Joint PHY-MAC Realistic Performance Evaluation of Body-to-Body Communication in IEEE 802.15.6 and SmartBAN

Abstract

This paper presents the joint physical-medium access control (PHY-MAC) performance analysis of inter-BAN communication systems using realistic body-to-body (B2B) wireless channel model in IEEE 802.15.6 and smartBAN standards. The time-varying distances for the space-time B2B link variations are generated by real-time motion capture traces which are then introduced into already established B2B wireless channel model to give the actual path-loss values in dynamic environments. The SNR (Signal to Noise Ratio), BER (Bit Error Rate) and PER (Packet Error Rate) computations are briefly discussed to give an overview of the radio link modeling employed in the simulations. Using the mobility and the proposed radio link models, a more tangible performance assessment of B2B systems with IEEE 802.15.6 and SmartBAN specifications is achieved. Consequently, transmission power, packet length and data rate variations are investigated and the obtained results of packet reception rate (PRR) identify "head" as the best position to place the coordinator nodes for B2B communication.

Full text

Joint PHY-MAC Realistic Performance Evaluation
of Body-to-Body Communication in IEEE 802.15.6
and SmartBAN
Rida Khan, Muhammad Mahtab Alam
Thomas Johann Seebeck Department of Electronics
Tallinn University of Technology
Tallinn, Estonia
Email: {rikhan, muhammad.alam}@ttu.ee
Abstract—This paper presents the joint physical-medium ac-
cess control (PHY-MAC) performance analysis of inter-BAN com-
munication systems using realistic body-to-body (B2B) wireless
channel model in IEEE 802.15.6 and smartBAN standards. The
time-varying distances for the space-time B2B link variations
are generated by real-time motion capture traces which are then
introduced into already established B2B wireless channel model
to give the actual path-loss values in dynamic environments.
The SNR (Signal to Noise Ratio), BER (Bit Error Rate) and
PER (Packet Error Rate) computations are briefly discussed to
give an overview of the radio link modeling employed in the
simulations. Using the mobility and the proposed radio link
models, a more tangible performance assessment of B2B systems
with IEEE 802.15.6 and SmartBAN specifications is achieved.
Consequently, transmission power, packet length and data rate
variations are investigated and the obtained results of packet
reception rate (PRR) identify “head” as the best position to place
the coordinator nodes for B2B communication.
Keywords—WBANs; inter-BAN; mobility modeling; radio link
modeling; IEEE 802.15.6; SmartBAN; PRR.
I. INTRODUCTION
Wireless body area networks (WBANs) refer to a network
of sensors (and/or actuators) placed on, inside or around
the human body in order to serve a variety of emerging
applications [1]. WBANs not only offer a wide scope of
research and development but also represent a new generation
of personal area networks, with their own unique set of
challenges for implementation. The vital issues encountered
by WBAN technology include the mobility of WBAN nodes,
reliable low power operation, security and privacy of WBAN
data and coexistence of multiple WBANs in the same envi-
ronment [2]. WBANs have different types of communication
scenarios based on the relative positions of WBAN nodes. The
placement of communicating BAN nodes on multiple bodies
is attributed to body-to-body Networks (BBNs) [3]. BBNs
provide innovative solutions for a wide range of applications
such as remote health care, precision monitoring of athletes,
search and rescue operations in disastrous situations and
coordination of soldiers on a battlefield [3].
Most of the efforts in channel characterization of WBANs
have been dedicated to on-body communications and the
contribution of research efforts in body-to-body (B2B) channel
modeling is quite limited. Nonetheless, many noteworthy
contributions exist in the literature which attempt to discuss
B2B channel characteristics [4]-[6]. But these channel models
assume very limited mobility scenarios and for the realistic
performance evaluation of BBNs on the higher layers such as
medium access control (MAC) and network, accurate mobility
and radio link modeling should be taken into account. A
comprehensive analysis of the MAC layer performance evalu-
ation is presented in [7] for on-body communication scenario,
after measuring the channel characteristics when the nodes
are placed on a walking subject. The notion of integrating
realistic mobility traces with IEEE 802.15.6 channel models
for accurate performance analysis of on-body communication
at the MAC layer was proposed in [8]. Considering other co-
located WBAN signals as interference and jointly exploiting
on-body and B2B realistic channel models, a comprehensive
MAC level performance analysis of intra-BAN communication
is given in [9], [10]. However, to the best of our knowledge,
no research work has been dedicated so far to study the joint
physical-MAC (PHY-MAC) layer performance evaluation of
inter-BAN communication systems under realistic/unrestricted
mobility scenarios.
