An Optimized Energy-Efficient Hello Routing Protocol for Underwater Wireless Sensor Network

International Journal of Computer Networks & Communications (IJCNC) Vol.17, No.3, May 2025
DOI: 10.5121/ijcnc.2025.17304 57
AN OPTIMIZED ENERGY-EFFICIENT HELLO
ROUTING PROTOCOL FOR UNDERWATER WIRELESS
SENSOR NETWORK
Vinayprasad M S 1
and Jayaram M N 2
Department of Electronics and Communication Engineering, JSS Science and
Technology University, Mysore.
ABSTRACT
Underwater wireless sensor network (UWSN) is a great technological advancement for numerous
implementations, such as pollution monitoring tsunami offshore gas and oil reservoirs. Coherent routing
with optimal energy use is necessary to send received data from the sensor to the sink hub. The distinct
characteristics of the underwater circumstances make designing routing protocols problematic. Therefore,
a novel Dove-based Hello Routing Protocol (DbHRP) was proposed to increase the data broadcasting rate
within low energy. The nodes are initially initialized, and their positions are estimated. Subsequently, the
dove function finds the SP(SP) from source to target, and the status of the target node is analyzed for data
packet transmission. The data transfer function is planned based on the destination node's initial request
priority if the target node needs to receive more data from a different source hub. Finally, the UWSN
communication parameters were measured and compared with prevailing models.
KEYWORDS
Underwater Wireless Sensor Network (UWSN), Shortest path, Source node, Target node, Routing Protocol.
1. INTRODUCTION
The fact that three-quarters of the ground atmosphere is occupied by water suggests that a few of
the world's oceans must be investigated further [1].Certain aspects of underwater environments
and their dynamics require further study [2]. This could contribute to several implementations
that remain obscured and partially submerged and are connected to a massive assortment of
armed services and quasi-applications such as crop production [3], coastlines and safeguards,
telephony, and weather controls and vigilance, lookup operations, and so on [4]. These apps
might also link to many other applications [5]. Consequently, UWSN systems are a pressing
concern, particularly in the rapidly expanding field of oceanic studies [6]. The study's outcomes
have been described by comparing several underwater sensors [7]. The route is complicated to
follow in the aquatic setting compared to specific other systems found throughout nature andis
tied to altitude variables [8]. The environment may be variable as subsea components, i.e.,
Sometimes muddy and often salty [9]. Therefore, to address all of the UWSN characteristics, a
technique compatible with each nature is required [10]. Relative to other established networks,
such as aerial and trunked radio, pulses get a slower submarine transmission speed, the cluster
heads are more prominent, and non-recyclable energy supplies are scarce [11]. Consequently,
anything relies on electricity usage, the proportion of actual use, and the component of protracted
connection [12].As previously said, an exterior mobile independent device is utilized chiefly for
effectively routing transmitted information from one area to another [13]. They are inexpensive
compared to other types of communication [14]. After gathering every node's report, it travels to
a specific area to transmit it, and then, after completing this procedure at each point, it returns to

