might not be monitored which results
in less power consumption of the whole system, or
in extended monitor space. The time complexity of
the DMA algorithm is O(n
3
·MR·DM), but depending
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235
on the algorithm’s input values and the environmental
parameters the DMA could perform even better than
that.
3.2 Implementation of AIRWISE
As far as the implementation of the AIRWISE sys-
tem is concern, we had to face the following chal-
lenges : the limited energy resources of the unmanned
aerial vehicles and the sensor nodes; the assembly of
a lightweight UAV which would be able to carry on
the additional payload of the sensor node; the integra-
tion of a flying mechanism that could enable the UAV
to fly also autonomously; and lastly the development
a WSN system that would be able to support the mo-
bility of the UAV in a three-dimensional environment
and transmit its data in near-real time. The imple-
mentation and our proposing solution towards those
challenges are divided in two subsystems which we
present below: the airborne-flying subsystem and the
WSN subsystem.
3.2.1 Airborne Subsystem
Due to the nature of the problem of monitoring the
ambient air quality, one of the key requirements that
we needed to face was the implementation of a sys-
tem that would be able to take measurements in
the air in three-dimensional spaces. The solution
that we propose towards this challenge is the use of
unmanned aerial vehicles (UAVs) and in particular
quadrocopters. Quadrocopters have the ability to take
off and land horizontally, they are also able to spin
around their vertical axis and most importantly hover
in the air. Their ability of hovering in the air allow us
to maintain them in the air at specific positions for as
long as it is needed. Alternative airborne systems that
are using small planes are not able to hover and thus
are not suitable for our application. The drone (the
term is used interchangeably with the term quadro-
copter) that we use in our system is shown in Figure 2
(Left) and we self assembled it from parts which are
produced by 3DRobotics. It is a lightweight and pow-
erful APM Copter with a load capacity of approxi-
mately 600gr. It benefits from mechanical simplic-
ity and design flexibility and despite its small size it
is capable of lifting small payloads. The four blades
of the drone as well as its communications are con-
trolled via the ardupilot, which is an open source
UAV platform able to autonomously control multi-
copters. We equipped the drone with a GPS antenna
and with a telemetry set operating at 433Mhz. In
our implementation we used the version of ArduPi-
lotMega 2.6 which gives us a lot of advantages such
as: autonomous flight; automatic stabilization; nav-
igation using GPS; reception of telemetry informa-
tion and control of the drone in real-time using the
MAVLink protocol.
3.2.2 WSN Subsystem
To achieve the main goal of our work (i.e. to auto-
matically monitor the ambient air and extract infor-
mation regarding its quality) we use a wireless sen-
sor network. This network is comprised of two nodes
with gas sensing capabilities and one basestation for
receiving the data from those nodes. One node was
dedicated for the airborne measurements and the other
one for ground measurements used for comparisons.
Both of them were transmitting their collected data
to the basestation. The nodes are comprised of the
following components: a) an electronic board for ac-
commodating the gas sensors, b) the gas sensors, c) an
external antenna for communicating with the basesta-
tion, d) a main board with the processor, e) a GPS
module and f) a rechargeable battery. Due to the fact
that the nodes and their components are very sensi-
tive and fragile we designed and 3D-printed a cover
box to protect them. The complete assembled node,
its cover box and the basestation are shown in Fig-
ure 2 (Right). Both the nodes and the basestation we
used are manufactured by Libellium (lib, ). As far
as the nodes are concerned, we used as their main
board the Waspmote v1.2. The Waspmote node runs
with the ATmega 1281 microcontroller at a frequency
of 14.7456 MHz and with a memory of 128kB. On
top of the mainboard, an 2dBi XBee pro 802.15.4
antenna was integrated for communicating with the
basestation. In addition, a sensor board with tem-
perature, humidity, atmospheric pressure and gases
sensors was integrated. In particular, the gases sen-
sors that we installed were: Molecular Oxygen (O
2
),
Ammonia (NH
3
), Methane (CH
4
) and Carbon Diox-
ide (CO
2
) manufactured by Figaro(fig, ). Moreover,
we equipped the nodes with a GPS module so that
we could time-stamp and position-stamp the measure-
ments taken by the sensors. The energy supply of
Figure 2: (Left) Drone used in AIRWISE, including GPS
and telemetry antenna. (Right) Basestation, wireless sensor
node and node’s cover box.