This research work is focused on the joint PHY-MAC
performance assessment of B2B communication over a dedi-
cated frequency channel with realistic channel models, using
IEEE 802.15.6 and smartBAN standards specifications. The
primary contributions of this paper include the identification
of suitable positions to place the BAN coordinators for B2B
communication and the examination of appropriate transmis-
sion power levels under different packet sizes and data rates.
With the help of real time motion capture data, mobility traces
are generated for multiple co-located BANs which provide
dynamic distances with space-time variations. These dynamic
distances serve to provide the realistic path-losses for B2B
links under various mobility profiles (e.g., walking, running,
standing etc.) using the B2B channel model derived by the real
time measurement campaign. Subsequently, a detailed radio
link modeling, based on the B2B channel characteristics, is
implemented in which signal to noise ratio (SNR), bit error
rate (BER) and packet error rate (PER) are computed using
Authors' version of the paper that appears in the proceedings of 2018 12th International Symposium on Medical Information and Communication Technology (ISMICT). DOI:
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the generated path-losses. The performance is examined in
terms of packet reception rate (PRR) and the PHY-MAC layer
specifications of both IEEE 802.15.6 as well as SmartBAN are
considered in this context. A thorough investigation of PRR
reveals that the relative coordinator nodes position is crucial
for reliable data transmission over B2B links under real time
dynamic environments.
The rest of the paper is arranged in the following way:
section II elaborates the system model whereas IEEE 802.15.6
and smartBAN PHY-MAC layer parameters are described in
section III. In section IV, the simulation results are presented
and discussed while section V gives the concluding remarks.
II. SY ST EM MO DE L
This section explains the underlying system model used in
performance evaluation, as given:
A. B2B Channel Model
We use B2B channel model derived in [11], [12], using
real time measurement campaigns under restricted mobility
scenarios. This channel model provides channel gain, long
term (LT) and short term (ST) fading components to estimate
the path-loss values and the channel characteristics are a
function of inter-body distance (d) and mutual body orientation
(α) for various inter-BAN links. These links include head-to-
head, belt-to-belt, wrist-to-wrist, head-to-belt, head-to-wrist,
belt-to-wrist and vice versa, as depicted in Fig. 1, and are
in-line with the links investigated in [11], [12]. In this model,
shadowing effects by the bodies are primarily dealt with the
distance and orientation-dependent channel gain, LT effects
caused by the environment are represented by LT fading and
ST fading is the outcome of the constructive and destructive
interference resulting from multi-path propagation. According
to [11], the distance (d) and orientation (α) dependent channel
gain can be stated in dB as
G(d, α) = G0(α)−10n(α)log10 d
d0,(1)
where ncorresponds to the path-loss exponent and G0rep-
resents the gain at the reference distance d0, equal to 1m.
G0(α)and n(α)show different characteristics for various
links between the relative node positions over different BANs.
For example, nand G0do not exhibit mutual orientation de-
pendence for head-to-head links between two different BANs
so, distance-based channel gain can be calculated with the
fixed values of nand G0. But mutual orientation αis crucial in
determining nand G0values for other links mentioned previ-
ously. Further information about G0(α)and n(α)calculations
for obtaining the channel gains corresponding to other links
can be found in [11]. The LT fading component in dB scale
can be characterized with a zero-mean normal distribution [11]
as
f(zLT ) = 1
σLT √2πexp −z2
LT
2σ2
LT ,(2)
where zLT is the LT fading component in dB and σLT is
the standard deviation, whose values for different B2B links
Fig. 1: B2B Channels.
are indicated in [11, Tab. 5]. The ST fading envelope can be
typically represented by Rice distribution [11], [12], as given
f(zST ) = zST
σ2exp −z2
ST −A2
2σ2I0zST A
σ2,(3)
where zST is the ST fading envelope, Ais the non-centrality
parameter and σrepresents the scale parameter. Rice K-factor,
the power ratio between the direct path and the multi-paths,
is given as K=A2
2σ2. The characteristics of head-to-head
links again do not show mutual orientation-dependence in
estimating Aand σvalues whereas for other links, Aand
σvalues are mainly described by mutual orientation α[11],
[12]. A comprehensive discussion of ST fading properties for
other B2B links is presented in [11], [12].