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the originating position [15]. This step is repeated for each node. Once the information is
collected, a bit stream is generated based on the specific quantity of current specificationsand
other criteria [16]. Transferring large databases in any interaction is challenging without the
quickest path between them [17].
Consequently, those systems may be less efficient despite transferring huge and comprising such
as video and sampling data [18]. However, because they are robust compared to other forms of
connection, they are chosen for relatively brief conversations. The concept of networking
detectors below the surface water was inspired by developing technologies revolving around
automated vehicles and multilevel routing (MR) security possibilities [19]. Despite the potential
for communication problems, the science of acoustics is flexible enough to assist in making the
concept a reality. Interconnected transmission methods have seen advancement in recent years,
making them particularly useful for independent channels.
Section 2 provides the previous studies review. Section 3discussesthe methodology. Section 4
details the results. Section 5concludes the work with future work.
2. REVIEW OF PREVIOUS STUDIES
Frequency band, visual, and electrostatic transmission are some methods that fall under this
category. To take advantage of the enormous available bandwidth required, they are used for
specific distances based on the application necessity [20]. Due to the severe attenuation
characteristics of such wide bandwidth transmissions, antenna systems were required. Recently,
clustering UWSN [21], energy-efficient UWSN [22], etc., were implemented to reduce the
constraints and requirements of data transfer. However, those approaches do not offer a high
throughput rate and require less energy usage. Considering all these issues, the present
investigation aimed to develop the optimal routing strategy for the UWSN application, which was
tested with chief metrics, and outstanding results were obtained.
2.1. A Few Recent Associated Works of UWSN are Detailed as Follows
The routing scheme called clustering (RSC) is implemented for the UWSN as the optimized
routing protocol to control the energy utilization of UWSN. Additionally, the communication
overhead and throughput parameters were investigated by Sun et al. [21]. Finally, the results
verified the implemented strategy's robustness by performing the comparative analysis. However,
this study did not address the path selection process, which resulted in high implementation time.
Kumar et al. [22] have established the energy-efficient paradigm (EEP) for the UWSN dynamic
topology. The chief reason for adopting this objective is. Usually, the sensor nodes need more
energy to capture and transfer data to the other end. Hence, the high energy usage might degrade
the sensor network's robustness by reducing the node's lifetime, resulting in packet loss. To
control these issues, the energy-efficient model is included in this study, which has kept the
designed model in the optimal status. However, a high packet drop occurred.
The multi-hop routing protocol (MHR) is implemented for the UWSN by Yang et al. [23]. Here,
the multi-hop system offered the multi-SP of ever-connected hubs in the sensor environment.
This function has tended to earn less transmission delay time. Also, the multipath has reduced
network data traffic by forwarding the overloaded data to another path. Here, the energy was
optimized by controlling the packet-sharing time. However, it is not aware of malicious events.

59
Noorbakhsh and Soltanaghaei [24] have executed the grid-based routing system (GRS) for the
UWSN framework. Here, the frequency resources were shared for the nodes in the grid
framework. So, the energy resources were initiated before the data-sharing process. When the
data broadcasting function began, the energy resource allocation was terminated. In this way, the
energy constraints of the UWSN were optimized. However, if any nodes were moved from the
grid environment, they lost their data transmission (DT) ability.
Ismail et al. [25] have conducted a routing protocol analysis (RPA) of target node localization
and mobility. Different functions were tested through this routing protocol analysis,including
finding the SP, localization, and energy management. Finally, the performance of the routing
protocols was specified under their unique characteristics. Later, those classified protocols were
utilized for the specific function process in the UWSN. However, using the different protocols for
a single UWSN tended to cause more resource usage.
Ali et al. [26] introduced a novel, time-based, reliable link (TBRL) for the dynamic topology
routing protocol. Primarily, the topology of each node is calculated, and the reliable link is
accessed by selecting the best forwarders for the Communication. Next, the data packets have
been sent through the selected reliable path. It acts as an efficient protocol for underwater
Communication and supports smart city monitoring facilities. However, this method can be
enhanced by selecting the more optimal path.
Though many existing techniques were developed for better transmission, energy efficiency, and
reliability, the proposed DbHRP improves the performance of the existing approaches. This
method differs from other clustering methods, which aim to reduce energy consumption at the
node level but experience high packet drop rates, compromising data reliability. The model also
presents an intelligent packet-scheduling system based on target requests, making data transfer
more dependable and adaptive. In contrast to multi-hop routing protocols that minimize
transmission latency and optimize network traffic, the DbHRP model presents an optimized
scheduling system for improving network resilience and making data transmission reliable. It also
adjusts routing paths dynamically according to network topology, maintaining ongoing data flow
and reducing error. It also performs a sophisticated shortest-path scheme adaptive to network
dynamics, minimizing energy overhead and enhancing overall performance. This method
achieves an adaptive and optimized routing scheme, achieving energy efficiency, transmission
robustness, and network reliability. The comparison of existing limitations and proposed methods
advantages is provided in Table1.
Table1. Comparison of Existing with proposed
Author Existing
Method
Limitations Proposed
Sun et al. [21] RSC Inefficient path selection Develops a SP selection process that reduces the
delay and energy usage
Kumar et al.
[22]
EEP high packet drop has
occurred
It improves data transmission reliability by
introducing an optimized scheduling method
based on target requests.
Yang et al. [23]. MHR Increases the transmission
latency
Improves network resilience by transmitting data
based on network conditions
Noorbakhsh and
Soltanaghaei
[24]
GRS Inefficient data transmission
occurs
Dynamically adjusts routing paths based on real-
time network topology to ensure continuous data
flow.
Ismail et al.
[25]
RPA It increases the resource
usage
The hybrid routing system consumes less energy
and reduces overhead
Ali et al. [26] TBRL Limits in suitable path
selection and increased
delay
Develops shortest-path Selection to improve
transmission efficiency

