This article has been accepted for publication in IEEE Sensors Journal. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2022.3218136
IEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2022
1
FCLR:Fuzzy Control-based Layering Routing
Protocol for Underwater Acoustic Networks
Duoliang Han, Xiujuan Du, Xiuxiu Liu, Xiaojing Tian
Abstract— Because of its wide application in the underwater environment, underwater acoustic networks (UANs) have received
high attention. However, the intrinsic attributions of low bandwidth,
long propagation delay, high bit error rate, and restricted energy
bring about great challenges to data transmission in UANs. In this
paper, we propose a routing protocol for UANs called fuzzy controlbased layering routing protocol (FCLR). With the FCLR protocol, an
underwater node may learn or update its layer and the information
of its neighbors from packets overhead before selecting the best
forwarding node. A fuzzy control system is used to select the
best forwarding node based on input variables including residual
energy and node density of neighbor nodes. Extensive experiments
are conducted by the NS3 network simulator to evaluate the performance of the FCLR protocol. The results show that the FCLR
protocol has superior performance in terms of packet delivery rate,
end to end delay, and total energy consumption. Furthermore, the
FCLR protocol is tested in Qinghai Lake, the largest saltwater lake in China, and its performance was evaluated in terms
of packet delivery rate, throughput, and end-to-end delay. When nodes are moving, the packet delivery rate can reach 92.6
percent and the throughput can reach 350.64bps.
Index Terms— UANs, Fuzzy Control, FCLR, Routing protocol, NS3, Performance evaluation
I. I NTRODUCTION
NDERWATER acoustic networks (UANs) have become
a hot issue for researchers in the last decade or so
due to their widespread applications in disaster detection,
pollution detection, and military applications [1]–[4]. UANs
are essentially ad-hoc networks, which are made up of surface
base stations and underwater sensor nodes. In general, sensor
nodes deployed in water are used to collect and transmit data
[5], [6]. One or more sink nodes are deployed on the surface to
receive data from sensor nodes in the water, and the sink nodes
are also responsible for transmitting data to a data center [7]–
[10]. The architecture of UANs appears simple, yet it faces
substantial challenges. Because of the serious scattering of
light signals and heavy attenuation of radio frenquency signal
in water, neither light nor radio frequency signal is applied
to UANs, UANs adopt acoustic waves to deliver data [11]–
[13]. Meanwhile, UANs face the following challenges: (1)
U
This work was supported in part by the Innovation Team Foundation
of Qinghai Office of Science and Technology under Grant No.2020-ZJ903, in part by the National Natural Science Foundation of China under
Grant No.61962052. (Corresponding author:Xiujuan Du.)
Duoliang Han and Xiaojing Tian are now with Department of
Computer, Qinghai Normal University, Xining 810008, China.(e-mail:
2945219148 @qq.com, 1984159821@qq.com)
Xiujuan Du and Xiuxiu Liu are now with Department of Computer,
Qinghai Normal University, Qinghai Provincial Key Laboratory of IoT,
The State Key Laboratory of Tibetan Intelligent Information Processing
and Application, and Academy of Plateau Science and Sustainability, Xining 810008, China.(e-mail:dxj@qhnu.edu.cn, 1154894860@qq.com).
the sensor nodes are powered by batteries, and it is hard to
recharge and change the battery, thus the energy is limited;
(2) the bandwidth of underwater channels is narrow; (3) the
speed of the acoustic signal is slow, resulting in a long
propagation delay; (4) the quality of the underwater acoustic
channel is poor. Therefore, it is very challenging to design
a routing protocol with excellent performance [14]–[19]. An
effective routing protocol performs well in terms of energy
efficiency, end-to-end delay (EED), and packet delivery rate
(PDR). Therefore, it is essential to develop effective routing
protocols to confront the challenges faced by UANs and
improve UANs communication quality [20]. At present, some
routing protocols designed for underwater acoustic networks
assign weights to different parameters of candidate nodes, and
the weights remain unchanged once assigned. However, with
the forwarding of packets, different discriminant parameters
of a candidate node interact and influence each other, and the
interaction and influence are difficult to be described by given
formula. So fuzzy control is introduced in this paper to deal
with the uncertain interaction among different discriminant
parameters of candidate nodes. Fuzzy control has been widely
employed in terrestrial wireless sensor networks due to its
ability to deal with uncertainties and optimize parameters in
recent years [21], [22]. So to optimize the routing decision for
UANs, a fuzzy control-based layering routing (FCLR) protocol
is proposed. FCLR implements fuzzy routing decisions with
two parameter inputs to improve the network performance.
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IEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2022
Before finding the best forwarding node(BFN) in routing
deceison, an underwater node may learn or update its layer
and the information of neighbors nodes from packets overhead,
then an fuzzy control system(FCS) is used to select the best
forwarding node based on input variables including residual
energy and node density of neighbor nodes.
With the FCLR protocol, each sensor node maintains a
neighbor information table(NIT) based on the information
from control or data packets overhead. The sink node broadcasts control packets periodically, which is updated and forwarded by the receiving nodes with higher layer. We employ
the FCS to select the BFN, besides the layer, the residual
energy ratio, and node density are also used as inputs of FCS.
After the BFN is determined, data packets are forwarded to the
BFN by the source node or the current forwarding node(CFN).
The major contributions of this paper are as follows:
(1) To deal with the uncertain interaction among different
discriminant parameters of candidate nodes, a fuzzy control
system is introduced into the routing decision of UANs in
this paper, and a fuzzy control-based FCLR routing protocol
is proposed. In addition, to solve the problem of void area,
only those neighbor nodes with lower layers are eligible to be
candidate forwarding nodes in our proposed routing protocol.
Furthermore, considering the balance of node energy, residual
energy and node density are used as the measure of candidate
nodes.
(2) The proposed protocol is realized by programming in
the NS3 platform and Raspberry PI embedded development
board. Extensive simulation experiments and field tests in
Qinghai lake which is the largest salt lake in China are
conducted to evaluate the performance of the FCLR protocol.
The results verify the superior performance of the proposed
FCLR protocol in terms of PDR, EED, and total energy
consumption (TEC).
(3) The algorithms in the literature review need location
information or depth information in routing decisions, while
the FCLR protocol does not need any location or depth
information. Therefore, the FCLR protocol reduces overhead
and saves costs.
The rest of this paper is organized as follows: the related
work of the routing protocol for UANs is discussed in section
II. The network model is described in Section III. Section IV
presents the FCLR protocol and describes the specific algorithm. In section V, we conduct comprehensive simulations and
analyze the results for the proposed protocol. In Section VI,
we evaluate the performance of the FCLR protocol in field
tests. In section VII, we summarize the paper and propose
future research.
