RPLUW/M: Enhanced RPL on the Internet of Underwater Things

Abstract

With the widespread use of the Internet of Things, underwater control and monitoring systems for purposes such as ocean data sampling, natural disaster prevention, underwater surveillance, submarine exploration, and the like have become a popular and challenging topic in computers. So far, various topology control and routing solutions have been proposed for these networks. However, as technology expands and applications grow, so does the need for a stable underwater communication platform. On the other hand, underwater communication is associated with challenges such as node mobility, long propagation delays, low bandwidth, limited resources, and high error rates. In this research, for the first time, a topology control platform based on the RPL tree is modelled by applying its structural changes underwater. The proposed RPLUW methods in the case of RPLUWM fixed nodes are introduced to support the mobility of nodes underwater. Flexible objective functions, utilisation of decision-making systems, and application of control schedules in these methods have increased network life, reduced overhead, and increased node efficiency. The simulation results of the proposed method, in comparison with recent methods in this field, show an increase in network efficiency.

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Article Not peer-reviewed version
RPLUW/M: Enhanced RPL on the
Internet of Underwater Things
MohammadHossein Homaei *
Posted Date: 17 July 2024
doi: 10.20944/preprints202407.1402.v1
Keywords: Internet of Underwater Things; Routing; RPL tree; Mobility; Decision Systems; Network Lifetime
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Article
RPLUW/M: Enhanced RPL on the Internet of
Underwater Things
MohammadHossein Homaei
Department, Universidad de Extremadura, School of Technology, Av/ Universidad S/N, Caceres, 10003, Extremadura, Spain;
mhomaein@alumnos.unex.es
Abstract: With the widespread use of the Internet of Things, underwater control and monitoring systems
for purposes such as ocean data sampling, natural disaster prevention, underwater surveillance, submarine
exploration, and the like have become a popular and challenging topic in computers. So far, various topology
control and routing solutions have been proposed for these networks. However, as technology expands and
applications grow, so does the need for a stable underwater communication platform. On the other hand,
underwater communication is associated with challenges such as node mobility, long propagation delays, low
bandwidth, limited resources, and high error rates. In this research, for the first time, a topology control platform
based on the RPL tree is modelled by applying its structural changes underwater. The proposed RPLUW methods
in the case of RPLUWM fixed nodes are introduced to support the mobility of nodes underwater. Flexible objective
functions, utilisation of decision-making systems, and application of control schedules in these methods have
increased network life, reduced overhead and increased node efficiency. The simulation results of the proposed
method, in comparison with recent methods in this field, show an increase in network efficiency.
Keywords: Internet of Underwater Things; Routing; RPL tree; Mobility; Decision Systems; Network Lifetime
1. Introduction
With the increasing use of Internet of Things (IoT) applications, underwater acoustic sensor
networks have become an important part of this technology in marine science for researchers and
marine-related industries. Nowadays, with the integration of telecommunication and computing
platforms, this issue is also recognised as part of the more comprehensive underwater IoT problem
for stationary and mobile sensors/actuators and underwater robots. Underwater monitoring in the
seas and oceans is important due to their different military, environmental, and industrial applications
[
1
]. Previously, underwater communications focused mainly on physical layer communications
and signal processing issues, and there was little networking discussion. Since applications such
as underwater environment monitoring are performed on a large scale, expanding the underwater
network is inevitable [
2
]. Many of these sensors (sonar, optical instruments, laser, magnetic, etc.)
are placed underwater to carry out the monitoring process. Expanding the monitoring environment
requires properly analysing sensor output and networking [
3
]. Most underwater ecosystems are
high-risk environments; therefore, the limited resources of the underwater sensor network require
performance reliability and stability more than a conventional sensor network.
Routing protocols in computer and telecommunication networks are essential to network perfor-
mance [
4
]. Proposed protocols for underwater sensor networks can be divided into two general parts:
location-based and location-independent routing protocols. We know that water currents and sea
creatures move randomly; therefore, location-based routing protocols are unsuitable for underwater
environments. On the other hand, using the GPS global positioning system in an underwater environ-
ment is inadequate. In underwater wireless sensor networks, the nodes are often battery-powered, and
it is impossible to recharge the battery; therefore, routing protocols must be optimised regarding power
consumption to communicate between sensor nodes. These protocols must be able to store energy and
consume it reasonably in exchange for error-free communication and data transmission [
5
,
6
]. On the
other hand, when the sensors collect the required information, they must send it to the water level’s
base station. Transmitting information from sensor nodes to the base station is very expensive in terms
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from any ideas, methods, instructions, or products referred to in the content.
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of energy consumption; hence, energy consumption is one of the vital factors in designing routing
protocols for underwater wireless sensor networks. There are many limitations to the underwater
environment, and some of the most critical issues to consider when designing underwater sensor
network routing protocols and the IoUT platform include the following [7]:
•
Energy issue: Energy is a limitation of the underwater sensor network, as batteries do not have
solar energy to charge and are not easily replaced; Therefore, routing protocols should consider
energy saving as a key element because the node is dead after the energy runs out and may cause
the project to fail.
•
Load Balancing: An optimal routing protocol uses network resources fairly and equitably. This
approach can prevent the occurrence of Bottlenecks or Hotspots. Also, in case of such incidents,
action should be taken to resolve the issue as soon as possible [3].
•
Underwater Location: One of the important features that all routing protocols in the underwater
network suffer from is the lack of GPS location information for nodes and their neighbours [
4
,
8
].
Knowing the location of nodes and neighbours reduces routing tables, reduces neighbour-finding
efforts, and prevents loops and wandering packages in the network. Unfortunately, GPS was
associated with errors in shallow water (less than 4 meters) and is unusable in deep water [
9
,
10
].
In the underwater network, the nodes use only one Z-axis, known as the depth gauge, and are
equipped with it. A routing protocol with only the depth of the node from the water level and
the neighbour’s list should act to transfer the node information to the base station at the water
level, which is an unresolved challenge.
•
Node mobility and instability of the fluid environment: The instability of the nodes underwater
due to environmental factors such as hot and cold water currents, collisions of underwater
organisms, and fluid waves is apparent [
11
]. Since the sensor nodes are connected to the seabed
by chains and are suspended between the seabed and the sea surface, they are constantly moving.
The node’s mobility causes the list of neighbours, the path is chosen, and the path to be explored
and repaired to undergo fundamental changes.
•
Lack of a fault detection system: If a failure or underwater network configuration problem occurs,
it is not detected before retrieving and aggregating network data. This process may easily lead to
the complete failure of the monitoring mission [10].
•
Lack of real-time monitoring: Recorded data are unavailable at the base station until collection
and processing. This process may occur several hours after each sampling [5].
•
Impossibility of instantaneous system configuration: Interaction between coastal control sys-
tems and monitoring commands is impossible in real-time. This prevents the adaptive set of
commands, and it is impossible to configure the system after a specific event [8].
According to the above-mentioned issues, there is a need to develop underwater network protocols
for real-time monitoring of ocean basins. Due to the many environmental changes in the underwater
environment, efficient protocols must be designed to meet the needs of this stressful environment [
4
,
7
].
The specific features of the underwater communication channel, such as limited bandwidth capacity
and long delays, require new, reliable, and efficient data communication protocols. Therefore, in this
paper, for the first time, we have adapted the RPL protocol developed in recent years by the IETF
Group for the Internet of Things and LLN networks on the ground to the underwater environment
[
12
]. For this purpose, our changes have been in the physical layer, the data layer, the network layer,
and the transmission layer. Also, by adding the mobility of nodes in this protocol, it is possible to
make simulations that are very close to reality.
The second part of this article is divided as follows: In the second part, we provide an overview
of routing methods and algorithms in the underwater sensor network. In Section 3, we propose an
optimized RPLUW routing protocol with mobility support capability to address some of the challenges
and limitations mentioned in Section 2. The fourth section introduces the system model and evaluates
the proposed method compared to other mixed methods. Finally, the fifth section is dedicated to the
conclusion and proposal of future work.
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2. Related works
2.1. Underwater sensor routing without location
The purpose of creating an underwater sensor network is to sample the physical parameters of the
environment and form a graph to transfer data to the central node. Therefore, several methods have
been introduced to manage data transfer. It is impossible to know the location of network nodes in the
underwater environment due to the lack of GPS signals, and the location of each node is constantly
changing due to environmental factors. In Table ??, we have examined models that operate on graph