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2.2. Problem Statement
Recently, UWSNs have focused on discovering underwater resources and instantaneous data
acquisition. In contrast to regular WSNs, UWSNs experience distinct challenges like high delay,
low bandwidth, node mobility, and power restrictions. Several routing methods have been
developed for energy minimization, such as clustering-based schemes, multi-hop, and grid-based
models. Yet, traditional models cannot accommodate dynamic data broadcasting scenarios,
resulting in high packet loss rates, poor path selection, and network performance degradation.
Moreover, energy-efficient frameworks cannot balance network lifetime and data transmission
reliability in dynamic topological variations. A new DbHRP that combines an intelligent shortest-
path selection scheme with an optimized scheduling strategy to improve energy efficiency,
reduce latency, and improve overall network performance in UWSNs is introduced to overcome
these limitations.
3. METHODOLOGY
The current study's primary steps are outlined below,
 Initially, the needed sensor hubs were created in the UWSN environment.
 Consequently, a novel DbHRP was executed to find the SP of every hub and to
transfer the packets optimally.
 Once the SP was enabled, then the status of the target hubs was estimated. If the
target node has different source hubs, the data has been scheduled based on the
target request.
 Henceforth, the data is transferred effectively between the sensor users, with
optimized energy usage.
 Finally, metrics like network lifetime, latency, energy consumption, throughput,
and data transfer speed were measured and compared with other models.
3.1. Proposed Methodology
A novel Dove-based Hello Routing Protocol (DbHRP) was established for the UWSN to improve
the data broadcasting rate between the users. Primarily, the required sensor nodes were created
with the sensing capacity, and then the dove function was activated to measure the SP between
the sources and sink nodes. If the sink node has to receive more data from a different source
node, then the data transmission is scheduled as per the target's first request priority, which helps
control energy usage. The Dove Optimization Algorithm (DOA) enhances the HELLO Routing
Protocol performance by dynamically optimizing critical routing parameters. HELLO-based
wireless network protocols employ periodic message exchanges to build and maintain routes, but
ineffective parameter adaptation may lead to unnecessary energy wastage and network
congestion. DOA is inspired by doves' foraging and migration behavior and optimizes
transmission power, node selection, and route stability parameters. DOA employs adaptive
exploration and exploitation mechanisms to optimize HELLO intervals, offering energy-efficient
neighbor discovery and preventing redundant transmissions. The optimization also alleviates link
failures by periodically reconfiguring the optimal routes according to network conditions, making
DOA appropriate for mobile and resource-constrained networks.

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3.1.1. Node Initialization
A novel DbHRP is proposed for energy-efficient routing (ER). The proposed DbHRP combines
dove optimization [27] and hello protocol. Initially, the required nodes are initialized in the
network environment. The node initialization is expressed in Eqn. (1).
𝑁 = 𝑁𝑧{𝑧 = 1,2,3, … … 𝑘} (1)
𝑁is the UN, and 𝑘defines the quantity of nodes. Here, the data sent by sensor nodes is
collected by many surface sinks at the water body's surface. Most sensor nodes are located under
the water's body, while others are positioned above the surface. According to the quality of the
link, the data packets were transferred. A reliable connection sends the data gathered from one
node to another. The success of the routing is verified after the arrival of data to any of the sink
nodes. The sinks can communicate with each other with more bandwidth and less latency due to
an acoustic communication capability. Here, the DbHRP is a position-aware routing protocol.
Due to the water's movement, the sensor hubs' position varies. So, the position of the employed
nodes is updated by the dynamic topology discovery algorithm described in [26].For underwater
Communication, the acoustic medium is mainly used. The acoustic channel was generated by
[28] for the recommended model, considering factors such as water temperature, pressure, and
salinity.
3.1.2. Shortest Path Finding using DbHRP
After confirming the position of each node, a short path is estimated. The suggested DbHRP
moves the data from the node to the sink. The shortest route is selected depending on two
parameters, namely link quality and distance. Dove selects the nearest spots with more crumps in
dove swarm optimization. Thus, in the presented technique, the SP is selected so that the
information packets can reach the sink node. The fitness features of the Dove identify the node
with the nearest distance and the great link quality. It is expressed in Eqn. (2).
𝑁𝑧𝑠
𝑑 = arg 𝑚𝑎𝑥[𝑓(𝑁𝑧).(𝑠, 𝑑)] (2)
Here 𝑁𝑧𝑠
𝑑Is the next forwarding node selection function, 𝑠represents the link quality 𝑑 denotes
the node's distance, and f is the fitness function. Link quality indicates the strength and stability
of the communication link between two nodes. Distance is the logical distance from the present
node to the destination. However, the link quality is concerned with the pressure parameter at the
network. Hence, the target node is selected to forward the packet. The process is repeated until
the SP is selected for the sink hub. Based on this optimization, the source Node chooses the
optimal forwarder node with a shorter distance and lower pressure. This reduces the useless
transmissions to the other nodes, which causes low energy consumption.
3.1.3. Analyzing Target Node Status
The destination node's availability to transfer the data is assessed once the path has been selected.
The Eqns. (3) and (4) are used to analyze node availability.
𝑡𝑧 = λ𝑡𝑧
−1
+ 𝑒
[𝑓(𝑁𝑧)−𝑓(𝑁
𝑧𝑠
𝑑)]
(3)
𝑡𝑧 = {
𝑖𝑓 𝑓 (𝑁𝑧𝑠
𝑑) = 0 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒
𝑖𝑓 𝑓 (𝑁𝑧𝑠
𝑑) ≠ 0 𝑛𝑜𝑡 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒
(4)