B. Realistic Mobility Modeling
The space-time variations of wireless links under unre-
stricted mobility are often not fully considered while develop-
ing path-loss models using measurement campaigns [8]. The
B2B channel model proposed in [11], [12] assumes restricted
mobility scenarios (close and far crossing and parallel walk-
ing) and can be enhanced to give more practical inter-BAN
performance by the integration of dynamic distances with
unrestricted mobility. This can be accomplished by exploiting
real-time body motion capture traces which include various
mobility scenarios (walking, running, sitting, exercising etc.)
[8]. This real-time motion capture data when combined with
geometrical transformation and analysis methods helps in ac-
tual performance evaluation of BANs and BBNs. The details of
the entire process for intra-BAN communication are illustrated
in [8], [10] but the major changes in algorithm to modify it
for B2B communication are mentioned as
•The determined body constructed by motion capture
traces is replicated in multiple human bodies for sim-
ulating dynamic inter-BAN links.
•The impact of body shadowing is mainly considered
in the distance and orientation-dependent channel gain
for the links between various relative node positions,
as discussed in sub-section II-A, so, it is not important
to characterize such links as LOS or NLOS. But wrist-
related channels can be line of sight (LOS) or non-line of
sight (NLOS) for the same body orientation because for
the same α, the two nodes may either be shadowed or not
by the torso [11], [12]. Therefore, geometrical analysis is

applied to ensure the accuracy of link types in inter-BAN
wrist-related channels. In this case, the intersection of the
link with single or multiple human body torso cylinders
declares the given B2B link as NLOS for the wrist-related
channels.
•Space-time varying inter-BAN links and mobility traces
are generated to give the appropriate dynamic distances
for the B2B channels mentioned in sub-section II-A.
The mutual orientation between two separate BANs is
taken the same throughout the mobility trace duration
since a coordinated movement scenario is simulated in
this paper and the variations in αare within a range of
10◦. Furthermore, the classification of dynamic link types
as LOS or NLOS is performed for B2B wrist-related
channels.
After obtaining the dynamic distances and link types for the
given inter-BAN scenario, the channel behavior is accurately
modeled with unrestricted mobility. The inter-BAN dynamic
distances and mutual orientation are used to obtain channel
gain values while ST fading parameters for different links are
a function of mutual orientation only. The channel gain, LT
fading and ST fading for wrist-related channels are computed
differently for LOS and NLOS link types so, the knowledge
of dynamic link types is important in this context. It should
be noted that mobility modeling provides higher and more
accurate space-time variations which help in estimating more
accurate path-loss results in comparison to the restricted
mobility based channel models [8], [10] for B2B channels.
Subsequently, the obtained path-loss values are utilized in
radio link modeling to calculate the SNR, BER and PER.
The entire system model with mobility modeling, B2B channel
modeling and radio link modeling is illustrated in Fig. 2.
C. Radio Link Modeling
The realistic mobility modeling of inter-BAN communica-
tion and the resultant space-time varying channels are followed
by the significance of accurate radio link modeling, which
Fig. 2: Mobility, channel and radio link modeling for B2B
communication.
includes SNR, BER and PER evaluation. The PER estimation
using threshold based method is not an accurate approach [8],
so an extensive approach and a practical method is presented
in this sub-section to calculate PER for B2B links. The SNR
between the two nodes iand jon two different BANs over
the time index tcan be written as
SN RdB
i,j,t =PdBm
T x +P LdB
i,j,t −PdBm
N,(4)
where PT x is the transmit power, PNis the noise power and
P LdB
i,j,t is the path-loss between iand jover the time t.
The exact formulation of the energy per bit to noise power
spectral density ratio Eb/N0and BER is done depending
upon the frequency and exact data rate at the physical layer.