formation, routing, and data transmission without knowing the node’s location.
Table 1. Abbreviations and Acronyms/ Variables and Definitions
Variable Definition Variable Definition
u, v Node in graph DPP DODAG Preferred
Parent
TTemperature in
degrees Celsius DRL DODAG Root List
S Set of nodes d Depth in meters
B Border routers f Frequency
N Noise ¯
kConstant K is degree
of graph
p Parent node p’ Alternative parent
node
DODAG
Destination Oriented
Directed Acyclic
Graph
Root Root node of graph
(Sink)
εShipping factor (0–1) DIO DAG Information
Object
w Wind speed DAO Destination
Advertisement Object
P Power of signal DIS DODAG Information
Solicitation
CC Capacity of channel DAO-Ack Acknowledgement
for DAO
DT Delay time IC Inconsistency
C Consistency Imin Minimum interval
DTproc Processing delay time Imax Maximum interval
DTqueue Queuing delay time ND Neighbour Discovery
DTtrans Transition delay time NS Neighbour
Solicitation
ρConstant factor NA Neighbour
Advertisement
αAbsorption RS Router Solicitation
Trx Transition time RA
Router Advertisement
r Radios LtLinkage Timer
RNumber hops of Root
node MtMobility timer
J Root node in graph RtResponse Timer
OF Objective Function M Alive nodes
G Graph τPredetermined
lifetime
V Set of vertices E Set of edges
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Table 2. QoS-aware Underwater IoT Routing Protocols (Part 1)
Year/Ref Aim/Strategy Strengths Parameters
2020 [13]
Clustered
geographic-opportunistic
routing protocol
(C-GCo-DRAR) for
UWSNs. Aims to address
challenges like high
propagation delay and
energy constraints
through clustering and
depth-based topology
adjustments.
Demonstrated superior
performance in packet
delivery, energy efficiency
(EE), and reduced delays
via Aquasim simulator.
Utilizes energy levels for
cluster head election and
depth adjustment for
void recovery.
Packet Delivery
Ratio(PDR), EC, E2E
Dealy.
2020 [14]
Energy-efficient routing
in IoT-enabled UWSNs
for smart cities using
"Underwater (ACH)²"
(U-(ACH)²). Incorporates
depth considerations to
optimize energy use
across varied deployment
scenarios.
Outperforms DBR and
EEDBR in packet delivery
rates, energy usage, and
network lifetime (NL),
promising for smart city
applications.
EC, PDR, NL.
2020 [15]
Game-Theoretic Routing
Protocol (GTRP) for 3-D
Underwater Acoustic
Sensor Networks
(UASNs). Utilizes a
strategic game with Nash
equilibrium for packet
forwarding, minimizing
broadcasts for node
degree estimation.
Shows enhanced packet
delivery, reduced delay,
and EE in Aqua-Sim
simulations. Addresses
latency, mobility, and
bandwidth challenges
effectively.
PDR, E2E delay, EC.
2020 [16]
Distributed Multiagent
Reinforcement Learning
(DMARL) protocol for
Underwater Optical
Wireless Sensor Networks
(UOWSNs). Focuses on
dynamic topologies and
energy optimization
through distributed
decision-making.
Improved energy usage,
PDR, and load
distribution validated
through simulations.
Demonstrates
adaptability and
efficiency in UOWSNs.
EC, PDR, load
distribution.
2020 [17]
Fuzzy Logic
Cluster-Based Energy
Efficient Routing Protocol
(FLCEER) for UASNs.
Implements multi-layer
clustering and fuzzy logic
for efficient routing and
UCH election.
Enhances EE, packet
delivery, throughput, and
NL, outperforming
MLCEE, DBR, and
EEDBR in simulations.
EE, PDR, throughput,
nNL.
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Table 2. Cont.
Year/Ref Aim/Strategy Strengths Parameters
2020 [18]
Sleep-Scheduling Oil
Detection Routing
Protocol for UWSNs in
smart oceans. Integrates
IoT for energy-efficient oil
spill detection using a 2D
network architecture and
sleep scheduling.
Extends NL and
improves detection
efficiency, focusing on
environmental
monitoring and
protection. Demonstrates
energy conservation in
simulations.
Energy conservation,
detection efficiency,
NL.
2020 [19]
FFRP introduces a
self-learning dynamic
firefly mating
optimization for efficient
and reliable data routing
in IoUT.
Superior packet delivery
ratio, lower latency and
EC, enhancing network
throughput.
Potential complexity
in real-world
deployment due to
the bio-inspired,
computation-
intensive
optimization process.
2020 [20]
Stochastic modelling of
opportunistic routing for
IoUT, leveraging
programmable physical
layers and multi-modal
communication.
Improved data delivery
rates through innovative
candidate-set selection,
integrating acoustic
modem and node
selection.
Increased Energy
Consumption
trade-off, requiring
efficient energy
management
strategies for practical
application.
2020 [21]
Hybrid optimization
routing for AUVs in IoUT,
focusing on EE and
effective data collection
via A-ANTD and TARD
phases.
Reduces energy usage,
improves data delivery
efficiency, and enhances
network performance for
smart ocean applications.
The complexity of
coordinating AUVs
and sensor nodes
might limit scalability
and adaptability in
diverse aquatic
environments.
2020 [22]
A novel
Power-Controlled
Routing (PCR) protocol
for IoUT that dynamically
adjusts transmission
power based on
environmental
conditions.
Improves energy use and
data delivery rates
through dynamic power
control and opportunistic
routing.
Complex adjustment
algorithms may
increase
computational
overhead.
2021 [23]
Utilizes Intelligent Data
Analytics (IDA) for
Optimized Energy
Planning (OEP) in IoUT,
enhancing data
transmission efficiency
and energy optimization.
Significant increase in
packet delivery rate and
latency and energy
expenditure reductions.
The dual-stage
programming
framework could be
complex to
implement and
manage in real time.
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Table 2. Cont.
Year/Ref Aim/Strategy Strengths Parameters
2021 [24]
Discusses green energy
harvesting and
energy-efficient routing
for IoUT, exploring
sustainable and
renewable energy
sources.
Focuses on sustainability
and tapping into
unexplored energy
resources, potentially
reducing dependency on
traditional power sources.
May require
substantial initial
investment and
infrastructure for
energy harvesting
technologies.
2021 [25]
Introduces an
Adaptive-Location-Based
Routing Protocol
(UA-RPL) for UASNs,
focusing on optimizing
packet forwarding in
three-dimensional spaces.
Enhanced network
throughput and PDRs,
reduced EC and
communication delays.
The protocol’s
efficiency could
diminish in extremely
dense or highly
dynamic underwater
environments.
2021 [26]
Examines demur and
routing protocols in
UWSNs for IoUT
applications, aiming to
support smart city
initiatives.
Highlights the potential
of IoUT in environmental
monitoring, underwater
exploration, and disaster
prevention.
Specific challenges
and disadvantages
related to the
implementation in
smart cities are not
detailed.
Table 3. QoS-aware Underwater IoT Routing Protocols (Part 2)
Year/Ref Aim/Strategy Strengths Parameters
2021 [27]
Energy-efficient routing
in IoT-based UWSN using
the Bald Eagle Search
(BES) algorithm.
Emulates bald eagle
hunting behaviour for
optimizing routing,
comprising initialization,
construction, and data
transmission phases for
effective energy use and
path efficiency.
Demonstrates superior
performance in EC,
average residual energy,
and NL over existing
algorithms, addressing
critical issues of E2E
delay, EC, and reliable
data delivery.
EC, average residual
energy, NL, PDR, E2E
(E2E) delay.
2021 [28]
To enhance reliability,
reduce delay, and
improve EE in UASN
using RLOR. / Merges
opportunistic routing
with reinforcement
learning for dynamic
node selection.
Demonstrated superior
performance in reliability,
low delay, and EE.
Innovative recovery
mechanism for void
navigation.
Complexity of
implementing
reinforcement
learning algorithms in
real-time underwater
environments.
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Table 3. Cont.
Year/Ref Aim/Strategy Strengths Parameters
2021 [29]
Address energy
consumption and void
avoidance in UASNs with
QL-EEVARP. / Uses
Q-learning for dynamic,
energy-efficient path
selection and void
avoidance.
Achieves better PDR and
enhanced EE; adaptive void
recovery enhances network
performance.
Scalability issues in
larger networks; the
complexity of
Q-learning algorithm
implementation.
2021 [30]
Improve IoUT data
dissemination by mitigating
void zones. / EVA
framework focuses on
preemptive void
identification and intelligent
routing.
Reduced energy
consumption, extended NL,
improved packet delivery,
and reduced latency.
Advanced algorithms
for void detection and
navigation may increase
system complexity.
2021 [31]
Optimize underwater
communication in IoUT with
dynamic path adjustment. /
ROBINA uses
Path-Adjustment and
path-loss models to maintain
data flow in aquatic
conditions.
Improved packet
transmission, reduced
transmission, and path loss;
adaptively managed
underwater routing.
Deployment complexity
in variable
environments due to
intricate path
adjustment mechanisms.
2021 [32]
Facilitate reliable and
energy-efficient UIoT
communication. / ELW-CFR
employs proactive routing
with layering and watchman
nodes for collision-free
communication.
Low E2E delay and high
PDR; address void hole
challenge effectively.
The layering model and
watchman node reliance
may complicate
implementation.
2022 [33]
Enhance energy efficiency in
UWSNs through optimized
power control. / Introduces
a power-controlled routing
protocol that dynamically
adjusts TPL based on
various factors.
Significant improvements in
data delivery rates and
network longevity;
optimizes energy usage.
Potential complexity in
dynamic power
adjustment and
monitoring for effective
implementation.
2022 [34]
Enhance communication
efficiency in underwater IoT
with FBR./ Evaluate FBR
performance across different
angles to optimize resource
use and packet delivery.
Narrower FBR angles led to
better performance metrics,
including energy
conservation and reduced
buffer strain.
Configuration of
optimal FBR angles is
critical and may not fit
all operational scenarios
in underwater IoT.
2022 [35]
Optimize energy efficiency
in UWSNs.
/Metaheuristic-based
clustering with Routing
Protocol employing CEPOC
for clustering and
MHR-GOA for routing.
Notable improvements in
energy efficiency and
network lifespan; effective
load balancing in data
transmission.
Complex algorithm
integration may
challenge real-time
applicability and
scalability in diverse
underwater conditions.
2022 [36]
Enhance underwater IoUT
communication. /
Cooperative Routing
Protocol based on
Q-Learning for hybrid
optical-acoustic networks,
optimizing connectivity and
energy use.
Improved network
connectivity, lifetime, and
efficiency; reduced packet
loss and E2E delay.
Deployment complexity
due to hybrid
optical-acoustic
communication needs
and the learning-based
routing decision process.
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Table 3. Cont.
Year/Ref Aim/Strategy Strengths Parameters
2022 [37]
Improve UWSNs’ energy
efficiency and network
longevity. / Cooperative-
Communication Based
Underwater Layered
Routing, integrating
cooperative communication
with hierarchical clustering.