II. R ELATED WORKS
So far researchers have proposed and designed some routing
protocols for UANs, among which some are more energysaving, and some have better delivery rates. Next, we will
review some of the protocols in this section.
The earlier protocols proposed for UANs were the vectorbased forwarding (VBF) and depth-based routing (DBR) protocols, and both of them are classic in UANs [23], [24]. The
VBF protocol depends on location information to work. In
the VBF protocol, a virtual routing pipe from the source node
to the destination node is created based on the locations of
the communication node-pair, and only the sensor nodes in
the pipe have the opportunity to forward data packets. Nodes
outside the pipeline will discard the packets after they receive
the packets. Therefore, the VBF protocol outperforms simple
flooding protocol in terms of energy-consuming. However,
multiple nodes in the pipe involved in forwarding in one
hop result in additional energy consumption. Furthermore, the
nodes outside the pipe in the VBF protocol are unqualified
to forward data packets, which may cause the void area
problem in sparsely deployed UANs. The DBR protocol takes
advantage of a greedy forwarding algorithm. Each node in
the DBR protocol is equipped with a depth sensor, which
is used to get the depth information of the node. One data
packet is more likely to be forwarded by the node closest
to the water surface (i.e.,the node with a smaller depth than
the current forwarding node). In contrast to VBF, the DBR
protocol does not require full position information of nodes
and consumes less energy, but it may also cause the void area
problem in sparsly deployed UANs. In [25], Coutinho R. W. et
al. proposed a geographic and opportunistic routing (GEDAR)
protocol with depth adjustment for mobile UANs. To transmit
a data packet, the GEDAR protocol employs the location
information of underwater nodes and surface sonobuoys. The
GEDAR protocol develops a depth adjustment approach to
address the problem of communication void area, which has
a significant impact on the performance of geographic and
opportunistic routing protocols.
Li Z et al. proposed a forwarding protocol based on relative
distance(RDBF) in [26]. The goal of RDBF is to achieve
energy-saving, efficient transmission and low latency routing.
The RDBF protocol uses a fitness factor to determine the suitability of a node to forward data packets, and there are only a
few nodes forwarding data packets, so the energy consumption
is reduced. However, because forwarding nodes are selected
based on their closeness, there will be an imbalance in energy
consumption, resulting in the energy hole problem.
M. A. Rahman et al. proposed an energy-efficient cooperative opportunistic routing (EE-COR) protocol for UANs
in [27]. EECOR selects forwarding nodes based on energy
consumption and packet delivery rate and uses a holding
timer to schedule data packet transmission, which improves
the packet delivery rate, but the routing hole problem has not
been addressed.
Anwar et al. presented a routing protocol for UANs called
localization-free interference and energy holes minimization
(LF-IEHM) [28]. LF-IEHM solves the energy hole problem
without the need for location information of nodes. In LFIEHM, the sensor nodes are capable of changing their transmission range during communication, so LF-IEHM has better
throughput. However, it consumes more energy during packet
delivery. In [29], Khan et al. proposed a cooperative energy
efficient optimal relay selection protocol(Co-eeors). When
selecting the forwarding node, Co-eeors take into account
the information of the node such as depth and location.
Co-eeors improves packet delivery rate compared with other
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content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2022.3218136
DUOLIANG HAN et al.: FCLR: FUZZY CONTROL-BASED LAYERING ROUTING PROTOCOL FOR UNDERWATER ACOUSTIC NETWORKS (XXXX 2022)
3
TABLE I
T HE P ERFORMANCE C OMPARISON O F T HE A BOVE P ROTOCOLS
Protocol
Year
Control Message
Requirements
VBF
2006
No
Location Information
Single entity
Improving robust and energy efficiency
DBR
2008
Yes
Depth Information
Single entity
Handling dynamic networks with good energy efficiency
GEDAR
2014
Yes
Depth Information
Single entity
Addressing the problem of communication
void area
RDBF
2014
Yes
Location Information
Single entity
Providing transmission efficient, energysaving, and low delay routing
EECOR
2017
Yes
Depth Information
Single entity
Improving energy efficiency
LF-IEHM
2018
Yes
Pressure Information
Single entity
Overcoming interference during data packet forwarding and mitigating energy hole
Co-EEORS
2018
Yes
Depth Information
Single entity
Ensuring reliable delivery of packets
Co-EEERD
2019
Yes
Location Information
Single entity
Improving energy efficiency and network
reliability
EDORQ
2020
No
Depth Information
Single entity
Guaranteeing the energy-saving and reliable data transmission
RLOR
2021
Yes
Depth Information
Single entity
Improving the reliability and energy efficiency, avoid void area
ELW-CFR
2021
Yes
Layer Information
Single entity
Improving energy efficiency and address
void hole problem
protocols. However, the need for depth information increased
the hardware complexity of nodes, and the acquisition of
accurate distance is another challenging issue. When nodes
are deployed sparsely and the distance between neighbor
nodes is long, the performance of the Co-eeors protocol is
poor. Ullah and Khan et al. proposed two energy-efficient
and reliable delivery routing protocols, EERD and CO-EERD
[30]. EERD is a single-path routing protocol, with which a
packet is delivered to the sink only along the best path. EERD
employs a weight function to select the best forwarding node
and a reliable routing path. However, the unpredictability of
subsea links decreases the reliability of data transmission.
Thus, the CO-EERD protocol introduces cooperative routing
into EERD, which improves the delivery rate and the reliability
of transmission. In [31], Yongjie Lu et al. proposed an energyefficient depth-based opportunistic routing algorithm with Qlearning (EDORQ) for UANs. Through Q-learning and void
detection, EDORQ improves the network performance in
terms of packet delivery ratio, average network overhead and
energy consumption. However, to get the depth information,
each node in EDORQ needs to be equipped with a depth
sensor, which increases the hardware cost. Ying Zhang et.al
proposed a reinforcement learning-based opportunistic routing
protocol (RLOR) in [32]. The RLOR is a kind of distributed
routing approach which employs a recovery mechanism to
solve the routing problem of void areas. RLOR improves the
delivery rate of data packets in sparse UANs while bringing
about additional energy consumption and delay. Umar Draz
proposed an energy-efficient proactive routing scheme (ELWCFR) for enabling reliable communication in the underwater
internet of things [33]. In the ELW-CFR protocol, nodes
are deployed layer-by-layer to avoid the void area problem.