According to IEEE 802.15.6 physical layer specifications,
differential binary phase shift keying (DBPSK) modulation is
used for low data rates and differential quadrature phase shift
keying (DQPSK) modulation is employed for high data rates
at 2.45 GHz frequency [1]. The value of Eb/N0in dB, based
on the current SN RdB
i,j,t, bandwidth BW in Hz and data rate
Rin bps can be written as
Eb/N0[dB] = SN RdB
i,j,t + 10log10 BW
R,(5)
Since Rice type ST fading is assumed in the channel model,
therefore the corresponding DBPSK BER for low data rate
between the inter-BAN links iand jover the time tcan be
calculated as
BERDBP S K
i,j,t =K+ 1
2(1 + K+ Γ)exp −KΓ
1 + K+ Γ.(6)
where Γis the average SNR given as Γ = E{z2
ST }Eb
N0[13].
The DQPSK BER expression for high data rate is derived
using the Rice density equation as a function of instanta-
neous SNR γband the DQPSK additive white Gaussian noise
(AWGN) error equation which are respectively written as
p(γb) = K+ 1
Γexp −γb(K+ 1) + KΓ
Γ
I0 r4(K+ 1)Kγb
Γ!,(7)
Pe(γb) = Qp1.112γb.(8)
Substituting p(γb)and Pe(γb)into the average error prob-
ability expression Pe=R∞
0Pe(γb)p(γb)d(γb)[13] and in-
tegrating using the Chernoff bound for Gaussian Q-function
Q(γb)≤1
2exp−
γ2
b
2[14], the upper bound on the respective
DQPSK BER for high data rate between the inter-BAN links
iand jover the time tcan be described as
BERDQP SK
i,j,t ≤Γ
2(1 + K+ 0.556Γ) K+ 1
Γ
exp −K+K(K+ 1)
1 + K+ 0.556Γ.(9)

The smartBAN standard defines the usage of Gaussian min-
imum shift keying (GMSK) with the bandwidth-bit period
product (BT )of 0.5 and modulation index (h)of 0.5 as the
key modulation technique at the physical layer [15]. The upper
bound on GMSK BER under Rice fading is acquired using the
procedure discussed above and taking Pe(γb)as
Pe(γb) = Qp2γb,(10)
where is the GMSK constant and for BT of 0.5 is equal
to 0.79 [16]. The upper bound on the corresponding GMSK
BER between the given B2B links iand jover the time tis
therefore mentioned as
BERGM SK
i,j,t ≤Γ
2(1 + K+ 0.79Γ) K+ 1
Γ
exp −K+K(K+ 1)
1 + K+ 0.79Γ.(11)
Consequently, the PER is computed based on the packet
length Nin bits and the adequate BERi,j,t expression as
P ERi,j,t = 1 −(1 −BERi,i,t )N.(12)
Finally, the obtained PER values which are based on the
dynamic space-time dependent channel measurements and the
accurate radio link modeling, are given to the high level
packet-oriented simulation environment for the MAC layer
performance evaluation.
III. PHY/MAC LAYER PAR AMETERS
In this work, the joint PHY-MAC layer performance evalua-
tion in terms of both IEEE 802.15.6 and smartBAN standards
is performed. Therefore, this section highlights the physical
and the MAC layer specifications of IEEE 802.15.6 and
smartBAN used in the PRR simulations.
A. IEEE 802.15.6 PHY/MAC Layer
We consider time division multiple access (TDMA)-based
scheduled access mechanism with beacon-enabled superframe
format [1] since the priority is the investigation of the impact
of accurate channel modeling on MAC layer performance.
The variable-length MAC frame body is appended with MAC
frame header and frame check sequence (FCS) to form phys-
ical layer service data unit (PSDU), which is spread using the
spreading factor determined by the data rate. The resulting
PSDU is added with physical layer convergence protocol
(PLCP) preamble for timing synchronization, channel offset
recovery and packet detection and with PLCP header for con-
veying information about the physical and MAC parameters
required at the receiver side. The PLCP header spreading is
additionally done and the combination of PLCP preamble,
PLCP header and PSDU forms a physical layer protocol
data unit (PPDU) which represents the information transmit-
ted through the propagation medium [17]. Guard duration
is used to separate PPDUs sent by different BBN nodes
in different time slots. The additional information on guard
duration formulation using the synchronization interval, inter-
frame spacing and turnaround time, as well as the maximum
packet transmission duration and packet size calculations can
be found in [1], [17].