Extended NL, improved
throughput and packet
delivery; effective energy
consumption balance.
The intricate clustering
and cooperative
communication
mechanisms may
complicate protocol
deployment.
2022 [38]
Enhance energy efficiency
and data transmission in
UWSNs. / Energy
Optimization using Swarm
Intelligence (EORO)
protocol, employing
EFF-PSO for optimal
forwarder node selection.
Superior throughput, EC,
and latency metrics;
improved PDR.
Complexity of swarm
intelligence algorithms
might increase
computational overhead
and affect real-time
performance.
2022 [39]
Mitigate signal transmission
challenges in UWSNs. /
Utilizes IoT and SNR
analysis with OSDM
modulation and improved
channel estimation for
efficient signal transmission.
Enhanced communication
efficiency with improved
SNR, reduced BER and
minimized MSE.
The complexity of
advanced modulation
techniques and channel
estimation may limit
adaptability to all
underwater conditions.
2022 [40]
Extend network longevity
and improve IoT WSN
connectivity. / ESEERP
optimizes CH selection
using a Sail Fish Optimizer
(SFO) for efficient route
selection.
Achieves significant
improvements in network
longevity, energy utilization,
and PDR.
The optimization
technique’s complexity
could impact the
protocol’s scalability
and adaptability to
varying network sizes.
2022 [41]
Optimize underwater IoUT
communication. /
Sector-based opportunistic
routing (SectOR) integrates
optical and acoustic
communications to enhance
packet delivery.
Significant improvements in
underwater networks’ EE,
delay reduction, and PDR.
Challenges in balancing
communication range
and beamwidth for
optimal performance
across underwater
environments.
Table 4. QoS-aware Underwater IoT Routing Protocols (Part 3)
Year/Ref Aim/Strategy Strengths Parameters
2022 [42]
Evaluate the efficacy of
various IoUT routing
protocols. /
Simulation-based analysis
of cluster-based and
chain-based routing
protocols to enhance
efficient data transfer in
UWSNs.
Comprehensive
comparison revealed
cluster-based protocols
show varied efficiency,
offering insights into
effective routing
strategies in IoUT.
Requires extensive
simulations to capture
real-world
complexities and
underwater
conditions accurately.
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Table 4. Cont.
Year/Ref Aim/Strategy Strengths Parameters
2022 [43]
Optimize data transfer
performance in UWSNs.
/Performance analysis of
diverse routing protocols
like AODV, DSR, and DYMO
under varying conditions
using QualNet simulator.
Identified protocols with
lower power consumption
and higher energy efficiency,
crucial for improving UWSN
performance.
Simulation-based
approach may not fully
replicate underwater
environments’ unique
physical and chemical
challenges.
2022 [44]
Enhance energy efficiency
and information
transmission in IoT-UWSNs.
/ Introduces adaptable
power networking methods
using Fastest Route Fist
(FRF) and a business unit
method for effective routing.
Proposed methods
significantly reduce Electric
Cost (EC) and Transmission
Drop Rates (RTDR) with
reasonable latency.
Complexity of
implementing and
tuning the proposed
adaptable power
networking methods in
diverse underwater
scenarios.
2022 [45]
Enhance routing efficiency
and energy conservation in
UWSNs. / Introduces the
Adaptive Clustering Routing
Protocol (ACRP) with
multi-agent reinforcement
learning for adaptive cluster
head selection, reducing
communication overhead
and EC.
Demonstrated improved
routing efficiency, energy
utilization, and network
lifespan compared to
existing methods. Efficiently
mitigates hotspot issues
through balanced EC.
Implementation
complexity due to
reinforcement learning
integration; requires
rigorous tuning to
effectively adapt to
diverse underwater
environments.
2022 [46]
Analyze UWSN
performance using diverse
routing protocols. /Evaluate
protocols like AODV and
DSR using simulations to
explore their efficacy under
various network conditions.
Provided comparative
insights into protocol
performance, identifying
those with potential for
UWSN enhancements.
Simulation-based
evaluations may not
fully capture the
operational complexities
of real-world
underwater
environments.
2022 [47]
Enhance IoUT
communication efficiency
with DSPR. / Utilizes angle
of arrival and depth
information for directional
data forwarding and
selective power control to
optimize energy use.
Demonstrated energy
efficiency, achieving better
performance in delivery
ratios and network
longevity.
May require
sophisticated hardware
to accurately determine
the angle of arrival and
implement selective
power control
effectively.
2022 [48]
Review energy optimization
techniques in UIoT. /
Evaluates various energy
optimization strategies,
including wireless power
transfer and artificial
intelligence, to enhance
network efficiency.
Highlighted potential
efficiencies from
mixed-medium
communication and smarter
battery management,
identifying research gaps
and future directions.
The breadth of the
review may necessitate
further empirical testing
to validate the
effectiveness of
proposed optimizations
in real-world
applications.
2022 [49]
Address energy
optimization in UWSNs. /
Proposes an energy-efficient
routing protocol leveraging
genetic algorithms for
optimal routing and data
fusion techniques for energy
conservation.
Showed improvements in
PDR and EC, offering a
viable solution for extending
NL.
The complexity of the
genetic algorithm and
data fusion process may
impact the scalability
and real-time
applicability of the
protocol.
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Table 4. Cont.
Year/Ref Aim/Strategy Strengths Parameters
2022 [50]
Adaptive
Transmission-based
Geographic and
Opportunistic Routing
(ATGOR) protocol for UIoTs.
Introduces a two-part
strategy focusing on cube
selection for transmission
reduction and reliable node
selection for optimal data
forwarding. Incorporates
Mobility Aware ATGOR
(MA-ATGOR) to predict
neighbour locations to avoid
voids and ensure packet
delivery.
Enhances packet delivery
reliability, reduces void
nodes, and optimizes energy
consumption per packet in
harsh underwater
environments.
PDR, the number of
void nodes, and EC per
packet.
2022 [51]
Stochastic
Optimization-Aided
Energy-Efficient Information
Collection for IoUT. Utilizes
heterogeneous AUVs for
data collection, optimizing
energy efficiency with
Particle Swarm
Optimization (PSO) and
Lyapunov optimization
considering AUV trajectory,
resource allocation, and Age
of Information (AoI).
Offers a holistic approach to
optimizing energy usage
and AoI in IoUT networks.
Successfully balances energy
consumption with system
stability and information
freshness through adaptive
planning and optimization
strategies.
EC, queue lengths, Age
of Information (AoI).
2022 [52]
Energy-Efficient
Guiding-Network-Based
Routing (EEGNBR) for
UWSNs. Establishes a
guiding network to direct
packets via the shortest route
with minimal hops,
incorporating a concurrent
working mechanism for
reduced forwarding delay
and energy conservation.
Reduces network delay
significantly while ensuring
reliable routing and EE.
Innovative use of guiding
network and concurrent
data forwarding mechanism.
Network delay, EC,
PDR, network service
life.
2022 [53]
Underwater Adaptive RPL
(UA-RPL) for IoUT. Modifies
RPL’s Objective Function
(OF) and DODAG
construction to improve NL
and reliability in underwater
conditions. Introduces
dynamic trickle algorithm to
reduce control message
overhead.
Enhances communication
reliability and EE in
underwater IoUT networks.
Successfully mitigates the
impact of underwater noise
and balances energy
consumption across nodes.
PDR, throughput,
control overhead, delay,
EC.
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Table 5. QoS-aware Underwater IoT Routing Protocols (Part 4)
Year/Ref Aim/Strategy Strengths Parameters
2023 [54]
Opportunity Routing
protocol based on Density
Peaks Clustering (ORDP)
for IoUT. Utilizes network
clustering with Density
Peaks Clustering (DPC),
entropy weight-TOPSIS
for cluster head election,
and opportunistic data
transmission.
Innovatively combines
DPC with entropy
weight-TOPSIS for
efficient cluster head
selection, significantly
improving EE,
transmission latency, and
PDR.
EC, average
transmission latency,
PDR.
2023 [55]
Delay and Reliability
Aware Routing (DRAR)
and Cooperative DRAR
(Co-DRAR) protocols for
UWSNs. Aims to enhance
reliability with strategies
for reducing delay and
managing power
consumption through
regional network division
and strategic sink node
positioning.
Introduces cooperative
transmission to improve
data packet quality,
effectively reducing E2E
delay, balancing energy
consumption, and
ensuring reliable
communication.
EC, E2E delay, PDR,
dead nodes, packet
drop ratio, alive
nodes.
2023 [56]
Neighboring-Based
Energy-Efficient Routing
Protocol (NBEER) for
UWSNs. It focuses on
Neighbor Head Node
Selection (NHNS),
cooperative load
balancing, and
performance
enhancement
mechanisms.
Excels in reducing energy
consumption and latency
while improving packet
delivery ratio, NL, and
total received packets
through efficient
neighbor-based routing
and data forwarding.
EC, E2E delay, PDR,
alive nodes, number
of packets received.
2023 [57]
Designing an
Underwater-Internet of
Things (U-IoT) network
model for marine life
monitoring. Utilizes
autonomous underwater
vehicles (AUVs) and
surface sinks for efficient
data transfer using
acoustic waves and RF
techniques.
Addresses the overfishing
problem by providing a
system that supports
effective marine life
monitoring and data
management,
demonstrating efficient
administration through
the proposed network
model.
Efficiency of data
transfer, management
of marine resources,
impact on
overfishing.
2023 [58]
Shared Underwater
Acoustic Communication
Layer Scheme (SUACL)
for enhancing UAC
technology development
and evaluation. Enables
remote operation of
communication units for
data transmission and
reception.
Offers a flexible and
adaptable platform for
underwater acoustic
research, significantly
improving
communication efficiency
with better SNR, lower
BER, and minimized
MSE.
Signal to Noise Ratio
(SNR), Bit Error Rate
(BER), Mean Square
Error (MSE).
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Table 5. Cont.
Year/Ref Aim/Strategy Strengths Parameters
2023 [59]
Opportunistic Hybrid
Routing Protocol (RAOH)
for Acoustic-Radio
Cooperative Networks
(ARCNet). Introduces a
hybrid routing strategy that
utilizes surface radio links
for neighbor discovery and
combines opportunistic and
on-demand routing for
efficient data forwarding.
Enhances packet delivery
success, reduces route
establishment times, and
improves EE by leveraging
the dual advantages of
acoustic and radio
communication.
Energy usage, average
transmission latency,
PDRs.
2023 [60]
Opportunistic
Routing-Based Reliable
Transmission Protocol
(OR-RTP) utilizing Artificial
Rabbits Optimization (ARO)
for energy-efficient routing
in UIoT networks. Focuses
on balancing energy
consumption and PDR
through meta-heuristic relay
selection.