Cluster or Single Entity Main goal
However, node movement leads to a rapid drop in the delivery
rate of the ELW-CFR protocol. Table I lists the performance
comparison of the above protocols.
III. N ETWORK M ODEL
The application environment of UANs is underwater, such
as in oceans and lakes. Underwater acoustic communication
faces many problems, such as noise interference, the Doppler
effect and the propagation loss of acoustic signals in water,
etc., which make the design and implementation of routing
protocols for UANs extremely difficult. The signal loss and
noise models of UANs are presented in this section.
A. Path Loss Model
Acoustic signal can be absorbed and reflected by water,
which causes the signal a certain amount of attenuation and
loss in the water. The loss of the sound signal in water
mainly includes expansion loss and absorption loss [34]. The
transmission path loss(TPL) is given by:
T P L = k ∗ 10 ∗ log (d) + d ∗ a(f )
(1)
where k is the spread coefficient, d is the distance between
two nodes and f is the carrier frequency, a(f ) is the energy
absorption coefficient which is given by:
a(f ) =
0.11 ∗ f 2
44 ∗ f 2
+
+ 2.75 ∗ 0.0001 + 0.003 (2)
1 + f2
4100 + f 2
B. Channel Noise Model
Underwater acoustic communication is greatly influenced by
noise. Turbulence, shipping, waves, and thermal noise are the
four major types of noise in the water, and they are represented
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IEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2022
TABLE II
A BBREVIATIONS A ND S YMBOLS U SED I N T HIS PAPER
Data center
Sink node
Abbreviations/Symbols
Description
UANs
Underwater acoustic networks
FCLR
Fuzzy control-based layering routing
FCS
Fuzzy control system
Forwarding node
NIT
Neighbor information table
Transmission range
BFN
Best forwarding node
Acoustic link
CFN
Current forwarding node
Source node
QFN
Qualified forwarding node
RER
Residual energy ratio
QND
Qualified node density
FP
Forwarding probability
Lcur
Layer of current forwarding node
Lsend
Layer of sending node
Er
Residual energy of qualified forwarding node
Ei
Initial energy of qualified forwarding node
NLq −1
Number of upper layer node of QFN
Nall
Number of all neighbor nodes of QFN
Neighbor ID
Layer
Residual energy
Node density
PDR
Packet delivery rate
3
0
Er 1
ND1
TEC
Total energy consumption
28
1
Er 2
ND2
TP
Throughput of the network
94
2
Er 3
ND3
EED
End to end delay
188
3
Er 4
ND4
...
...
...
...
Radio link
Sink node
Source node
Fig. 1. UANs structure.
TABLE III
T HE S TRUCTURE OF NIT
by Ntu (f ), Nsh (f ), Nwa (f ), and Nth (f ) respectively [5]. The
power spectral density of environmental noise N is given by:
N = Ntu (f ) + Nsh (f ) + Nwa (f ) + Nth (f )
(3)
where Ntu (f ), Nsh (f ), Nwa (f ), Nth (f ) are given by (4) ,(5),
(6), (7) respectively:
10 log Ntu (f ) = 17 − 30 log(f )
(4)
10logNsh (f ) = 40 + 20(s − 0.5)
+26log(f ) − 60log(f + 0.03)
(5)
√
10 log Nwa (f ) = 50 + 7.5 w
+20 log(f ) − 40 log(f + 0.4)
(6)
10logN th(f ) = −15 + 20log(f )
(7)
where f is the frequency in kHz, w is the speed of wind in
m/s and s is the movement factor of shipping.
IV. P ROPOSED P ROTOCOL
The FCLR protocol employs an FCS to select the BFN,
with the residual energy ratio and node density as inputs of
the FCS. The FCLR protocol is described in detail in this
section. The abbreviations and symbols used in this paper are
listed in Table II.
A. Network Structure
We considered the network structure in Fig.1 for the FCLR
protocol. A sink node is deployed on the surface of the water
and is responsible for collecting data from the source node and
forwarding data to the data center. A source node is deployed
at the bottom of the water to collect information. Many sensor
nodes are deployed in the water and are responsible for
forwarding data collected by the source node to the sink node.
The data packets are transmitted to the sink node hop by hop.
When a data packet is received by the sink node successfully,
the transmission is completed.
B. Node Layer and Neighbor Information Table
In the FCLR protocol, each node maintains dynamically a
layer, which is used to indicate the number of hops from the
node to the sink node. The layer of the sink node is set to 0.
The layer of other nodes is initialized to 0xFF. Moreover, each
node also maintains an NIT to record the IDs, layers, residual
energy, and qualified node density (QND) of neighbor nodes
in real-time. The structure of NIT is shown in Table III. The
layering algorithm is shown in Algorithm 1.
To facilitate the nodes in UANs to obtain or update their
layers, the sink node broadcasts HELLO packets periodically.
In a HELLO packet, the ID, layer, residual energy and other
information of the sending node are included. The format of a
HELLO packet is shown in Table IV, in which PHN denotes
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content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2022.3218136
DUOLIANG HAN et al.: FCLR: FUZZY CONTROL-BASED LAYERING ROUTING PROTOCOL FOR UNDERWATER ACOUSTIC NETWORKS (XXXX 2022)
TABLE IV
T HE FORMAT OF THE HELLO PACKET
Bits
8
16
16
2
... 16 16 ...
Packet type
0:DATA
Fields
Layer S ID R ID
... Er ND ...
1:ACK
2:HELLO
PHN
NHN
Position
Head
Load
the previous hop node and NHN denotes the next hop node.
S ID (R ID) is the ID of the sending node (receiving node).
The HELLO packets are updated and forwarded by the
receiving nodes with a higher layer. After receiving a HELLO
packet, the receiving node extracts the ID, layer, residual
energy and node density from the HELLO packet, and stores
them into its NIT, then the node determines whether to
update and forward the HELLO packet. To avoid collision and
overmuch energy consumption, only the nodes whose layers
are higher than that of the sender update and forward the
HELLO packet. The fields of layer, sender ID, residual energy,
and node density in the HELLO packet are updated hop by
hop.
The periodic broadcasting of HELLO packets and the timely
updating of NIT help the CFN to select the BFN from its newly
updated neighbor table.