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the nodes was provided by a Li-Ion rechargeable bat-
tery with a capacity of 6600mAh. The size of the
box including all the components was 8x8x7 cm and
it weighted in total 300gr. with a battery weighting
200gr.
On the other endpoint of our WSN subsystem,
the basestation was equipped with a 5dBI XBee pro
802.15.4 antenna. It was connected via a USB to a
computer for receiving and propagating the informa-
tion to the AIRWISE program, which we developed
in C#. This program was designed to be responsible
for logging all the information that is receiving, an-
alyze them in order to calculate the concentration of
the pollutants
CPa and their weights W
(x,y,z,i)
and as
well visualize them.
4 EXPERIMENTS AND
EVALUATION
4.1 Overall Experimental Set Up
The overall experimental set up of the system can be
seen in Figure 2. The weight of the drone itself was:
1.5kg and the additional weight of the sensor node
was 0.3kg resulting in a total weight of 1.8kg. For
our experiments we chose an area of 6.3 hectares in
a heterogeneous environment in-between of a small
forestall area and residential buildings.
The experiments we conducted regarding the AIR-
WISE system were divided in the three following cat-
egories: a) WSN behaviour, b) Airborne system be-
haviour and c)integration of the WSN and airborne
system.
4.1.1 WSN Experiments
Firstly we run experiments to determine the behaviour
of the WSN subsystem. In order to achieve highly ac-
curate calibration of the gas sensors, specific chemi-
cal gas tubes need to be used. However, as the mea-
surements of the pollutants with high laboratory ac-
curacy is out of the scope of this paper, the calibra-
tion of the gas sensors was done based on trial and
error. Nonetheless, even if we could not achieve high
accuracy we could obtain very accurate variations in
the concentration of the pollutants between different
measurements.
For our experiments we installed gas sensors for
CO
2
, CH
4
, NH
3
and O
2
, along with sensors for en-
vironmental parameters of temperature, humidity and
atmospheric pressure. The raw data acquired from the
gas sensors, the environmental sensors and the GPS,
Figure 3: AIRWISE program for receiving, analyzing and
visualizing the data collected from the sensor node.
were sent to the basestation using the XBee antenna in
four separate packets. Once the packets were received
by the basestation, the AIRWISE backbone program
running on a laptop analyzed the raw data and visual-
ized them in a user friendly way. A screenshot of the
program while it was receiving data from the wire-
less node is shown in Figure 3. In order to complete
one data gathering cycle (from the sensors described
above) at one specific location, it was required 1 min.
and 15 sec. This relatively big amount of time intro-
duced some energy and time related problems that we
will discuss below.
4.1.2 Airborne System Experiments
As far as the experiments of the airborne system are
concerned, we were able to operate the quadrocopter
described previously in two different flying modes:
the automatic and the manual one. The automatic
flying mode uses the APM 2.6, a GPS receiver, an
accelerometer and the ”mission planner” software in-
stalled on a laptop. Via this software we were able
to set specific waypoints in a area and program the
drone fly towards those waypoints. Once we set up
the waypoints on a graphical interface, we uploaded
them to the APM of the drone using the MAVLINK
protocol. The benefit of the automatic flying mode
enables the drone to take off and land without our in-
tervention. Moreover, we could send commands to
the drone,in real time, while it was flying to change
its direction. This was proven especially useful when
the pollution in some areas was higher than expected.
In the second flying mode of the drone i.e. the manual
one, we used a Futaba 7-Channel Radio Transmitter.
The auto-stabilization system of the APM stabilized
the drone even in the presence of strong winds. In or-
der to maintain the safety precautions, the drone was
landing when its battery was at 20%. Its maximum
flying time with a fully charged 5000mAh 11.1V LiPo
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237
battery without any payload, was approximately 15
minutes.
4.1.3 WSN and Airborne System Integration
Experiments
In the last set of experiments we combined and tested
the integration of the WSN and the drone. For this
category of experiments, we defined a fraction of our
overall experimental area, a small cubic area D. The
edges of this cubic area of interest were 39 meters
long with a volume of 59319m
3
. This area was tes-
sellated in 3x3x3 subcubes where the centers of each
subcube (subarea S) were 13 meters apart from each
other. Every time the measurements were gathered
from each subarea, the collected data were sent to the
basestation in near-real time, and simultaneously they
were also saved locally. Due to the additional weight
of the sensor node and its battery, the maximum flight
time of the drone was reduced from 15 to 12 minutes.