Offers an adaptive relay
selection mechanism for
dynamic underwater
environments, improving
network longevity and
reliability while reducing
overall energy consumption.
EC, PDR, throughput,
NL.
2023 [61]
Opportunistic Routing based
on Directional Transmission
(ORDT) for IoUT. Utilizes
directional transmission for
energy focus, improving
packet delivery rates,
minimizing latency, and
conserving energy.
Combines directional
transmission with
opportunistic routing for
targeted energy use and
enhanced packet delivery,
addressing underwater
communication’s unique
challenges.
Packet delivery success
rate, transmission
latency, energy usage.
2023 [62]
Hybrid-Coding-Aware
Routing Protocol (HCAR)
for UASNs. Introduces
interflow network coding
within a reactive and
opportunistic routing
framework to enhance
packet transmission
efficiency and network
performance.
Integrates network coding to
correct errors and optimize
transmission efficiency,
significantly reducing
transmission counts and
adapting to UASN
conditions.
EC, PDR, throughput,
NL.
2023 [63]
Member Nodes Supported
Cluster-Based Routing
Protocol (MNS-CBRP) for
UWSNs. Utilizes clustering
and leverages network
member nodes for efficient
information transfer,
optimizing energy
consumption.
Improves scalability and
data integrity through
clustering, significantly
extending the network’s
lifespan by optimizing
energy use and enhancing
data transmission reliability.
EC, PDR, throughput,
NL, energy trade-off.
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Table 5. Cont.
Year/Ref Aim/Strategy Strengths Parameters
2023 [64]
Efficient Geo-Routing-Aware
MAC Protocol (GO-MAC)
based on OFDM for UANs.
Integrates geo-routing with
OFDM, optimizing
transmission delay and
energy consumption
through a cross-layer MAC
protocol.
Reduces data collisions and
enhances EE with optimized
OFDM resource allocation
and improved next-hop
selection.
EC, PDR, throughput,
NL.
2023 [65]
Energy-Depth Aware
Channel Routing Protocol
(ED-CARP) for UWSNs in
IoUT. Focuses on relay node
selection based on Channel
State Information (CSI),
considering residual energy
and depth.
Combines energy and depth
awareness in relay selection,
optimizing energy
consumption and enhancing
data delivery efficiency.
EC, PDR, throughput,
NL, and balance
between energy used in
transmission and
reception.
Table 6. QoS-aware Underwater IoT Routing Protocols (Part 5)
Year/Ref
Aim/Strategy Strengths Parameters
2024
[66]
Machine Learning-Based Optimal Cooper-
ating Node Selection for IoUT. Employs ML
algorithms for selecting cooperating nodes
based on delay, energy, and collision rates.
Uses DDPG-SEC algorithm for
improved EE, reduced latency,
and enhanced packet delivery,
showing significant advance-
ments over traditional methods.
EC, PDR, throughput,
NL, successful transmis-
sion probability, and E2E
delay.
2024
[67]
Enhancing Energy Efficiency of Underwa-
ter Sensor Network Routing to Achieve
Reliability using a Fuzzy Logic-based Ap-
proach. Implements a clustering-based rout-
ing method utilizing fuzzy logic to optimize
energy consumption and reliability by con-
sidering factors like residual energy, dis-
tance, depth, and number of neighbors for
node selection.
Efficiently reduces energy con-
sumption and improves network
reliability. Balances traffic load
and extends the network lifespan
through dynamic clustering and
fuzzy logic.
Residual energy, Distance
to sink, Depth, Number
of neighbors, Packet
generation rate, Network
topology, Communica-
tion range.
2024
[68]
Energy-efficient routing protocol using a
hybrid metaheuristic algorithm (GSLS) for
UWSNs. Combines Global Search Algo-
rithm (GSA) and Local Search Algorithm
(LSA) for optimal routing paths.
Efficiently reduces energy con-
sumption and routing discov-
ery time by leveraging a parallel
search mechanism, significantly
improving UWSN performance.
EC, PDR, NL, algorithm
speed.
3. Network Settings
The RPL protocol is designed and developed for the WSN and LLN networks in the IoT infrastruc-
ture. Changes to the protocol structure are necessary to implement it in an underwater environment.
According to the structural differences between the wireless sensor network and the underwater
audio sensor network, these differences will be applied in different network layers. Some of the most
important adjustments are presented in sections 3.1 to 3.6. These changes are modelled in RPL code in
the NS2 environment and the Aquasim package.
3.1. Speed of Sound in Water
The measure of the speed of sound frequency in the sea environment (
ζ
) is related to three primary
factors called water temperature (
T
), salinity (
ψ
), and depth (
d
). According to Mackenzie’s formula
[
69
], a relatively accurate estimate of the speed of sound frequency underwater can be obtained by
Equation 1, [70].
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ζ=(1449 +4.591T)−(5.304 ×10−2T2)
+ (2.374 ×10−4T3) + (1.34(ψ−35))
+ (1.63 ×10−2d) + (1.675 ×10−7d2)
+ (1.025 ×10−2T(ψ−35)) −(7.139 ×10−3Td3)
(1)
3.2. Underwater Frequency Link Quality Criterion
Factors affecting the quality of the underwater connection include noise due to water turbulence
Nt
and noise due to the movement of vessels with a coefficient
ϵ
denoted by
Ns
. Also,
Nw
is the noise
caused by the waves generated by the wind at a speed of m/s, and finally,
Nth
is the ambient thermal
noise. The spectral density power of a frequency in an underwater environment is calculated through
Equations 2to 6[71]:
N(f) = Nt(f) + Ns(f) + Nw(f) + Nth (f)(2)
10 log Nt(f) = 17 −30 log f(3)
10 log Ns(f) = 40 +20(e−0.5) + 26 log f−60 log(f+0.03)(4)
10 log Nw(f) = 50 +7.5√w+20 log f−40 log(f+0.4)(5)
10 log Nth (f) = −15 +20 log f(6)
In these equations,
ϵ
is equal to the coefficient of the noise of the shipping factor, which takes
values between 0 and 1. Also,
w
equals the wind speed, which varies from 0 to 10 meters per second.
It is observed that the noise rate of the carrier frequency
N(f)
, given by Equation
??
, increases with
increasing the amount of
ϵ
and
w
in the environment. The signal-to-noise ratio, known as SNR, is
calculated by Equation 7[72]:
SN R =P
N(f)(7)
In this relation,
P
is equal to the power of the transmitted signal in a narrow frequency band.
According to Shannon’s theory, the communication channel’s capacity C, is given by Equation 8.
C(d,f) = Blog2(1+SNR(d,f)) (8)
According to network logic, as the noise rate in the media environment increases, the available
capacity of the channel decreases. This is represented by
N(f)
in Equation 7, which is based on the
link stability rate. Thus,
(d
,
f)
can be used to gauge both link quality and as a benchmark for detecting
acceptable levels of noise communication in underwater networks.
3.3. Delay Time Model
Underwater sensor nodes use sound waves to transmit information, and the speed of underwater
sound is about 1500 meters per second. This value is several times less than radio signals in the out-of-
water environment [
73
]. Therefore, the signal propagation delay in the underwater environment is
significant. Total underwater delays include processing time
DTproc
, queue delay
DTqueue
, propagation
delay DTpro p , and transmission delay DTtrans .
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DTpro p =
HC
∑
i=1
di
vp(9)
DTtrans =HCL
Rbit (10)
In relations 9and 10, the parameter
di
equals distance,
vp
equals signal propagation speed, and
HC
is the hop-count. The processing and queue latency is much less than the propagation delay, and
often in calculations, they deal with only the two propagation and transmission delays [
74
]. Finally,
the total latency of a packet from origin to destination, passing through
n
hops, is calculated by
Equation 11.
DTall =
N
∑
i=1
(DTproc +DTqueue +DTpro p +DTtrans )(11)
3.4. Calculating Node Depth
Each underwater sensor node is equipped with a depth gauge unit to obtain the amount of
pressure applied to the node, as indicated by
P
. The value of
P
is calculated from the equation
P=ρg
where
ρ
and
g
are two computational constant values. The depth difference between nodes A and B,
denoted by
∆d
, is calculated by Equation 12. Here,
dA
represents the depth of the sender node, and
dB
the depth of the sender node’s neighbor.
∆d=dA−dB=PA−PB
ρg(12)
3.5. Frequency Attenuation or Absorption Model
The
α
parameter, measured in dB/km, is used to quantify the rate of frequency absorption.
Typically, the power of sound frequency underwater diminishes by about 21% per kilometer. The
depth of water plays a significant role in determining the attenuation rate [75].
αd=α0(1−1.93 ×10−5d)(13)
Equation 13 describes the degree of attenuation at a certain depth
d
, where
α0
is the attenuation at
the surface level. As the equation indicates, the absorption rate decreases in deeper water.
α=0.106 f1×f2
f2
1+f2e
pH−8
e0.56 +0.521+T
43
×(Ψ
25)f2×f2
f2
2+f2e−d
6+4.910−4f2e−(T
27 +d
17 )(14)
Where
pH
represents the water’s acidity,
Ψ
its salinity,
T
the temperature in Celsius, and
d
the depth in kilometers. The frequencies
f1
and
f2
relate to the water’s salinity and temperature,
respectively, as defined in Equations 15 and 16.
f1=0.78rΨ
35eT
26 (15)
f2=42eT
17 (16)
Equation 14, for simplicity in calculations, assumes a temperature of 4
◦C
, a depth of 1000m, a
salinity of 30, and a pH level of 8.
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3.6. Structure of Control Packets
The RPL network uses four control packets to form, repair, and maintain the network graph.
Some of these messages are sent periodically, while others are dispatched as needed in response to
network errors or instability. These messages have been redefined and adapted for the underwater
sensor network within the proposed RPLUW protocol.
• The DIO packet is broadcasted by parents to the children to promote DODAG.
• The DAO packet is unicast from membership by the child to the parent.
•
The DAO-Ack packet is also unicast from the parent to the child to confirm the membership of
the child node in the parent list.
•
If the node is orphaned or located in an area where the network instability has reached the
threshold, the DIS packet will be broadcasted by the parentless node in the network.
Figure 1. The structure of control packets in RPLUW and RPLUWM
4. The Proposed Rpluw Method
4.1. The System Model
RPL is an IPv6 distance vector routing protocol that operates on the physical layers and IEEE
802.15.4 data link and is suitable for sensor networks with low power sources and very limited
bandwidth. With the advancement of technology, more suitable facilities and communication platforms
have been provided for these networks than in the previous decade. In this paper, we present a new
development of RPL called RPLUW, which is the motionless version, and RPLUWM, the version that
supports mobility in underwater sensor networks. To provide these methods, significant structural
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changes were necessary to make the RPL compatible with the underwater environment. The significant
