INTRODUCTION
Introduction
Underwater communication networks are designed to support various applications, including offshore exploration, oceanographic data collection, pollution monitoring, disaster prevention, tactical surveillance, and assisted navigation These networks will facilitate the operation of multiple autonomous or unmanned underwater vehicles (AUVs, UUVs) equipped with sensors to gather scientific data and explore natural undersea resources during collaborative monitoring missions.
The Underwater Acoustic Communication Network (UACN) is a crucial technology for various underwater applications, consisting of static and mobile nodes that communicate through wireless acoustic wave channels A gateway node, equipped with an acoustic modem, facilitates communication among these nodes and establishes a data link to connect the UACN to ships, command centers, or other installations.
In underwater environments, static and mobile nodes, including sensor nodes and vehicles, are deployed for collaborative monitoring and scientific data collection These nodes gather underwater information and transmit it to a gateway node via relays or directly, which then forwards the data to shipboard processing centers or other networks To accomplish this, the sensor nodes and vehicles form an autonomous underwater network that adapts to the unique characteristics of the underwater environment.
Underwater communication networks (UACNs) share similarities with terrestrial wireless networks, particularly mobile ad hoc networks (MANETs), as they operate without any established infrastructure These networks often depend on node mobility and utilize self-powered nodes However, UACNs possess unique characteristics due to the properties of sound propagation in seawater, where the speed of sound waves is significantly altered.
Sound waves travel at a speed of 1500 meters per second (1500 m/s), but their bandwidth is quite limited, typically ranging from 100 to 2500 bits per second (bps), with an average of around 1000 bps Due to their significant propagation delay, UACNs are classified as Delay Tolerant Networks (DTN).
One significant limitation of underwater data collection by sensors is the reliance on non-rechargeable batteries, which restrict their operational lifespan To enhance energy efficiency in Underwater Acoustic Sensor Networks (UACNs), technologies such as Dynamic Voltage Scaling (DVS) and Dynamic Power Management (DPM) are employed DVS adjusts the CPU's operating voltage and clock frequency based on system load to minimize idle time and reduce overall energy consumption Meanwhile, DPM analyzes real-time system resources and load status to dynamically modify system configurations through the Power Management unit, effectively lowering total power usage while meeting performance constraints.
Figure 1- 1 Underwater communication topology network
The applications for underwater acoustic communication networks
Underwater seismic monitoring plays a crucial role in oil extraction from underwater fields, utilizing advanced sensor networks to ensure effective oversight Frequent seismic assessments are essential for optimizing oil recovery processes Recent studies focusing on temporal variations in ocean conditions, known as "4-D seismic," are instrumental in driving timely interventions and evaluating overall field performance.
Autonomous Underwater Vehicles (AUVs), like the Odyssey-class, form networks that enable cooperative adaptive sampling of the three-dimensional coastal ocean environment The Monterey Bay field experiment showcased how integrating advanced AUVs with sophisticated ocean models enhances the prediction and observation of underwater characteristics, highlighting the benefits of these innovative technologies in oceanography.
Underwater sensor networks (UWSNs) play a crucial role in environmental monitoring by enabling pollution detection, including biological, nuclear, and chemical contaminants They can analyze water quality in situ by monitoring chemical substances such as estrogen-type hormones, insecticides, and antibiotics in various water bodies like streams, lakes, and ocean bays Additionally, UWSNs facilitate the monitoring of winds and ocean currents, aiding in climate change detection, weather forecasting, and understanding human impacts on marine ecosystems Biological monitoring applications, such as tracking microorganisms and fish populations, further enhance their utility A study by Zhang et al highlights the design of a UWSN capable of detecting extreme temperature gradients, which are known to promote the proliferation of specific marine microorganisms.
Underwater sensor networks play a crucial role in disaster prevention by monitoring seismic activity in the ocean from remote locations These advanced systems can analyze the impacts of submarine earthquakes, also known as seaquakes, and deliver timely tsunami warnings to coastal communities.
Underwater Autonomous Cooperative Networks (UACN) enhance the functionality of groups of underwater robots and vehicles, enabling them to coordinate adaptive sensing for monitoring biological phenomena and detecting chemical leaks, as well as for equipment monitoring applications.