62
Here 𝑡𝑧is the target node availability, λ represents the tracking function, and 𝑡𝑧
−1
is the previous
(source) node. 𝑖𝑓 𝑓(𝑁𝑧𝑠
𝑑) = 0 it means the node is available for packet transmission and
𝑖𝑓 𝑓(𝑁𝑧𝑠
𝑑) ≠ 0means the node is unavailable for packet transmission. The Dove's satiety degree
updating function attains the target node availability. To locate more food, doves go in the
direction of the Dove with the greatest level of satisfaction. In each iteration, the satiety degree of
the Dove is updated. Through this Dove behavior, the status of the selected nodes is updated. The
status of the nodes is analyzed through Eqn. (3) and identified using the condition given in Eqn.
(4). If the node is available, the packets are sent to the destination.
3.1.4. Scheduling Data Request
Each source node contains the same type and an equal number of sensors. Each data packet is
sent to the destination node with a priority number that is dynamically assigned by the source.
The sink node validates the arrived packets' source address and priority digit and orders the
packet. Priorities were assigned, and the scheduling algorithm then scheduled the packets for
transmission according to the priority number. The first request priority sent the data to the target
node, and the proposed routing protocol carried out the data transmission.
Algorithm 1. DbHRP
Start
{
Initialization ()
{
z
N
N,
int
k
z
Nz ,.......
3
,
2
,
1


//initialization of node
}
The position of the node has been assessed.
developed DbHRP
// The protocol has been developed to incorporate the necessary features.
{
Pathfinding ()
{
f
N d
s
z
,
int
//pathfinding variables are initialized
)]
(
max[
arg z
z
N
f
N d
s

//target node is estimated based on the fitness function
// SP is selected by repeating the function
}
Target node status ()

63
{

,
int z
t
//The variables for node analysis have been initialized.
0
)
( 
d
s
z
N
f
if
{
available node
}
else{
Node is not available.
}
}
Data request scheduling ()
{
The information was supplied in alignment with the original priority
request.
}
} Stop
Algorithm 1 details the developed model's procedures and methodologies, including pseudo-
code-formatted mathematical function parameters. The code was executed, and the outcomes
were validated. The workflow of the designed protocol is also given in Fig.1.
Fig.1. Flowchart of DbHRP