changes to the proposed method with the standard version of RPL are shown in Figure ?? in gray.
The following are some definitions and details of the proposed protocol.
•
The rank
R(u
,
j)
is a measure that defines the logical distance of node
u
as a subset of
N(u∈N)
from the root of the network graph
J
, by the objective function (OF) of the RPL protocol. The
R
rate will often increase as it moves away from the graph’s root. In the underwater network, this
criterion can be defined according to the application, such as the amount of node depth from the
water surface or as a combination of step and node depth.
•
The Preferred Parent DODAG (DPP): Suppose
u
is a node of
G
,
N(u)
its one-hop neighbors, and
DPP(u
,
j)
a finite subset of
N(u)
. For each node
v∈N(u)
, we have
v∈DPP(u
,
j)
, if it has the
lowest rank up to the root
R(u
,
j)
specified DODAG
j∈B
. In the improved version of RPL, we
will have several preferred parents for each node, and the best parent will be chosen by node
u
when the node sends the packet.
•
DODAG (DRL) root list: Each node
v∈N(u)
must broadcast DIO packets. The root location of
DODAG should be included in these packets. Thus,
DRL(u)
is a list of DODAG root locations
stored at each node uin the network.
The proposed Underwater-RPL method forms and supports at least one DODAG per well node.
This protocol calculates the upward and downward paths independently and according to a process to
benefit from them if placed in the network objective function. In the RPL method, the base of each
node is bound to have a preferred parent in its list, which directs the generated data and data received
from its offspring to the root by the preferred parent. In this method, the network is modelled as a
G(V
,
E)
directionless link graph. A constant
k
is defined where
γ=
1 is the preferred parent number
of each node in the network. This
γ=
1 method in the basic RPL protocol suffers from some injustice
and imbalance in traffic transfer because each parent node can have
n≤N−
1 members in the worst
case. This causes imbalance, queue inefficiency, and congestion in the network. Therefore, according
to the new RPL models, we use a multi-path mechanism in the proposed method. That is, for each
node u∈G(V,E), the value of k≥1. To better understand this multi-path mechanism, suppose that,
according to Figure2, several nodes are randomly distributed in the underwater environment. The
sink node is located on the water surface and is connected to the water floor by anchors and chains
like the network nodes.
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Figure 2. RPL network graph in underwater conditions, unrestricted and with
γ=
1 child node limit.
Figure 3shows that we will face the following graph by removing the limit value of the number
of parents for the child node. The number of available parents for each child node is
k≥
1, which
increases the number of network paths and reduces network failure.
Figure 3. Demonstration of a network graph without parental degree constraints
4.2. Detection and Management of Displacement in Underwater RPL
Displacement is one of the main reasons for graph incompatibility and instability in RPL. Each
node in the network is moved or removed from its neighbor’s list, and its neighbor list changes
periodically. In general, there are two main methods for detecting and managing displacement:
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•
Trickle timer algorithm: A dynamic schedule based on network stability/instability rate prevents
repetitive sending of control packets and keeps the network graph’s repair mechanism dynamic.
The trickle timer of the t-period is limited by the interval
[Imin
,
Imax]
, where
Imin
is the minimum
interval defined in milliseconds by the base value of 2 (e.g., 2
12 =
4096 ms) and
Imax =Imin ×
2
Idoubling
is used to limit the number of times
Imin
can be doubled. Assuming
Idoubling =
4, the
maximum interval is calculated as
Imax =
4096
×
2
4=
65536ms. The trickle timer algorithm
can update the topology in a short time. When the incompatibilities of 13 nodes are reached,
i.e.,
IC ≥k
, it sets the value of
t
equal to
Imin
and updates the tree. If the network is stable, i.e.,
IC <k
, the value
I
double until the constant value
Imax
is reached. If the network nodes are
moved, the number of DIS packets in the network increases, and this causes the
IC
to converge
to the value of
k
, and the scheduler returns to
Imin
. As the timing of DIO packet periods in the
network decreases, the overhead network will increase, and the network will be involved in
resolving incompatibilities.
•
IPv6 Neighbour discovery method: RPL can use neighbor discovery to detect environmental
changes with an optimal version of ND. ND makes it possible to detect neighbor inaccessibility
and discover new neighbors, which is supported by four ICMPv6 control packets:
–
Neighbour Solicitation (NS): Checks the node’s availability by checking the neighbor’s
MAC address.
–
Neighbour Advertisement (NA): Responds to NS packets, is also sent intermittently to
announce link changes.
–
Router Solicitation (RS): The host node (mobile node in the proposed model) solicits infor-
mation from the router.
–
Router Advertisement (RA): The router periodically sends its presence packet and graph
and link parameter information to respond to the RS packet.
4.3. The RPLUWM Method Timers
We used several timers to increase underwater network communications’ stability, availability,
and reliability. These timers are designed to control the process of reducing link instability and node
inaccessibility to the network’s parent.
•
Linkage timer (
Lt
): To increase the response of network nodes, they must periodically monitor
the activity of the communication channel. An
Lt
timer is installed for this process, the sequence
value of which is determined by a trickle timer (
Imax
). During this process, the moving node
listens to the channel and monitors the input packets from the parent. After the
Lt
timer expires,
the discovery phase begins if the parent exchanges reach zero. Also, the link timer is reset upon
receipt of any packet from the parent (such as trickle DIO, unicast DIO, or data packet).
•
Mobility timer (
Mt
): After receiving the unicast DIO packet, the quality of the Average received
signal strength indicator (ARSSI) is evaluated to evaluate the reliability of the link. As the ARSSI
rate decreases, the moving node begins to explore to find a new parent. The node data generation
rate sets this timer during network setup.
•
Response timer (
Rt
): If the parent detects disconnection and receives a DIS packet from the
neighbors, it must send a DIO packet at certain times. Selecting the wrong response moment
may cause data packets to collide, activating the exploration phase. The parent node monitors
the other children’s packets, estimates the packets’ sequence and response time, and sends a DIO
packet outside.
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Figure 4. RPLUW network scheduling steps
4.4. RPLUWM network graph construction method
The network graph formation steps in the RPLUWM protocol are created using the proposed
objective function. In the basic RPL protocol, the hop or rank of the nodes is usually the criterion for
graph formation. However, in the proposed method, we have used a combination of three parameters:
hops to root, node depth from the water level, and ARSSI rate of the interface link to form the initial
graph (Algorithm 1).
4.5. Routing in the RPLUWM network
Routing is the main pillar of data transfer in the Internet of Things. This means that without a
logical mechanism that is aware of the resources and capacity of the network, data transfer is associated
with a high resource consumption overhead or, in some applications, seems impossible. In the Internet
of Things, multi-path mechanisms are used to improve network reliability and minimize bottlenecks
or hotspots. In this section, we propose a quality service-aware approach in the underwater mobile
sensor network that can achieve acceptable efficiency with minimal consumption of network resources.
To this end, an optimal multi-criteria decision system [
74
] proposed in the previous work for the RPL
network in the offshore environment with changes and adaptation to the underwater network has
been proposed for routing. This design can combine all the effective parameters according to the
network’s needs and determine each parent’s final value and priority for the nodes. After forming the
network graph in the first step, each node has a list in its memory, the values of which will be updated
periodically. This list is assigned to the status of available parents for the node whose maximum
number of parents in this list is limited to
¯
k
= 4; Because, according to tests, more than a fixed number
of parents per node increases congestion, hidden terminals, and requires more complex calculations.
Table 7shows this list of parents.
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Algorithm 1 RPLUWM Protocol Network Graph Formation
1: Input: Underwater network environment with nodes, including a sink node
2: Output: Network graph formed based on an objective function
3: The sink node broadcasts a DIO packet to all nodes in the network
4: for each node receiving a DIO packet do
5: Send a DAO packet back to the sink node with node’s depth and link ARSSI rate
6: end for
7: Sink node sends DAO-Ack packets to first-level nodes due to unlimited one-hop children
8: for each parent node do
9: Update hop value, current depth, and ARSSI rate in the DIO packet
10: Resend the updated DIO packet to other nodes
11: end for
12: for each node receiving updated DIO packets do
13: Compile and update a list of potential parents based on received values
14: end for
15: for each child node do
16: Select one or more parents from the list and send a DAO request with depth and ARSSI
17: end for
18: for each parent node receiving DAO packets do
19: if link quality level to the child node is above the threshold then
20: Send a DAO-Ack packet to the child node
21: end if
22: end for
23: Child nodes receiving DAO-Ack packets proceed to form graph, continue to the next hop
24: Repeat the process until the entire network graph is formed
Table 7. An example of a list of parents of each node with their quantitative values
Parents /
Parameters PiPi+1Pi+2P(i≤¯
k)
Hop-Count(n)3 3 3 3
Remaining
Energy(j)167.5 183.2 179 138.8
ARSSI ¯
Ri¯
Ri+1¯
Ri+2¯
Ri≤¯
k
Delay Time(ms) DTiDTi+1DTi+2DTi≤¯
k
ETX ϵiϵi+1ϵi+2ϵi≤¯
k
Link’s PDR (%) 0.78 0.85 0.76 0.88
Depth(m) 129.8 141.2 155.4 117.4
In the section on prioritizing parent nodes, a multi-criteria decision-making system was used for
children to make optimal selections 2. In this approach, each parent node is weighted based on the
parameters of the hop, energy, ARSSI rate, latency, ETX, link delivery rate, and depth in the decision
system and will obtain its final value in combination. In the next matrices, the steps for calculating the
decision system are given. For example:
A=