We employ the same layer updating mechanism in the FCLR
protocol as in reference [35]. Algorithm 1 provides the layer
updating procedure. As in Algorithm 1, Lcur represents the
layer of the current node, Lsink denotes the layer of the
sink node, and Lsend indicates the layer of the previous hop
node(sending node). We set an aging timer for the layer of
each node, and a node starts its aging timer when its layer
is updated. The aging timer is introduced for the following
purposes: (1) If a node receives a HELLO packet before the
aging timer expires and the value of the layer field in the
packet (Lsend ) is no less than the layer of the receiving node
(Lcur ), the layer of the receiving node and its aging time
remain unchanged; (2) If a node receives a HELLO packet
before the aging timer expires and the value of layer field
in the packet (Lsend ) is less than Lcur − 1 (the layer of the
receiving node minus 1), the layer of the receiving node is
changed to (Lsend +1) and its aging timer restarts;(3) If a node
receives a HELLO packet after its aging timer has expired, it
updates its layer to Lsend + 1 and restarts the aging timer.
After a node obtains or updates its layer according to the
HELLO or data packets received, it can forward the HELLO
or data packets. With the proposed FCLR protocol, HELLO
packets are transmitted always along the paths from nodes
with low-layer to nodes with high-layer; and DATA packets
are transmitted along the paths from nodes with high-layer to
nodes with low-layer till to the sink node with layer 0, in this
way the transmission efficiency and reliability are improved.
C. BFN SELECTION
To reduce collision, improve energy efficiency, and data
PDR, the FCLR protocol implements single path routing.
In other words, the BFN needs to be selected at each hop
5
Algorithm 1 Layer update procedure
1:
2:
3:
4:
5:
6:
7:
8:
9:
10:
11:
12:
13:
14:
15:
Network initialization completed;
Lcur = 255;
Lsink = 0;
when a node N receives a HELLO packet from node M;
if Lcur ==255 then
Lcur = Lsend + 1 //Lsend is the layer of the sending node;
Node N updates the HELLO packet and continues to forward;
else
if agingtimer > 0 and Lcur < Lsend + 1 then
Lcur remain unchanged, discard the HELLO packet;
else
Lcur = Lsend + 1 ;
update HELLO packet and forward;
end if
end if
before forwarding data packets. When a non-BFN node hears
a data packet, it just updates its layer and NIT according to
the information of the header fields from the packet without
forwarding the packet.
To balance the energy consumption among nodes and prolong the lifetime of the whole network, when determining
the BFN, the residual energy and node density of candidate
nodes are taken into account, and two weight coefficients are
introduced for the two routing discriminant parameters of the
candidate node. However, with the forwarding of packets, the
two discriminant parameters of a candidate node interact and
influence each other, and the interaction and influence are
difficult to be described by given formula. So fuzzy control is
introduced in this paper to deal with the uncertain interaction
among different discriminant parameters of candidate nodes.
FCS is an intelligent control system that uses fuzzy set
theory, fuzzy language variables, and fuzzy logic reasoning
for decision-making, so it is an intelligent decision-making
system. FCS can imitate human inference and decision-making
process and is widely used in various fields. Since FCS has
many advantages such as low computational complexity and
strong adaptability, to optimize the routing decision for UANs,
an FCS-based layering routing (FCLR) protocol is proposed.
In FCS, the structure of dual input and single output is the
most common. In UANs, if a node has more residual energy
and more upper layer neighbor nodes, it is more suitable to
be the next hop forwarding node. So considering the energy
limitation and the imbalance of energy consumption of nodes
in UANs, the residual energy rate (RER) and QND of qualified
forwarding node (QFN) are taken as the input variables, and
the forwarding probability (FP) of QFN is taken as the output
variable of FCS in the proposed FCLR protocol.
The core of an FCS is a fuzzy controller, also known as
an fuzzy language controller. The fuzzy controller is mainly
composed of the fuzzy interface, fuzzy rule base, inference
and defuzzy-interface.
The fuzzy control takes five steps as follows:
Step 1: Determine the input and output variables;
Step 2: Define the linguistic values of the input and output;
Step 3: Determine the membership functions of input and
output variables;
Step 4: Establish the rules of fuzzy control;
Step 5: Output the result after rule matching, rule triggering,
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IEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2022
TABLE V
L INGUISTIC VALUES FOR RER, QND A ND FP
Fuzzification
RER
Fuzzy inference
Defuzzification
FP
QND
Fuzzy rule
Input/output variables
Membership
RER
Low
Medium
High
QND
Low
Medium
High
FP
Low
Medium
High
Fig. 2. The flow chart of BFN selection.
a
l
m
c
0HPEHUVKLSGHJUHH
n
y
z
o
b
x
/RZ
0HGLXP
+LJK
5(5
Fig. 3. The network topology.
Fig. 4. Membership functions of RER.
defuzzification, etc..
Before selecting the BFN by the FCS, the node first finds
out all the neighbor nodes with layers less than Lcur as the
qualified forwarding node (QFN). Next, the residual energy
rate (RER) of QFN and qualified node density are input into
the FCS. The flow chart of BFN selection is shown in Fig.2.
RER: the rate of the residual energy of a QFN to its initial
energy, it is given by:
node n. Thus, the QNDs of node y and node z are 1/2 and
3/5 respectively according to Eq.9. The RER and QND of
the QFN node (node y, and node z successively) are used as
input variables of the FCS, and the output value of FCS is the
probability that the QFN node is selected as the BFN, which
is defined as forwarding probability (FP). The larger the FP,
the more probably the QFN node is selected as the BFN.
In FCS, three triangle membership functions are used for
fuzzification and three linguistic values for RER, QND and
FP are specified as shown in Table V.
The membership functions of Low, Medium and High are
fL , fM and fH , respectively, which are given by equation (10)
- (12) [36].

x60
 0,
0.5−x
,
0
6
x 6 0.5
fL (x) =
(10)
 0.5
0,
x > 0.5

0,
x60


 x
, 0 6 x 6 0.5
0.5
fM (x) =
(11)
, 0.5 6 x 6 1
 1−x

 0.5
0,
x>1

x 6 0.5
 0,
x−0.5
,
0.5
6x61
fH (x) =
(12)
 0.5
0,
x>1
RER =
Er
, RER ∈ (0, 1)
Ei
(8)
where Ei and Er are the initial energy and residual energy of
the QFN respectively.
QND: the rate of the number of neighbors whose layer is
smaller than that of QFN to the number of neighbors of QFN,
it is given by:
QN D =
NLq −1
, QN D ∈ (0, 1)
Naq
(9)
where NLq −1 is the number of neighbor nodes whose layer
is 1 smaller than that of QFN and Naq is the number of all
neighbor nodes of the QFN. Lq is the layer of QFN.
The network topology is shown in Fig.3. In Fig.3, the node
x is the current node, Ni ={y, z} composes the set of QFN,
that is, Ni is the set of neighbor nodes in the upper layer of
CFN x. The nodes in Ni are nodes with the layer Lcur − 1.