64
4. RESULTS AND DISCUSSION
The developed DBHRP is designed and executed in the MATLAB platform. Primarily, the
required nodes are initialized in the network environment. A new DbHRP for energy-efficient
routing is proposed, and its performance metrics are assessed. The simulation parameters
required for execution are tabulated in Table 2.
Table 2. Simulation parameters
Parameter Description
Platform MATLAB
Nodes 50,100,150,200,250,300,350,400,450,500
Wireless channel Acoustic and radio
Network area 1000 × 1000 𝑚2
Frequency 15KHz
Mobility of nodes Random
Sink hub 5
Antenna Omni antenna
Packet size 1000 bytes
Range of Transmission 70m
Network topology 2D
Receiving power 0.1 W
Transmitting power 0.5W
Idle power 0.008 W
Communication medium Wireless
Initial energy 1000 J
The simulation parameters in Table 2 were selected to closely mimic actual UWSN conditions
while balancing computational efficiency and accuracy. The network size (1000×1000 m²) and
number of nodes (50–500) were chosen to examine scalability and network performance at
different densities. Realistic signal propagation is provided by the transmission range (70m) and
frequency (15KHz), which are under-accepted requirements for underwater Communication. The
undersea nodes' energy constraints determine the power consumption, and the initial energy
(1000 J) offers an adequate operational lifespan. The antenna and 2D topology were used to
facilitate modeling to capture the important routing dynamics in underwater networks (UN).
4.1. Case Study
The proposed DbHRP is tested with varying numbers of nodes to examine its functioning range.
Here, for the study of DbHRP, an efficient RP system is tested with 50 and 100 sensor nodes and
5 sink hubs. The simulation result for the shortest route finding data transmission is shown in
Fig.2. Here, the red color node indicates the two source nodes, source 1 and source 2. (b). The
black shaded line indicates the SPs specified by the source nodes using the iteration of the fitness
function to send the data to the sink. Further, the DT process was enabled through the selected
path.

65
Fig.2.Path finding and transmission of data packets: (a) 50 nodes, (b) 100 nodes
4.2. Performance Analysis
The developed model's performance is validated and compared with current methods such as
whale green UN (WGUN) [29], cluster MR (CMR) [30], ER reliable multipath (ERRM) [31],
optimized Depth RP (ODRP) [32], TBRL [26], Depth Cluster Routing (DCR), Adaptive DR
(ADR) and Collision DR (CDR) [33].
4.2.1. Throughput
It defines the total quantity of packets collected successfully by the sink hub presented at the
water's surface. Also, it validatesthe total amount of information a system can process at a
specific time. The throughput rate can measure the efficiency of the system. It is calculated by the
Eqn. (5).
𝑇 =
𝑎𝑟𝑟𝑖𝑣𝑒𝑑 𝑝𝑎𝑐𝑘𝑒𝑡𝑠
𝑠𝑖𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒
(5)
The replica throughput rate for WGUN attained is188 Kbps, ERRM scored 299 Kbps, and the
TBRL protocol gained 269 Kbps. At the same time, the proposed technique achieved a
throughput rate of 161 Kbps, 191 Kbps, 214 Kbps, 296 Kbps, and 338 Kbps for 100-500 nodes,
respectively. This comparison shows that the proposed scheme's throughput is higher than other
methods.
4.2.2. Energy Consumption
The network's total energy used to gather data, transmit, and receive is called energy
consumption (EC). The average energy taken to select the target nodes at one round is considered
energy consumption. It mainly depends on the range between destination and source and the size
of the packet. It is measured in Joules (J). The resulting values are recorded in Table 3.

66
Table 3. Comparison of EC
Energy consumption (J)
Nodes WGUN TBRL ODRP Proposed
100 229 164 659 144.6
200 398 362 1110 338.4
300 1880 568 1431 495.6
400 3449 748 1784 598.3
500 5018 927 2224 691.4
4.2.3. Network Lifetime
It is described as the network's active phase until node death. The factors such as the frequency of
utilized nodes, distribution, and connection impact the network's lifetime (NLT).The NLT
comparison is provided in Table.4 The low energy consumption increased the network lifespan.
Table 4. Comparison of NLT
Network lifetime (s)
Number of nodes DCR ADR CDR Proposed
100 721 720 714 925.39
200 735 730 750 1187.40
300 814 809 804 1392.37
400 869 863 859 1408.37
500 889 884 898 1492.25
4.2.4. Packet Delivery Ratio
It is determined by adding up every data packet that leaves the source node and makes it to the
sink hub. It is determined by the ratio of total packets reached at the sink hub to the complete
packets sent from the source node. The calculation of average PDR is expressed in Eqn. (6). The
comparison is shown in Table 5.
𝑃𝐷𝑅 =
𝑎𝑟𝑟𝑖𝑣𝑒𝑑 𝑝𝑎𝑐𝑘𝑒𝑡𝑠
𝑡𝑜𝑡𝑎𝑙 𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑠𝑒𝑛𝑡
(6)
Table 5. Comparison of PDR
Packet delivery ratio (%)
Number of nodes TBRL ODRP Proposed
100 50 73.82 75.36
200 70 75.8 78.4
300 81 84.82 86.4
400 85 91.9 92.4
500 89 96 97.5
4.2.5. Delay
There is a delay in the data packet's transit from the source node to the sink hub. It is the typical
delay of all information packets flowing at the target from various sources. This parameter