1a12 a13 ··· a17
1/a12 1a23 · · · a27
.
.
..
.
..
.
.....
.
.
1/a17 1/a27 1/a37 · · · 1






w
w

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Algorithm 2 Multi-Criteria Decision Making AHP Matrix
1: Input: Pairwise comparison matrix Aof size n×n
2: Output: Priority vector ωof size n
3: Initialize sum vector Sof size nto zeros
4: Initialize priority vector ωof size nto zeros
5: for j=1 to ndo ▷Calculate column sums
6: columnSum[j]←∑n
i=1A[i][j]
7: end for
8: for i=1 to ndo ▷Normalize the matrix
9: for j=1 to ndo
10: A[i][j]←A[i][j]/columnSum[j]
11: end for
12: end for
13: for i=1 to ndo ▷Calculate priority vector
14: S[i]←∑n
j=1A[i][j]
15: ω[i]←S[i]/n
16: end for
17: return ω









x1··· ··· xnS
x11··· ··· ω1
ωn∑n
i=1ω1,i
.
.
..
.
.....
.
..
.
.
.
.
..
.
.....
.
..
.
.
xnωn
ω1··· ··· 1∑n
j=1ωi,j









w
w













x1··· ··· xnS
x11··· ··· ω1
ωn∑n
i=1ω1,i
.
.
..
.
.....
.
..
.
.
.
.
..
.
.....
.
..
.
.
xnωn
ω1··· ··· 1∑n
i=1ωn,i
S∑n
j=1ωj,1 ··· ··· ∑n
j=1ωj,n∑n
i,j=1ωi,j












w
w












x1··· ··· xnS
x11
1ω1
ω2··· ω1
ωn
∑n
j=1ω1,j
n
.
.
.ω2
ω1
1
1··· ω2
ωn
∑n
j=1ω2,j
n
.
.
..
.
..
.
.....
.
..
.
.
xn∑n
i=1ωi,1
ωsomet hing · · · · · · 1
1
∑n
j=1ωn,j
n