Nj ={a, b, c, x} is the set of neighbor nodes of QFN y and
Nk ={l, m, n, o, x} is the set of neighbor nodes of QFN z.
As shown in Fig.3, the neighbor nodes of the QFN y with
a smaller layer are node a and node c, the neighbor nodes
of the QFN z with an smaller layer are node l, node m, and
We plotted the membership function curves of RER, QND
and FP as Fig.4, Fig.5 and Fig.6 respectively.
Fuzzy rules adopt if-then rules in FCS. For example, if RER
is Medium and QND is High, then FP is High. Consequently,
the total fuzzy rules employed by the control engine are shown
in Table VI.
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0HPEHUVKLSGHJUHH
0HPEHUVKLSGHJUHH
DUOLIANG HAN et al.: FCLR: FUZZY CONTROL-BASED LAYERING ROUTING PROTOCOL FOR UNDERWATER ACOUSTIC NETWORKS (XXXX 2022)
/RZ
0HGLXP
+LJK
/RZ
0HGLXP
+LJK
7
41'
)RUZDUGLQJSUREDELOLW\
Fig. 6. The Membership function and FP for node y.
Fig. 5. Membership functions of QND.
TABLE VI
TOTAL F UZZY RULE U SED B Y T HE C ONTROL E NGINE
RER
QND
Forwarding probability
1
Low
Low
Low
2
Low
Medium
Low
3
Medium
Low
Low
4
Medium
Medium
Medium
5
Low
High
Medium
6
High
Low
Medium
7
Medium
High
High
8
High
Medium
High
9
High
High
High
0HPEHUVKLSGHJUHH
Rule
/RZ
0HGLXP
+LJK
)RUZDUGLQJSUREDELOLW\
Fig. 7. The Membership function and FP for node z.
Since the method of gravity center has smoother output
control than the method of maximum membership, the method
of gravity center is used in the process of defuzzification, in
which the x-axis value of the center point of the area enclosed
by the membership function curve and the x-axis is the final
output value of the FCS. The calculation formula of the output
value FP on a discrete domain with j output quantized number
is given in equation (10).
j
P
FP =
(Xi µ(Xi ))
i=1
j
P
(13)
µ(Xi )
i=1
where j is the number of quantized values on the discrete
domain of the x-axis, Xi is the ith fuzzy value in the x-axis
and µ(Xi ) is the membership degree of Xi .
Now we present the BFN selection in detail, which is
divided into the following steps.
Step 1: Before a source node (or a CFN) forwards data, it
first selects the nodes whose layers are 1 lower than its layer
from the neighbor table as the QFN nodes.
Step 2: The current node calculates the RER and QND of
QFN nodes according to equations (8) and (9).
Step 3: The RER and QND of QFN nodes are input into
the FCS successfully and the FPs of QFN nodes are output.
Step 4: The node ID of the BFN is returned.
Step 5: The BFN forwards the data packet.
Assume the RER and QND of node y are 0.85 and 0.5
respectively, and the RER and QND of node z are 0.92 and
0.6 respectively. The FP and membership functions for node
y and node z are shown in Fig.6 and Fig.7 respectively.
According to Table VI, the following rules take effect:
Rule 1: IF RER is Medium and QND is Medium, then FP
is Medium;
Rule 2: IF RER is Medium and QND is High, then FP is
High;
Rule 3: IF RER is High and QND is Medium, then FP is
High;
Rule 4: IF RER is High and QND is High, then FP is High.
After fuzzy inference and defuzzification, the output values
of FCS for node y and node z are 0.62 and 0.69 respectively.
Therefore, the probability that node z is selected as the BFN
is higher than that of node B, and the QFN with the maximum
forwarding probability is selected as the BFN.
D. Data Forwarding
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IEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2022
Source node or CFN
have data to send
Yes
Source node or CFN will
select BFN
Neighbor nodes hear the
data packet
Neighbor_ID ==
sink_ID ?
Update NIT
No
Select QFN
m_ID == BFN_ID ?
Calculating RER and
QND of QFN
Sink_ID ==
BFN_ID ?
Yes
Input RER and QND
No
Discard the data packet
float Fuzzy_controller::trimf(float x, float a, float b, float c)
{
float u;
if (x >= a && x <= b)
u = (x - a) / (b - a);
else if (x > b && x <= c)
u = (c - x) / (c - b);
else
u = 0.0;
return u;
}
RER =it->second.ap /DEFAULT_INIT_AP ;
QND=(it->second.up_layer_neighbor_number)/(it->second.all_neighbor_number) ;
desired_tmp = Fuzzy_controller::calculate_FP(RER,QND);
No
Yes
Reveiving the data
packet
Fig. 9. Example codes of the FCLR protocol.
(0,250,0)
FCS
Determine the maximum
value of the output result
Sink node
Communication
completed successfully
(250,0,0)
return the BFN_ID
Ordinary node
Forwarding data packet
End
Fig. 8. The main workflow chart of the FCLR protocol.
After the BFN is determined, the data packets can be sent
(or forwarded). Usually, all the neighbor nodes within the
communication range of the sender can hear the data packets,
and one of the following three cases may occur:
Case 1: If the receiving node is the sink node, the sink node
will receive the data packet, update its NIT and forward the
data packet to the data center by a radio frequency signal.
Case 2: If the receiving node is the BFN, the BFN will
receive the data packet, update its NIT, then select the next
BFN at the next hop, and forward the data packet to the next
BFN;
Case3: If a node other than the BFN hears a data packet, it
will update its NIT and discard the packet.
When the sink node receives the data packet, it is considered
that the communication has been completed successfully. The
main workflow of the FCLR protocol is shown in Fig.8. Fig.8
shows the overall process to select the best forwarding node
and perform routing forwarding.
V. P ERFORMANCE E VALUATION
In this section, we evaluate the performance of the proposed
FCLR protocol. The FCLR protocol is compared with DBR,
QELAR, VBF, RLOR, and ELW-CFR protocols in terms of
packet delivery rate (PDR), end-to-end delay (EED), and total
energy consumption (TEC).
The performance of the proposed FCLR protocol is evaluated by simulation experiments with the NS3 simulator, which is
a new network simulator written in C++. Compared with NS2,
the architecture of NS3 is clearer and more convenient for the
researcher to develop their routing protocol. Fig.9 gives some
codes of triangle membership functions and the calculation of
input and output variables of FCS.
A. Simulation Parameter Setting
(0,0,500)
Source node
Fig. 10. The network topology in simulation experiments.
We consider the network model described previously.