B. SmartBAN PHY/MAC Layer
Again TDMA-based scheduled access method is used in
this context and each time slot comprises of data frame trans-
mission and ACK frame transmission periods separated by
inter-frame spacing. Each BBN node transmits its data in data
frame transmission period while the receiving node shall send
an ACK frame (successful transmission) or a NACK frame
(unsuccessful transmission) in the ACK frame transmission
time which is ended with inter-frame spacing at the end of
the slot [18].
On the MAC layer, a 56 bit MAC header and 16 bit frame
parity are added to the MAC frame body to generate MAC
protocol data unit (MPDU). Since we assume uncoded data
transmissions for both IEEE 802.15.6 and smartBAN, MPDU
will be the same as PSDU. The PSDU is further appended
with 16 bit PLCP preamble and 40 bit PLCP header fields
to create a PPDU structure [15]. A complete discussion on
the smartBAN physical and MAC layer specifications and
parameters can be explored in [15], [18].
IV. JOI NT P HY-MAC PERFORMANCE RE SU LTS
This sections presents a thorough analysis of the results
obtained using the system model and PHY/MAC layer pa-
rameters discussed in Section II and III respectively.
A. Simulation Setup
We assume three different BANs with one of them being the
leader (BAN1) and the rest two being the followers (BAN2 and
BAN3), receiving information from their leader for the coor-
dinated movements over a dedicated frequency channel. Note
that a separate frequency channel is used for on-body com-
munication within each BAN and here different coordinator
node positions for inter-BAN communication are investigated.
The mobility scenarios considered in the simulations include
walking, running, sitting and standing and therefore, represent
the primary movements made in the mission critical operations
and precise monitoring during sports activities. The node
positions for all the BANs examined in simulations consist
of head (H), belt (B) and right wrist (W). Each coordinator
node on the leader BAN sends its information to all the other
coordinator nodes placed on the follower BANs in its assigned
time slot with an objective to identify the best coordinator
Fig. 3: TDMA for B2B communication.

location, as shown in Fig. 3. For IEEE 802.15.6 standard,
MAC payload sizes of 16, 128 and 256 bytes as well as both
low data rate (LDR, 121.4 kbps) and high data rate (HDR,
971.4 kbps) are considered. Whereas smartBAN assumes a
data rate of 1000 kbps for all payload sizes [15] and with no
data transmission repetition, MAC payload sizes of 16, 128
and 250 bytes are taken.
B. Simulation Results
The main purpose of this work is the investigation of
the suitable coordinator nodes positions in inter-BAN com-
munication using PRR as the performance criteria. For this
purpose, statistical results including mean, standard deviation
and correlation coefficient of path-losses corresponding to
different transmitter-receiver location combinations are listed
in Table I. The results are demonstrated for the running
scenario since it involves the highest level of mobility. It can
be seen that for every transmitter node location, the mean
path-loss values are the minimum when the receiver node is
placed on head. Moreover, the positioning of the transmitter
node on head also results in the reduction of mean path-
loss values as compared to the other coordinator positions.
Furthermore, the high correlation coefficient values indicate
TABLE I: Statistical Analysis of the Channel Model with
Mobility Modeling (Running Scenario)
Link Type Mean Standard
Deviation
Correlation
Coefficient
Belt-to-Belt 59.25 3.18 0.23
Belt-to-Head 54.50 2.94 0.17
Belt-to-Wrist 71.36 3.97 0.20
Head-to-Belt 54.35 2.90 0.14
Head-to-Head 42.15 2.72 0.17
Head-to-Wrist 66.41 17.09 0.67
Wrist-to-Belt 74.65 3.91 0.20
Wrist-to-Head 63.90 13.92 0.55
Wrist-to-Wrist 60.83 3.37 0.25
that the unrestricted mobility-based path-loss model keeps the
track of high mobility and temporal variations for B2B links
as well, in the same manner as indicated in [8] for on-body
links. It is also noticeable that the statistical values are not very
different when the transmitter and the receiver node positions
are interchanged. Using these observations as the basis, the
MAC level performance results are further narrowed down to
the links which include head as the receiver node position since
these links assume comparatively lesser mean path-losses.