w
w

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







∑n
j=1ω1,j
n
∑n
j=1ω2,j
n
.
.
.
∑n
j=1ωn,j
n








⇒







ω1
·
·
·
ωn







⇒







0.48
0.24
0.16
0.08
0.04







Finally, the values for each parent node in the children list will be updated, and if the nodes move,
the ARSSI value will be updated separately at a higher sampling rate. Equations (1) and (2) decide
how to calculate the system.
The value of each node can be calculated as follows:
NodeValue(k=1 . . . n) =
n
∑
k=1
(Paramk×ωk)(17)
The MADM selection criterion is given by:
MADM selection =max(NodeValuek)(18)
Section 5 simulates and evaluates the proposed method against recent methods.
5. System Model and Simulation
To evaluate and compare the proposed method with the recent methods of EEGNBR [
43
], Co-
DRAR [
55
], OR-RTP [
60
], and UA-RPL [
53
], NS simulation version 2.31 and Aquasim package version
2 were used. The beam widths of each underwater sensor node varied between 0 and 360 degrees.
The radio range of the sensor node was 150 meters and the radio range of the well was 200 meters.
Network nodes were randomly distributed in the underwater environment. This section evaluates all
the details and simulation conditions in Table 8.
Table 8. Network simulation conditions
Parameters Value
Network topology Random position
Deployment area 1000 x 500 m3
Initial node energy 50 J
Initial sink energy 50kJ
Number of nodes 50, 100, 200
Nodes mobility 1 m/s–5 m/s
Mobility model Random mobility
Percentage of Mobile Nodes 40%
Cost of long transmission 1.3 W
Cost of short transmission 0.8 W
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Table 8. Cont.
Parameters Value
Cost of reception 0.7 W
Idle power 0.008 W
Data aggregation power 0.22 W
Communication range of ASN 150 m
Acoustic transmission range(sink) 200 m
Spreading values 1.3
Frequency 30.5 kHz
Channel Underwater channel
Maximum Bandwidth 30 kbps
DIO packet size 50 bytes
DAO packet size 4 bytes
DAO-ACK packet size 4 bytes
DIS packet size 4 byte
Packet generation rate λ=0.1 ∼0.2 pkt/s
Memory size 12 MB
Sink position Surface (500 x 500 x 0)
Antenna Omni-directional
Simulation time 600
Iterations 10
Number of Channels 11 (30.511, 30.518, ... 30.581) kHz
*Bellhop is used to calculate the path loss between each node
in a given location.
5.1. Testing the Effect of Environment on Network Energy Consumption Rate
To evaluate the energy consumption rate, it is not enough to pay attention to the hardware and
software energy model of the underwater sensor nodes. Instead, attention should be paid to the water
temperature, ambient salinity rate, and node depth for network exchanges. In Figure 5, we model
the rate of increase in node energy consumption under temperatures ranging from 11.5
◦
C to 14.5
◦
C.
Due to the lack of temperature changes in deep water environments, energy consumption will not
change significantly. Also, in Figure 6, the changes in energy consumption increase are modeled for
two shallow and deep water environments with varying degrees of salinity, and the obtained results
are plotted.
Figure 5. The rate of increase in node energy consumption under varying temperatures.
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Figure 6. The changes in energy consumption increase for varying degrees of salinity in shallow and
deep water environments.
5.2. Network Lifetime Test
The viability of underwater networks hinges critically on the network’s lifespan, as the exhaustion
of node energy denotes the cessation of connectivity and, subsequently, the surveillance operation’s
failure. Standard benchmarks for a network’s longevity include the times to failure of the first and
median nodes, with imbalanced energy consumption accelerating these events. The RPLUW technique
and its mobile adaptation, RPLUWM, have demonstrated enhanced outcomes by intelligently em-
ploying network graphs and prioritizing quality of service in routing. Moreover, the dynamic nature
of the RPLUWM graph structure adeptly manages the inherent mobility of underwater networks,
ensuring more rapid convergence than alternative methods. This swift convergence mitigates node
disconnection, futile attempts, and energy depletion, prolonging network life and bolstering energy
efficiency.
Illustrated in Figure 7, the RPLUW method and its counterparts show distinct advantages in the
longevity of the first and median network nodes. Specifically, at a traffic load of
λ=
0.1 pkt/s, the
RPLUW method extends the life of the first node by 2.36%, 10.00%, 8.00%, and 9.45% longer than
EEGNBR, Co-DRAR, OR-RTP, and UA-RPL methods, respectively. When the traffic load is at
λ=
0.2
pkt/s, the RPLUW’s advantage is further pronounced, outperforming EEGNBR, Co-DRAR, OR-RTP,
and UA-RPL by 4.67%, 13.08%, 14.95%, and 11.21% respectively. Regarding the median node lifespan,
under a
λ=
0.1 pkt/s traffic load, RPLUW outlasts EEGNBR, Co-DRAR, OR-RTP, and UA-RPL by
margins of 2.28%, 6.05%, 2.97%, and 6.96% correspondingly. At a higher traffic load of
λ=
0.2 pkt/s,
RPLUW still maintains its lead, with lesser depletion rates than EEGNBR, Co-DRAR, OR-RTP, and
UA-RPL by 6.33%, 5.28%, 9.50%, and 7.69% respectively.
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Figure 7. Comparative analysis of the lifespan of the first and median nodes in underwater networks,
showing the performance of the RPLUW method against other methods.
In the dynamic realm of underwater networks, where mobility is inherent to many nodes, assess-
ing the efficacy of routing protocols becomes critical. To this end, a refined iteration of the suggested
method, RPLUWM, was rigorously tested against conventional methods such as EEGNBR, Co-DRAR,
OR-RTP, and UA-RPL. The RPLUWM variant was designed to accommodate scenarios where around
40% of the network nodes are mobile, a condition reflective of real-world applications and essential for
a fair assessment of the method’s robustness compared to others.
The core principle of RPLUWM, which integrates flexible scheduling and minimizes unproductive
route explorations, significantly extended the operational lifetime of network nodes. When scrutinized
at a traffic intensity of
λ=
0.1 pkt/s, the RPLUWM approach surpassed its contemporaries, showing
remarkable improvements in the longevity of the first node—EEGNBR by 9.51%, Co-DRAR by 14.40%,
OR-RTP by 22.11%, and UA-RPL by 14.14%. For the median node, the increments in lifespan were
equally noteworthy—EEGNBR by 11.03%, Co-DRAR by 8.58%, OR-RTP by 23.12%, and UA-RPL by
10.72%.
The benefits of RPLUWM were not just confined to lower traffic conditions. As the load escalated
to
λ=
0.2 pkt/s, the method’s superiority persisted, with delays in the death time of the first node by
12.78% for EEGNBR, 9.09% for Co-DRAR, 18.18% for OR-RTP, and 8.24% for UA-RPL. Meanwhile,
the advantage for the median node was maintained with a lesser reduction of 6.33%, 5.28%, 9.50%,
and 7.69%, respectively. This demonstration of resilience emphasizes RPLUWM’s aptitude for energy
conservation and its capacity to maintain network integrity under varying node mobility and traffic
load conditions. Refer to Figure 8for a visual representation of these comparative results.
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Figure 8. Performance evaluation of the RPLUWM method compared with other methods, highlighting
the extended lifespan of network nodes at various traffic loads.
5.3. Average Lifetime Networl
The Average Lifetime Network(ALTN) is a pivotal metric in evaluating network sustainability.
This measure reflects on the equity of energy distribution within the grid and its consumption pattern
after 900 seconds of simulation. A primary indicator for ALTN is the delay in the time of death of
the network’s first node; the longer this delay, the more effective the method is at addressing energy
consumption hotspots and achieving balance. The ALTN is calculated according to Equation (19):
ALTN =∑N−M
i=1ti+ (M×℘)
N(19)
where
ti
represents the time of death of the
ith
node,
N
is the total number of nodes in the network,
M
is the number of nodes surviving at the end of the simulation, and
℘
is the predefined lifetime of the
network.
Figures 9and 10 illustrate the enhancements achieved by RPLUW and RPLUWM, respectively.
For a traffic input rate of
λ=
0.1 to 0.2, RPLUW has demonstrated significant improvements in
the average lifetime of nodes, outperforming EEGNBR by 5.62% to 11.25%, Co-DRAR by 6.82% to
17.11%, OR-RTP by 11.90% to 12.66%, and UA-RPL by 14.63% to 15.58%. Similarly, RPLUWM has
shown substantial superiority with mobile nodes, registering improvements of 8.97% to 23.44% over
EEGNBR, 13.33% to 16.18% over Co-DRAR, 16.44% to 19.70% over OR-RTP, and 13.33% to 27.42% over
UA-RPL. These results underscore the protocols’ effectiveness in extending operational durations and
maintaining robust network performance under varying traffic conditions.
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Figure 9. Performance comparison showing the superior average lifetime of network nodes using
RPLUW.
Figure 10. Performance comparison showing the superior average lifetime of network nodes using
RPLUWM.
5.4. Packet Delivery Ratio
In underwater Internet of Things networks, the Packet Delivery Ratio (PDR) is a critical perfor-
mance metric, reflecting the data packet delivery success rate to the intended destination. The PDR is
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mathematically represented as the ratio of packets received by the destination nodes to the number of
packets sent by the source nodes. Formally, the PDR can be expressed as:
PDR =Number of packets successfully received
Total number of packets sent ×100 (20)
As defined in Equation (20), the PDR for the proposed RPLUW and RPLUWM protocols was
meticulously evaluated under diverse conditions characterized by static and mobile nodes. The
intelligent multi-attribute decision-making approach, which synthesizes parameters such as node
depth, signal strength, energy reserves, and latency, has enabled RPLUW to exhibit remarkable
improvements in packet delivery. Specifically, in a static node environment, RPLUW achieved a PDR
enhancement of approximately 4.44% at
λ=
0.1 pkt/s and 17.65% at
λ=
0.2 pkt/s, in comparison to
existing protocols like EEGNBR, Co-DRAR, OR-RTP, and UA-RPL. These advancements are vividly
depicted in Figure 11.
Figure 11. The PDR results for static node configurations, showcasing the performance of RPLUW in
comparison to traditional protocols.
For mobile nodes, the adaptability of RPLUWM to the dynamic underwater environment fa-
cilitated PDR improvements of 14.29% and 16.67% for traffic rates of
λ=
0.1 pkt/s and
λ=
0.2
pkt/s, respectively. Despite the underwater communication challenges, this capability to maintain
high packet delivery rates is substantiated by the simulation results showcased in Figure 12. The
findings collectively underscore the superiority of the proposed methods, solidifying the efficacy of the
multi-attribute decision-making framework in enhancing the reliability of underwater sensor network
communications.
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Figure 12. The PDR results for mobile node configurations, illustrating the efficacy of RPLUWM under
dynamic conditions.
5.5. Average End-to-End Delay
The simulation results for the RPLUW protocol, and its mobile version RPLUWM, under two
different traffic rates—
λ=
0.1 and
λ=
0.2 packets per second—demonstrate significant improvements
in end-to-end delay compared to other established protocols. At a
λ
of 0.1, the RPLUW protocol exhib-
ited remarkable reductions in delay; EEGNBR, Co-DRAR, and OR-RTP experienced approximately