The sensor nodes are randomly deployed in a 3D area of
500m*500m*500m, and the sink node is deployed on the water
surface, which is shown in Fig.10.
A movement model is applied to the sensor nodes, and
each node moves at 1-3m/s in the horizontal direction and
the movement in the vertical direction can be negligible. The
main simulation parameters are listed in Table VII.
B. Performance Metrics
To better evaluate the performance of the protocol, the
following evaluation indicators are selected:
1) Packet Delivery Rate:
PDR: The rate of the number of packets successfully
received by the sink node to the number of packets sent by
source nodes. PDR is given by:
Ntrans
P
P DR =
i=1
Nrece
Nsend
Ntrans
(14)
where Ntrans is the experiment times. Nrece is the number of
data packets successfully received by the sink node. Nsend is
the number of data packets sent by source nodes.
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DUOLIANG HAN et al.: FCLR: FUZZY CONTROL-BASED LAYERING ROUTING PROTOCOL FOR UNDERWATER ACOUSTIC NETWORKS (XXXX 2022)
9
1 .0
TABLE VII
S IMULATION PARAMETER
0 .8
Value
Unit
Simulation scene range
500*500*500
m
Simulation time
800
S
Number of nodes
300-800
Data packet size
64
Bytes
Hello packet size
33
Bytes
Transmitting power
2.0
w
Receiving power
0.1
w
Idle power
0.01
w
Topology
Random
Initial energy
1000
Number of experiments
20
0 .6
F C L R
Q E L A R
D B R
V B F
P D R
Simulation parameter
0 .4
0 .2
0 .0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
N u m b e r o f N o d e
Fig. 11. The effect of the number of nodes on PDR.
J
5
4
EED: The time taken to deliver a data packet from a source
node to the sink node. EED is given by:
E E D (s )
Ntrans
P
(Tsink − Tsource )
i=1
F C L
Q E L
D B R
V B F
R L O
E L W
3
(15)
2
where Ntrans is the experiment times. Tsink is the time when
the sink node receives a data packet, and Tsource is the time
when the source node sends the packet.
TEC: The sum of the energy consumed by all nodes
for transmitting, receiving, and hearing packets during
the simulation time. TEC is calculated according to the
equation(16):
1
EED =
Ntrans
P
T EC =
Ntrans
(Esend + Erece + Eidle + Esleep )
i=1
Ntrans
(16)
where Ntrans is the experiment times. Esend , Erece , Eidle
and Esleep are the energy consumed by nodes in sending,
receiving, idle and sleep states respectively.
C. Simulation Results Analysis
In this section, we analyze the effects of node number and
node movement speed on PDR, EED, and TEC. Furthermore,
we analyze the effect of the initial energy of nodes on PDR.
1) The effect of the number of nodes on protocol performance:
The effects of node number on PDR, EED, and TEC are
shown in Fig.11, Fig.12, and Fig.13 respectively. To fully
evaluate the performance of the FCLR protocol, in Fig.11,
Fig.12, and Fig.13, the PDR, EED, and TEC of the FCLR
protocol are compared with the QELAR, DBR, VBF, RLOR,
and ELW-CFR protocols respectively.
As shown in Fig.11, the PDRs of the six protocols all show
an increasing trend as the number of nodes increases, which
is because, a better node would be selected as the BFNs with
the number of (candidate) forwarding nodes increases, and
the better BFN can deliver data packets to the sink more
R
A R
R
-C F R
0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
N u m b e r o f n o d e s
Fig. 12. The effect of the number of nodes on EED.
successfully. From Fig.11, it can be seen that when the number
of nodes reaches 600 (800, 800, 800, 700, or 700), the PDR
of the FCLR (QELAR, DBR, VBF, RLOR, or ELW-CFR)
protocol reaches its maximum value. The PDR of the FCLR
protocol is always higher than any of the QELAR, DBR, VBF,
and ELW-CFR protocols but lower than that of the RLOR
protocol.
From Fig.12 we can see that the EEDs of the six protocols
all show a decreasing trend as the number of nodes increases.
When the number of nodes reaches 800, the EEDs of all the
protocols reach their minimum values. When the number of
nodes is smaller than 700, the EED of the FCLR protocol is
less than any of the QELAR, DBR, VBF, RLOR, and ELWCFR protocols. However, when the number of nodes is more
than 700, the EED of the FCLR protocol is higher than that
of the QELAR and ELW-CFR protocols.
From Fig.13 we can see that the TECs consumed by FCLR,
QELAR, DBR, and VBF protocols increase as the number of
nodes increases since more nodes are involved in receiving or
hearing, or forwarding packets. It is also seen that the TEC of
the FCLR protocol is the least compared with QELAR, DBR,
and VBF protocols. This is because in the FCLR protocol only
one node (the BFN) forwards the packet at this hop. Redundant
forwarding is unavoidable in other protocols due to the nature
of broadcasting. The number of redundant packets forwarded
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IEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2022
1 .0
0 .8
1 0 0 0 0
8 0 0 0
F C L R
Q E L A R
D B R
V B F
6 0 0 0
4 0 0 0
2 0 0 0
0 .6
P D R
T o ta l E n e r g y C o n s u m p tio n (J )
1 2 0 0 0
0 .4
0 .2
0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
0 .0
N u m b e r o f N o d e
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
I n itia l e n e r g y (J )
Fig. 13. The effect of the number of nodes on TEC.
Fig. 15. The effect of the initial energy on PDR.
1 .0
0 .8
0 .6
P D R
1 -3 m /s
3 -5 m /s
0 .4
0 .2
0 .0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
N o d e o f N u m b e r
Fig. 14. The Effect of node movement speed on PDR.
increases with the number of nodes, resulting in an increasing
in TEC.
From Fig.11 to Fig.13, we can see that the FCLR protocol
outperforms the DBR and VBF protocols in terms of PDR,
EED, and TEC. Although the PDR of the RLOR protocol is
better than that of the FCLR protocol, the FCLR protocol outperforms the RLOR protocol in terms of EED. In addition, the
FCLR protocol outperforms the QELAR protocol in terms of
TEC. In general, the FCLR protocol has superior performance
in terms of PDR, EED, and TEC.
2) The effect of movement speed on protocol performance:
To further evaluate the effect of node movement on the
performance of the FCLR protocol, we set the nodes to
be moveable. The movement speed is set to 1-3m/s and 35m/s successively in simulation experiments. We analyze the
effect of node movement on the network performance. The
simulation experimental results are shown in Fig.14.