Initially we set up the sensor node to collect data from
all of its sensors (i.e. pollutants, environmental pa-
rameters and GPS). In these initial experiments, the
time needed to perform measurements from one sub-
area was 1 min. and 15 sec. and compared to the 12
min. of maximum flight time of the drone, we were
able to gather measurements only from 9 subareas.
Those 9 subareas correspond to only 0.33 iteration cy-
cles and for covering the whole area D we needed at
least 27 measurements ( i.e. one iteration cycle). The
traveling time from the endpoint of one layer to the
starting point of an other was in average 4 seconds.
4.2 Evaluation
In order to evaluate better our algorithms in this real
world development, we set the WSN subsystem to
measure only the CO
2
in the air, including though
GPS and environmental parameters. This shortened
significantly the subarea’s data gathering cycle to 15
seconds. The experience we acquire from this fact is
that for the time being the batteries of the drones, de-
spite being off the shelf, are not yet adequate to per-
form complex tasks. For this reason we do need to
develop efficient mechanisms to overcome those en-
ergy constraints.
Figure 4 shows the results we obtained from mea-
suring the CO
2
using the SMA and DMA during the
12 min. lifespan (flying time) of the drone. The SMA
scheme reported in this lifespan, measurements from
45 subareas. These 45 subareas correspond to 1.67
iteration cycles. On the other hand, using the DMA
scheme (with: Threshold at 0.5%, min
i
and LiC at 5
and the maxId at 10), for the same 12 min. life-span,
Figure 4: CO
2
reported concentrations using SMA and
DMA algorithms during the lifespan of the drone.
a total of 74 measurements were reported. These 74
measurements correspond to 2.74 iteration cycles.
Comparing the performance of the two algo-
rithms, we observe that the DMA algorithm performs
better and in particular it can report 29 more measure-
ments than the SMA with a 0.5% tolerance in the CO
2
concentration. Consequently, the DMA is approxi-
mately 64% more energy efficient. In Figure 4 we
can observe also that the two algorithms report almost
identical measurements. The only drawback using the
DMA scheme is that more messages need to be sent
and received which impacts negatively in the energy
consumption of the sensor node. Specifically, using
the DMA, the battery of the sensor was reduced by
4% whereas using the SMA it was reduced by 2%.
However, comparing the battery depletion rate of the
sensor node to the one of the drone, the difference is
almost negligible.
Due to the design of the DMA, it is let on the free-
dom of the system operator to decide the tradeoff be-
tween the sensitivity of the measurements and their
quantity. Meaning that a bigger value in the Threshold
would allow for more measurements while a smaller
value would allow for more precise ones. The advan-
tage of the near-real time monitoring of our system,
is that a meteorologist for example in a scenario of a
volcanic eruption, could change on-the-fly the trajec-
tory of the drone towards another area of interest. In
addition, in emergency pollution situations, by using
the architecture of the AIRWISE system, more drones
with more sensors could be dispatched for a more de-
tailed monitoring. The AIRWISE backbone system
which can be run on a laptop, makes the whole system
easily portable and transferable.
5 CONCLUSIONS AND FUTURE
WORK
In this paper we investigated the challenges of the
air quality monitoring and we presented a system-
SENSORNETS2015-4thInternationalConferenceonSensorNetworks
238
solution using WSNs and UAVs. We proposed a sys-
tem’s architecture together with a theoretical frame-
work and two schemes for monitoring the air pollu-
tion in 3D spaces. Furthermore, we showed the imple-
mentation of our approach with which the automatic
monitoring of the ambient air can be facilitated. We
have extended the capabilities of airborne systems by
coupling them with WSNs. In particular, we imple-
mented the AIRWISE system which is able to moni-
tor pollutants in the air such as: NH
3
, CH
4
, CO
2
, the
O
2
percentage and environmental parameters such as
temperature, humidity and atmospheric pressure. We
developed the system, we run experiments with it and
lastly we evaluated and compared our schemes and
algorithms in a real deployment scenario.
Our future work plans include scaled up experi-
ments with more drones and sensors acting in a col-
laborative way. In addition, we plan to investigate
the direct interconnectivity between the wireless node
and the autopilot system of the drone.
ACKNOWLEDGEMENTS
This work was partially supported by the EU/FIRE
IoT Lab project - STREP ICT-610477.
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