Fig. 4, Fig. 5 and Fig. 6 summarize the PRR results of
belt-to-head, head-to-head and wrist-to-head links respectively
for different transmission power levels using IEEE 802.15.6
specifications. It is quite obvious that head-to-head links
outperform all the other links while wrist-to-head links give the
worst performance. The PRR values degrade for all link types
if the packet size and the data rate are increased. For belt-to-
head links, the acceptable performance of equal or above 90
percent PRR is achieved only when 16 byte payload is sent
with LDR at any given transmission power level. 128 byte
payload also gives adequate performance when transmitted at
higher power levels with LDR. But for head-to-head links, the
PRR performance is considerably improved in comparison to
the belt-to-head links. For LDR, the transmission of different
payload sizes is permissible even at the lower transmission
power levels. High transmission power and small payload size
should be used when data is transmitted at the high rate. For
wrist-to-head links, a PRR above 90 percent is achieved only
at the higher transmission power levels with lower payload
sizes and data rate.
Finally, the PRR performance evaluation of smartBAN is
shown in Fig. 7 for the above mentioned link types. Head-
to-head links again give the best results among all the link
types with smartBAN specifications as well. Data can be sent
with all payload sizes at almost all transmission power levels
over head-to-head links. For belt-to-head links, payload of 16
bytes can be transmitted at the transmission power level of
above -5dB while the payload size of 128 bytes requires higher
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0
Transmitter Power (dBm)
0
10
20
30
40
50
60
70
80
90
100
PRR (%)
Belt-to-Head Links
LDR, 16 Bytes
LDR, 128 Bytes
LDR, 256 Bytes
HDR, 16 Bytes
HDR, 128 Bytes
HDR, 256 Bytes
Fig. 4: PRR versus transmission power level results for belt-to-
head links in BBN, N= 16, 128 and 256 bytes, LDR (121.4
kbps) and HDR (971.4 kbps).
-10 -7.5 -5 -2.5 0
Transmitter Power (dBm)
0
10
20
30
40
50
60
70
80
90
100
PRR (%)
Head-to-Head Links
LDR, 16 Bytes
LDR, 128 Bytes
LDR, 256 Bytes
HDR, 16 Bytes
HDR, 128 Bytes
HDR, 256 Bytes
Fig. 5: PRR versus transmission power level results for head-
to-head links in BBN, N= 16, 128 and 256 bytes, LDR (121.4
kbps) and HDR (971.4 kbps).

-10 -7.5 -5 -2.5 0
Transmitter Power (dBm)
0
10
20
30
40
50
60
70
80
90
PRR (%)
Wrist-to-Head Links
LDR, 16 Bytes
LDR, 128 Bytes
LDR, 256 Bytes
HDR, 16 Bytes
HDR, 128 Bytes
HDR, 256 Bytes
Fig. 6: PRR versus transmission power level results for wrist-
to-head links in BBN, N= 16, 128 and 256 bytes, LDR (121.4
kbps) and HDR (971.4 kbps).
-10 -7.5 -5 -2.5 0
Transmitter Power (dBm)
0
10
20
30
40
50
60
70
80
90
100
PRR (%)
SmartBAN PRR
B2H, 16 Bytes
B2H, 128 Bytes
B2H, 250 Bytes
H2H, 16 Bytes
H2H, 128 Bytes
H2H, 250 Bytes
W2H, 16 Bytes
W2H, 128 Bytes
W2H, 250 Bytes
Fig. 7: PRR versus transmission power level results for smart-
BAN, N= 16, 128 and 250 bytes, belt-to-head (B2H), head-
to-head (H2H) and wrist-to-head (W2H) links.
transmission power levels. Finally, wrist-to-head links do not
contribute to any transmission with acceptable performance
for any payload size or transmission power and might require
encoded or repetitive transmissions.
V. CONCLUSION
Recently developed channel models through measurement
campaigns are integrated into realistic mobility and radio link
modeling and the joint PHY-MAC performance evaluation
of B2B communication for IEEE 802.15.6 and smartBAN
standards specifications is performed. The usage of mobility
modeling facilitates more accurate performance analysis of
time-varying inter-BAN links. The presented results indicate
that the placement of coordinators on the head significantly
reduces the required transmission power levels for inter-BAN
communication, even at high data rate and payload sizes.
ACKNOWLEDGMENT
This research was supported by the Estonian Research
Council through the Institutional Research Project IUT19-11,
and by the Horizon 2020 ERA-chair Grant Cognitive Electron-
ics COEL H2020-WIDESPREAD-2014-2 (Agreement num-
ber: 668995; project TTU code VFP15051).
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