From Fig.14 it can be seen that the speed of node movement
has a significant effect on the PDR. The PDR with nodes
moving at 3-5m/s is significantly lower than that with nodes
moving at 1-3m/s. This is because when the movement speed
of the node is high, the neighbor nodes of the sending node
may move out of the senders transmission range. If the NIT
of the current node is not updated timely, the wrong BFN may
fail to receive and forward data packets, which leads to lower
PDR. It is also seen that, when nodes move at 3-5m/s, the
PDR can still reach 75.8%. Meanwhile, we can see that when
the number of nodes is 600, the PDR reaches its peak value
for both movement speeds.
3) The effect of initial energy on protocol performance:
The effect of the initial energy of nodes on PDR is shown
in Fig.15, in which the initial energy is set to 10J, 20J, 30J,
40J, 50, 60J, 70J, and 80J.
As shown in Fig.15, it can be seen that the initial energy
has a significant effect on the PDR. When the initial energy
of the node is between 10-60J, with the increase of the initial
energy, the PDR increases obviously. When the initial energy
of the nodes is 60J, the PDR of the FCLR protocol reaches
the maximum value. As the initial energy of nodes increases
gradually, the number of nodes that fail to participate in
data forwarding due to insufficient energy gradually decreases.
Therefore, the PDR of the FCLR protocol gradually increases.
When the initial energy of the nodes exceeds 60J, the PDR
remains at about 92%. This is because the nodes in the network
hardly run out of their energy during the simulation time when
the initial energy of the nodes exceeds 60J.
There are some limitations to our work. The broadcasting
interval of HELLO packets is not easy to determine. When
the interval is large, the layer or NIT may not be updated in
time, resulting in a node that has moved away being selected
as the BFN and a drop in PDR. When the interval is small,
there will be too many HELLO packets in the network, which
leads to a collision between HELLO packets and data packets,
also a drop in PDR.
VI. P ERFORMANCE E VALUATION I N F IELD
E XPERIMENTS
To better verify the performance of the FCLR protocol and
apply FCLR to the real environment, we developed and implemented the FCLR protocol in August 2021 and conducted tests
in the real environment at Qinghai Lake, the largest saltwater
lake in China, from September 19 to September 21, 2021, and
the test scene is shown in Fig.16.
We deployed five modems in the Qinghai lake. The five
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11
TABLE VIII
S IMULATION PARAMETER
Fig. 16. Lake test scene.
Raspberry Pi
Cable
Test parameter
Value
Modem model
AMN-OFDM-13A
Modem modulation mode
OFDM
Unit
Number of modem
5
Modem ID
1,5,8,36,65
Distance of node
200-1200
m
Transmitting power
20
w
Transmission gain
20
Data type
Picture
Picture size
11.2/4.4
KB
Data packet size
200
Bytes
Hello packet size
33
Bytes
Data packet interval
163
s
Hello packet interval
47
s
Data server
Node_ID:1
Node_Type:Sink_node
GPS:36.58494949340820,100.4923858642578
Depth:5
Node_ID:36
Node_Type:Forwarding node
GPS:36.58472442626953,100.4927215576172
Depth:5
Node_ID:8
Node_Type:Forwarding node
GPS:36.58496093750,"lon":100.4923095703125
Depth:5
Node_ID:5
Node_Type:Forwarding node
GPS:36.57589340209961,100.5027389526367
Depth:5.0
Node_ID:65
Node_Type:Source_node
GPS:36.58438873291016,100.5003356933594
Depth:6.0
Fig. 17. Node deployment in the Qinghai lake.
modems formed firstly a one-hop network, then formed a twohop network, which is shown in. Fig.17. The IDs of the five
nodes are 1, 5, 8, 36 and 65 respectively. In Fig.17, each
node is powered by a battery and controlled by a raspberry
pi microcomputer. The sink node is fixed at the dock in the
test which has a diving depth of roughly 5m. The ID of the
sink node is 1. The sink node is responsible for transmitting
the data forwarded by other nodes to a server through 4G and
the Internet. Node 5, Node 8 and Node 36 are responsible for
relaying the data packets from the source node. The source
node is responsible for collecting and transmitting data. The
ID of the source node is 65.
We carried out both one-hop experiments and two-hop
experiments, in which the source node is stationary first and
then mobile, the parameters used in the field test are shown
in Table VIII.
In the field test, the RCHF protocol proposed by our team
was used as the MAC protocol. The RCHF MAC protocol
has a re-transmission mechanism for data packets. However,
it should be emphasized that: (1) when calculating PDR, the
number of data packets received successfully by the sink
node does not include the re-transmitted packets; (2) When
calculating the throughput of the network, the number of data
packets received successfully includes the re-transmitted data
packets.
In the field experiments, all the information such as the
receiving time, sending time or re-transmission time of each
packet at each node is recorded in a log file in each node,
which is analyzed and counted. Based on the data in log files,
the performance of the FCLR protocol is evaluated in terms
of PDR, throughput, and end to end delay(EED). The PDR is
defined as in equation (14) and the throughput is defined as
follows.
Throughput: the number of packets successfully received
by the sink node multiplied by the length of a packet, divide
by the time it takes to receive the packet, which is given by:
N umrece ∗ Lenpack
(17)
T ime
where N umrece is the number of packets successfully received by the sink node. Lenpack is the length of a data packet.
T ime is the time interval from the time the sink node receives
the first data packet to the time it receives the last data packet.
TP =
A. One-hop test with node stationary and move
In all the field experiments, the sink node is deployed under
a wharf and other nodes are deployed in water with a depth
of about 5m. The nodes are either stationary or move with
the water flow or a boat. The distance between two neighbor
nodes is within a range of 200m-1000m. The data delivered
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IEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2022
TABLE IX
T HE P ERFORMANCE OF O NE H OP N ETWORK WITH A S TATIONARY
S OURCE N ODE
Metrics
Group
PDR
TP(bps)
1
2
3
4
Average
0.982
0.965
0.947
0.965
0.965
512
517.61
507.28
255
250
255.75
518.69 480.77
EED(s)
249
269
TABLE XI
T HE P ERFORMANCE OF O NE H OP N ETWORK WITH A M OVING S OURCE
N ODE
Metrics
Group
PDR
TP(bps)
1
2
3
4
Average
0.407
0.778
0.926
0.741
0.713
471.23 235.82 333.96 350.64
347.91
4 5 0
4 0 0
TABLE X
T HE P ERFORMANCE OF O NE H OP N ETWORK WITH A M OVING S OURCE
N ODE
Group
PDR
TP(bps)
EED(s)
1
2
3
4
Average
1
1
0.964
0.964
0.982
486.77 516.57 433.42 517.62
237
241
312
251
3 0 0
E E D (S )
Metrics
3 5 0
2 5 0
O n e h o p
T w o -h o p
2 0 0
488.6
1 5 0
260.25
1 0 0
5 0
0
underwater is a digital picture of a fish with a size of 11.2KB.