67
validates the time efficiency of the routing protocol. The delay is calculated using the expression
described in Eqn. (7). The DbHPRdelivered the data quickly, which shows efficiency. The
comparison of delay rate is noted in Table 6.
𝐷 =
𝑡𝑖𝑚𝑒 𝑡𝑎𝑘𝑒𝑛 𝑡𝑜 𝑑𝑒𝑙𝑖𝑣𝑒𝑟 𝐼𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 𝑝𝑎𝑐𝑘𝑒𝑡𝑠
𝑡𝑜𝑡𝑎𝑙 𝑝𝑎𝑐𝑘𝑒𝑡𝑠
(7)
Table 6. Delay rate comparison
Delay(ms)
Number of nodes TBRL CMR Proposed
100 369.4 610 322
200 319.6 512 241
300 240.1 436 184
400 198.6 338 158
500 159.4 253 134
4.2.6. Transmission Loss and Path Creation
The system's performance improved when the transmission loss (TL)was lower. TheTL is
computed by Eqn. (8).
𝐿𝑇 = 20𝑙𝑜𝑔𝑡 + 𝜙∗
𝑡∗
10−3
(8)
Here, the distance between the receiver and sender is indicated as𝑡 measured in meters,𝜙 is
indicates the absorption coefficient computed in dB/km.The amount of time needed for the
system to find a route from its starting point to its final destination to forward the data packet to
the sink nodes is termed path creation (PC) time. The time consumed for achieving a short path
and the transmission loss by the presented optimized protocol is given in Fig.3 with the different
number of initialized nodes.
The proposed DbHPR attains the TL(dB) of 71.445, 127.009, 223.259, 387.31, 601.59, 798.782,
1021.908, 1302.295, 1616.716, and 1918.469 for the different frequencies such as 500 Hz to
9500 Hz in 1000 Hz intervals respectively. Also, it attained the path in 149.5ms, 144.62ms,
118.63ms, 91.57ms, 70.23ms, 66.65ms, 48.59ms, 30.83ms, 28.69ms, and 24.6005ms for 50 to
500 nodesin 50 nodes intervals respectively. Overall metrics validation showed that the best
results were obtained using the specified framework. Therefore, the structure that has been
provided is sufficient for effective routing in the UN. Furthermore, it consumes a low energy rate
with minimum delay. Additionally, the optimal routing path is created quickly and within a short
distance, making it easy to transmit data.
Additionally, to justify the outcomes of the proposed DbHPR, the resultant values are compared
with the perspective UWSN routing research such as Low Clash Probability Routing (LCPR)
[34], Enhanced Metaheuristic based Multi-hop Protocol (EMMP) [35], Anchor Nodes Served
Protocol (ANSP) [36], Cooperative Delay and Security Aware Routing (CDSAR) [37] and Q-
learning based Energy Aware Protocol (QEAP) [38]. The comparison of the resultsis shown in
Table7.

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Fig.3. (a) Transmission Loss comparison (b) Path creation time
Table 7. Overall comparison statistics
Methods Throughput
(Kbps)
PDR (%) Energy usage
(J)
Network
Lifespan (sec)
Delay
(ms)
LCPR 178.8 78.56 1805.7 759.76 526
EMMP 192 83.75 1584.2 955.64 467
ANSP 203 87.62 1286.6 1034.45 423.8
CDSAR 248.5 90.45 903.5 1112.7 387.6
QEAP 283.9 92.67 835.46 1287.6 286
Proposed 338 97.5 691.4 1492.25 134
The proposed DbHPR validated a very efficient outcome compared to the prevailing UWSN
routing methods. Therefore, the presented approach is very efficient for Communication in the
UWSN. Using the DbHPR, UWSN can be more reliable, efficient, and versatile. To verify the
Selection of dove optimization over other optimization algorithms, The hello routing is hybrid
with other optimizations such as Whale Optimization Algorithm-Based Hello Routing (WOA-
HR), Ant Colony Optimization-Based Hello Routing (ACO-HR), Particle Swarm Optimization-
Based Hello Routing (PSO-HR), Cuckoo Optimization Algorithm-Based Hello Routing (COA-
HR), and Zebra Optimization Algorithm-Based HelloRouting (ZOA-HR) and its performance is
evaluated is displayed in Table8.
Table 8. Comparison with existing models
METHODS Throughput
(Kbps)
Network
lifetime (%)
PDR (%) Delay (ms)
WOA-HR 124 65.321 72.4 310
ACO-HR 210 70.892 78.6 275
PSO-HR 181 76.134 82.9 240
COA-HR 214 81.502 87.3 200
ZOA-HR 163 88.021 76.1 170
Proposed
DbHRP
338 94.814 97.5 134
The Dove-based Hello Routing Protocol (DbHRP) demonstrated superior performance compared
to existing optimization techniques in several key metrics, including throughput, network