166.95%, 31.78%, and 13.56% higher delays, respectively, while UA-RPL showed a minimal but notable
3.81% lower delay. When the traffic rate increased to
λ=
0.2, RPLUW’s performance continued to
outpace the competition, with EEGNBR, Co-DRAR, and OR-RTP delays being higher by 184.92%,
48.02%, and 18.25%, respectively, and UA-RPL presenting a marginally improved delay of 1.19%.
For the RPLUWM protocol tailored to mobile scenarios, the efficiency becomes even more pro-
nounced. With
λ=
0.1, delays in EEGNBR, Co-DRAR, OR-RTP, and UA-RPL were 128.71%, 45.50%,
54.01%, and 40.63% higher, respectively. At a
λ
of 0.2, RPLUWM showcased a stellar performance
increase with reductions in delay compared to EEGNBR, Co-DRAR, OR-RTP, and UA-RPL by 140.86%,
75.91%, 93.12%, and 54.41%, respectively.
End-to-end latency is a vital metric for the efficiency of underwater sensor networks, reflecting
network performance in environmental monitoring tasks. The RPLUW and RPLUWM protocols’
decreased jitter and latency signify robust environmental monitoring capabilities. Specifically, RPLUW
with stationary nodes improved end-to-end latency by a minimum of 3.81% and a maximum of
166.95% across various protocols. In contrast, the mobile adaptation RPLUWM secured a minimum
improvement of 40.63% and a maximum of 140.86%. These results underscore the efficacy of the
proposed protocols’ scheduling, multi-route routing, and decision-making systems within the net-
work. This enhancement is attributed to the protocols’ consideration of critical factors in establishing
and maintaining link longevity between nodes. Figures 13 and 14 depict fixed and mobile node
environments, respectively.
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Figure 13. The superior performance of the RPLUW protocol in a fixed node environment showcases
significant delay reductions.
Figure 14. The superior performance of the RPLUWM protocol in a mobile node environment demon-
strates substantial improvements in end-to-end delay.
5.6. Network Convergence Time Test
In the realm of underwater sensor networks, achieving optimal performance across various
parameters remains a complex trade-off. Comparative analysis of different protocols in terms of energy
consumption, network lifespan, packet delivery ratio, delay, and the time to first and half-node deaths
reveals that our proposed protocols, RPLUW and RPLUWM, excel in most performance metrics (see
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32 of 40
Figures 15 and 16). They demonstrate enhanced energy efficiency, increased average network lifetime,
and superior packet delivery rates with lower overall delays. These protocols also show a delayed
occurrence of the first and half-node deaths, indicating robustness in network functionality over time.
However, these advantages come at the cost of a longer network convergence time. This trade-off
emphasizes that while RPLUW and RPLUWM protocols offer significant improvements in sustaining
network performance and reliability, they require a larger initial investment to establish network
routes. This characteristic is a strategic choice that prioritizes long-term operational benefits over
immediate network readiness, marking the protocols as particularly suited for applications where
network durability and resilience are paramount and initial setup time can be afforded.
Figure 15. Network topology convergence in Static Nodes.
Figure 16. Network topology convergence in Mobile Nodes.
6. Conclusion
As hardware technologies advance and telecommunications networks become increasingly effi-
cient, implementing IoT networks to monitor physical parameters has become more streamlined and
possible. In light of the diverse applications of the Internet of Things within aquatic environments,
there is a pressing need for efficient platforms that can extend operational life and minimize latency,
particularly in applications where timing is crucial. Our research introduced a suite of pivotal parame-
ters that contribute to establishing, upkeep, and restoring network topology through a multi-criteria
decision-making framework. Additionally, we addressed the complexities of node mobility within
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33 of 40
the network by integrating innovative trickle timers and refined algorithms for neighbour discovery.
Adopting a multi-route approach has been instrumental in diminishing node traffic and achieving a
harmonious load distribution. The employment of decision-making systems requiring minimal com-
putational resources has notably enhanced the topology management and network graph restoration
processes. Our simulation results, juxtaposed with contemporary methods, demonstrate the superior
functionality and effectiveness of our proposed RPLUW and RPLUWM protocols. Achievements such
as prolonged network longevity, improved delivery rates, and reduced end-to-end latency underscore
the successes of this study. Looking forward, we aim to tackle the intricacies of data aggregation
in underwater IoT networks by proposing a model that focuses on energy optimization and time
efficiency, rooted in the foundational RPLUW and RPLUWM strategies and bolstered by learning
algorithms. The remarkable prolongation of network node lifespans via RPLUWM signals its promise
for sustained operation in underwater monitoring endeavours, vital for environmental research, re-
source prospecting, and security activities. This innovation marks a significant leap in underwater
communications, presenting a resilient framework that plays a transformative role in the future of
marine exploration and data acquisition.
Acknowledgments: Test text for Acknowledgement.
Appendix A. Appendixes
•
Shallow Water Energy Consumption Model: This model assesses the energy consumption in
shallow waters. It is based on a linear distribution of sensor nodes featuring
N+
1 nodes where
the distance between consecutive nodes is denoted as
d
. The calculations in this model address
the transmission of packets containing
B
bits to the sink node, using either a single-hop or a
multi-hop process involving relay nodes. The model adopts a linear chain configuration of nodes,
which is considered a worst-case scenario for analysis. As illustrated in Figure 3, the propagation
of sound signals in shallow waters is modeled cylindrically, necessitating the application of
cylindrical spatial geometry for accurate calculations.
Figure A1. Cylindrical environment around each underwater sensor node.
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34 of 40
As illustrated in Figure A1, we utilize the subsequent equations to calculate the transmission
power at two distinct points, r1and r2:
P=2πr1H1(A1)
P=2πr2H2(A2)
The variable His defined as the height of the cylinder, equivalent to the depth of the node. The
rate at which transmission diminishes between r1and r2is determined by Equation A3:
TL =100 log r1
r2(A3)
Equations A1 and A2 describe the transmission power related to the radial distances
r1
and
r2
from the central axis of the cylindrical underwater environment surrounding a sensor node, as
depicted in Figure A1.
Now consider that a node located at a distance of
Nd
from the sink node needs to send
K
packets.
The required power level and energy consumption during its transfer are calculated through
Equations A4 and A5:
P=2πdH1(A4)
E=NPTxK(A5)
The parameter
d
is the distance between two nodes,
N
indicates the number of steps to the Sink,
Ttx
is the transmission time of a packet. When each node is in the process of transferring
m
packets and this transfer is a multi-hop relay mechanism, the energy consumption is equal to
Equation A12:
Etotal =NPTtxm+ (N−1)PTtxm+. . . +PTtxm
=N(N+1)PTtxm
2(A6)
However, if the sensor node wants to interact with a single-hop connection and directly with the
sink, then the power consumption of the node will be obtained from Equation A13:
P=2πr1H1(A7)
In this relation
r1
is the distance between each node and the sink. Total energy consumption in
this scenario uses Equation A8:
Etotal =PNdTtx m+P(N−1)dTtx m+. . . +PdTtxm
=mT
N
∑
i=1
P(i·d)(A8)
•
Deep Water Energy Consumption Model: Unlike shallow waters, the propagation of sound
signals in deep water is spherical. In a network scenario, as in the case of shallow water, the
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35 of 40
power P is generated at the source and propagated in all directions as a sphere, as shown in
Figure A2.
Figure A2. Signal propagation model in deep water.
Thus, if we consider the points
r1
and
r2
as shallow water, then the signal strength is obtained
from Equation A9:
P=4πr2
1I1=4πr2
2I2(A9)
The transfer loss rate between points r1and r2is also calculated from Equation A10:
TL =10 log I1
I2=20 log r2(A10)
The power and energy consumption levels for a network topology when the node is at a distance
Nd
from the sink and wants to transmit data in several hops will be calculated from Equations A11
and A12:
P=4πd2I1(A11)
E=NPTxm(A12)
Whenever in the linear distribution, each node needs to transfer
K
packets, the energy consump-
tion in the multi-hop relay scenario is obtained by Equation A12. If the node wants to transmit in a
single hop, the received signal must be generated with the power resulting from Equation A13:
P=4πr2
1I1(A13)
r1
is the distance between the node and the sink. However, unlike in the case of shallow water,
the general drop or rate of loss of a data transmission is a combination of spherical propagation,
signal attenuation, and environmental incompatibility. Therefore, the amount of signal attenuation is
calculated from Equation A14:
TL =20 log r+αr×10−3+A(A14)
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Short Biography of Author
[ ]
MOHAMMADHOSSEIN HOMAEI (M’19) was born in Hamedan, Iran. He
obtained his B.Sc. in Information Technology (Networking) from the University
of Applied Science and Technology, Hamedan, Iran, in 2014 and his M.Sc. from
Islamic Azad University, Malayer, Iran, in 2017. He is pursuing his Ph.D. at
Universidad de Extremadura, Spain, where his prolific research has amassed
over 100 citations.
Since December 2019, Mr. Homaei has been affiliated with Óbuda University,
Hungary, initially as a Visiting Researcher delving into the Internet of Things and
Big Data. His tenure at Óbuda University seamlessly extended into a research
collaboration with J. Selye University, Slovakia, focusing on Cybersecurity from
January 2020. His research voyage then led him to the National Yunlin University
of Science and Technology, Taiwan, where he served as a Scientific Researcher
exploring IoT and Open-AI from January to September 2021. His latest role was
at the Universidade da Beira Interior, Portugal, in the Assisted Living Comput-
ing and Telecommunications Laboratory (ALLab), from June 2023 to January
2024, where he engaged in cutting-edge projects on digital twins and machine
learning. He is the author of ten scholarly articles and holds three patents, high-
lighting his diverse research interests in Digital Twins, Cybersecurity, Wireless
Communications, and IoT.
An active IEEE member, Mr. Homaei has carved a niche for himself with notable
contributions to Digitalization, the Industrial Internet of Things (IIoT), Informa-
tion Security Management, and Environmental Monitoring. His substantial body
of work profoundly influences the technological and cybersecurity landscape. .
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those
of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s)
disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or
products referred to in the content.
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