Table IX and Table X list the test results in the one-hop test
when the source node is stationary and moving respectively.
As can be seen in Table IX, the maximum values of PDR
and TP for the one-hop test when the source node is stationary
can reach 98.2% and 518.69 bps respectively. The minimum
EED delay is 249s. Among the four groups of experiments,
the results of the first group are the most perfect in terms of
PDR, TP and EED. In the results from the first group, the
throughput is the highest since the sink node takes the least
time to receive successfully all the data packets in the picture
file. In contrast, in the results of the second group, the opposite
was true, the increase in EED leads to a decreasing of TP.
From Table X it can be seen that the maximum values of
PDR and TP for the one-hop test when the source node is
moving can reach 100% and 517.62 bps respectively. The minimum EED is 237s. Among the four groups of experiments,
the results of the second group are the most perfect in terms
of PDR, TP and EED. The results of the fourth group achieve
the highest TP the lowest PDR, and the longest EED, in which
more data packet are re-transmitted.
According to Table IX and Table X, there is little differences
in the performance of the proposed protocols in different
experimental scenarios. The average of the test results shows
that the performance of the network with a fixed source node
outperforms that with a moving source node in terms of TP
and EED. The PDR when the source node is moving is greater
than the PDR when the source node is stationary, which is
different from the simulation results.
1
2
3
4
A v e r a g e
D iffe r e n t te s t g r o u p s
Fig. 18. EED of one-hop test and two-hop test.
B. Two-Hop test with nodes moving
XI shows the PDRs and TPs of the two-hop test in which the
three nodes with IDs of 5, 36 and 65 moved. Fig.18 shows
the EED results of the one-hop and two-hop tests.
From Table XI it can be seen that the maximum values
of PDR and TP for the two-hop test can reach 92.6% and
471.23 bps respectively when Node 5, Node 36, and Node
65 are moving. The first test achieves the lowest PDR and
the highest TP. The second test achieves the lowest TP. This
is because a large number of collisions and re-transmission
result in a long EED and small TP, which can be seen also in
Fig.18.
Fig.18 shows the EEDs of the one-hop and the two-hop
tests. From Fig.18 it can be seen that the EED in the twohop test is significantly greater than that in one-hop test. The
maximum delay in two-hop is 401s, in which some of the data
packets to Node 1 forwarded by Node 5 are re-transmitted
twice according to the log file, leading to the long delay at
the second hop. The minimum EED in the two-hop test is
287s.
According to Table X and Table XI, the overall performance
in the one-hop network outperforms that in the two-hop
network in terms of PDR, TP and EED. After analyzing the
log files in each node we draw an important conclusion, that
the multi-hop transmission in UANs decreases the overall
performance of the network. The more hops, the worse the
network performance.
In the two-hop test, No.1 and No.36 are deployed under
the dock, No.5, No.8, and No.65 are deployed in water using
yachts. The depths of No.1 and No.36 are approximately 5m,
and the depth of No.5, No.8, and No.65 are approximately
6m. The distances between The distance between two neighbor
nodes is within a range of 200m-1200m. The data transmitted
in the test is a digital photo of fish with a size of 4.4KB. Table
In this paper, we propose a fuzzy control-based layering
routing protocol FCLR. Extensive simulation experiments and
real field tests are conducted based on the proposed FCLR
protocol. We compare and analyze the proposed protocol with
the QELAR, DBR, VBF, RLOR, and ELW-CFR protocols.
VII. C ONCLUSION
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This article has been accepted for publication in IEEE Sensors Journal. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2022.3218136
DUOLIANG HAN et al.: FCLR: FUZZY CONTROL-BASED LAYERING ROUTING PROTOCOL FOR UNDERWATER ACOUSTIC NETWORKS (XXXX 2022)
When the moving speed of nodes is 1-3m/s, the PDR of the
FCLR protocol can reach 91.67%, which is higher than that
of QELAR, DBR, VBF, and ELW-CFR protocols. The EED
of the FCLR protocol is always less than any of the QELAR,
DBR, VBF, RLOR and ELW-CFR protocols when the number
of nodes is smaller than 700. With the increase in number
of nodes, the TEC of the FCLR protocol increases, but it
is smaller than that of QELAR, DBR, and VBF protocols.
Moreover, when the initial energy of the nodes is 60J, the PDR
of the FCLR protocol reaches the maximum value. When the
initial energy of the nodes exceeds 60J, the PDR remains at
about 92%. Meanwhile, the lake test results show that the
FCLR protocol exhibits stable and acceptable performance
in a real environment. When nodes are moving, the packet
delivery rate can reach 92.6 percent and the throughput can
reach 350.64bps.
In future research, we will optimize the HELLO packet
broadcast interval to alleviate the drop in PDR caused by a
collision between HELLO packets and data packets. Furthermore, we will combine the FCLR protocol with reinforcement
learning and other technologies to improve the FCLR protocol
and further improve the performance of the network.
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© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on November 09,2022 at 07:15:09 UTC from IEEE Xplore. Restrictions apply.
This article has been accepted for publication in IEEE Sensors Journal. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2022.3218136
14
IEEE SENSORS JOURNAL, VOL. XX, NO. XX, XXXX 2022
Duoliang Han received his master’s degree from
Qinghai Normal University, in 2020. He is now
a doctoral student in Qinghai Normal University,
Qinghai, China. His research interests include
protocol design and optimization.
Xiujuan Du received her Ph. D degree from
Tianjin University,Tianjin, China in 2010. She is
currently a professor in Qinghai Normal University, Qinghai,China. Her research interests include
underwater acoustic networks, wireless network
and security and Internet of things. She has
received the New Century Excellent Talent from
Education Ministry, China, in 2011.
Xiuxiu Liu received her master’s degree from
Qinghai Normal University, in 2016. She is now
a doctoral student and a lecturer in Qinghai
Normal University, Qinghai, China. Her research
interests include underwater acoustic networks.
Xiaojing Tian received her master’s degree from
Qinghai Normal University, in 2021. She is now
a doctoral student in Qinghai Normal University,
Qinghai, China. Her research interests include
protocol design and optimization.
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
Authorized licensed use limited to: BEIJING INSTITUTE OF TECHNOLOGY. Downloaded on November 09,2022 at 07:15:09 UTC from IEEE Xplore. Restrictions apply.