69
longevity, Packet Delivery Ratio (PDR), and latency. It surpassed WOA-HR, ACO-HR, PSO-
HR, COA-HR, and ZOA-HR, achieving a throughput of 338 Kbps and extending the network's
lifespan by 94.814%. Additionally, DbHRP recorded a PDR of 97.5% and a reduced delay of 134
ms, outperforming its counterparts. These findings underscore DbHRP's exceptional capabilities
in enhancing network efficiency. Dove optimization is well suited for UWSNs due to its energy
efficiency, low latency, and adaptive routing capabilities. The DbHRP protocol identifies the
shortest and most reliable routes, reducing transmission delays and optimizing energy
consumption. It prioritizes target nodes to facilitate efficient data transmission, ensuring
robustness in challenging underwater environments. Compared to conventional models, it
significantly increases stability, reduces congestion, and increases overall network performance.
4.3. Discussion
DbHRP is a potential candidate for UWSN real-world deployments. DbHRP provides energy-
efficient and adaptive, which is well suited for underwater environment monitoring. DbHRP's
energy-efficient route selection prevents extended network lifetime. It also offers a low-latency
and dependable data transmission mechanism for real-time detection of tectonic activities to avert
disasters. DbHRP's adaptive routing method guarantees continuous data transmission even in the
event of dynamic topological changes created by ocean currents. DbHRP's adaptability makes it a
feasible option for real-world UWSN applications, providing reliable, energy-efficient, and
adaptive underwater Communication.
5. CONCLUSION
The balance between the energy usage and network lifetime of the UWSN nodes is currently
broad research for several human benefits applications. So, this article presented a novel DbHPR
to accomplish energy-efficient routing in the UWSN. The sensor nodes are set up in the
MATLABsoftware, and the node's position at each deep water level is estimated. Next, the
Dove's fitness function of the protocol is activated to create a shorter path by selecting the
optimal forwarding nodes. Furthermore, the node's status is estimated based on Dove's satiety
degree testing,and the transmission is done according to the priority request from the target node
if the node is unavailable. Hence, the performance is validated and compared. The designed
approach attained a higher rate of 338 Kbps throughput and a lower delay rate of 134ms. Also,
the PDR rate is increased to 97.5% with a maximum network lifetime; the PDR rate is improved
by 5% to the existing protocols; also, the throughput of the system is significantly increased by
39 Kbps; additionally, the energy consumption rate is 691.4 Jwhich is reduced approximately by
15% than the perspective protocols. The Selection of optimal nodes in DbHPR efficiently
reduced the energy usage and improved the throughput and PDR. Thus, the designed model
successfully performed efficient routing in the UWSN.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
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AUTHORS
Prof. Vinayprasad M S is currently working as an Assistant Professor in the
Department of Electronics and Communication Engineering, JSS Science and
Technology, Mysuru. He completed his M. Tech in Networking and Internet
Engineering from SJCE in 2014. He is currently pursuing his Ph.D. at JSS Science and
Technology University. His research area of interest includes Networking, Storage
Networking, Communication and Internet of Things.
Dr. Jayaram M N, Associate Professor at the Sri Jayachamarajendra College
Engineering, he has teaching experience of more than 31 years in the areas of mobile
and optical Communication. He obtained his PhD in University of Mysore and ME in
IISC, Bangalore and participated in several high profile conferences. His research area
of interest includes Underground Communication, Microwave and antennas, RF and
Optical Communication.

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