Assisted navigation utilizing underwater acoustic networks enables effective bathymetry profiling, identification of mooring positions, detection of submerged wrecks, and assessment of seabed hazards, including shallow water shoals and dangerous rocks.
Fixed underwater sensors and autonomous underwater vehicles work together to enhance reconnaissance, surveillance, intrusion detection, and targeting A notable development is a 3D underwater sensor network designed for tactical surveillance, capable of classifying and detecting submarines and divers through data collected from mechanical delivery vehicles, magnetic, acoustic microsensors, and radiation detection.
Problem statement
In the past two decades, underwater acoustic communication networks (UACNs) have gained significant attention due to their critical applications in both commercial and military sectors Unlike terrestrial radio communications that utilize electromagnetic waves, UACNs rely on acoustic waves for data transmission in underwater environments This unique setting presents challenges such as multipath propagation, limited bandwidth, refractive medium properties, large Doppler shifts, and severe fading, all of which negatively affect latency, capacity, and throughput To effectively design and deploy UACNs, it is essential to develop accurate channel models that reflect the underwater environment A comprehensive understanding of these acoustic channel characteristics is crucial for creating efficient underwater transmission systems that align with real marine conditions, ultimately leading to improved performance.
Energy conservation is crucial in Underwater Acoustic Communication Networks (UACNs) due to battery-powered nodes, which are challenging to recharge or replace in underwater settings Additionally, these networks face issues such as network dynamics, high error rates, and significant propagation delays inherent to underwater acoustic channels Notably, the power needed for receiving signals in underwater acoustic networks is approximately 100 times lower than that required for transmitting, highlighting the importance of optimizing energy usage in these systems.
The design of scalable, robust, and energy-efficient routing protocols for underwater acoustic networks (UACNs) is a critical research area Existing data forwarding protocols from terrestrial networks are unsuitable due to their design for stationary environments Key challenges include communication range, memory limitations, costs, computational power, and, most importantly, the limited battery life of individual nodes As nodes deplete their power over time, the operational coverage area diminishes, emphasizing the need for energy-efficient protocols to extend node lifespan In energy-constrained underwater environments, innovative solutions are essential to enhance node longevity and network performance.
The media access control (MAC) mechanism poses significant challenges for underwater acoustic communication networks (UACNs) Traditional methods often replicate terrestrial wireless solutions, utilizing contention-based control protocols that involve the exchange of control packets to reserve the medium However, this approach can hinder network performance due to the substantial propagation delay associated with underwater communication, where sound travels at approximately 1.5 x 10^3 m/s, compared to the speed of light in terrestrial networks at around 3 x 10^8 m/s—highlighting a five-order magnitude difference As a result, many MAC protocols designed for terrestrial environments are either ineffective or exhibit low efficiency in underwater settings, underscoring the need for dedicated MAC protocol designs tailored for UACNs A well-designed MAC protocol is essential for enabling nodes to share the common channel and to minimize packet collisions, which is particularly crucial in high-latency, low-quality channels like those found in underwater environments, ultimately impacting overall network efficiency.
Scope and objectives
This thesis aims to model underwater acoustic channels, develop MAC protocols tailored for these environments, and implement energy-saving technologies to minimize energy consumption in underwater acoustic sensor nodes The specific objectives include enhancing the efficiency and sustainability of underwater communication systems.
Simulate the characteristics of underwater acoustic channel
Simulate P-Aloha, CSMA/CA and MACAW protocols in underwater acoustic channel environment
Propose a new MAC protocol based on the FAMA protocol
Propose a new MACA-based MAC protocol called MACA-C protocol
Propose an energy-saving technology to reduce the energy consumption of the underwater acoustic sensor nodes.
Organization
This thesis consists of eight chapters, a brief summary of chapter-by-chapter is presented as follows:
Chapter 2 introduces fundamentals of underwater acoustic channel
Chapter 3 employs the simulation tool OPNET to simulate the characteristics of underwater acoustic channel, with respect to Propagation-Delay-Stage, Receiver-Power-Stage and Background-Noise-State, by using the models: the MacKenzie’s speed equation for the speed of sound; Thorp’s, Schulkin and Marsh’s, Francois and Garrison’s models for propagation loss; and four sources for ambient noise are the turbulence, shipping, wind driven waves and thermal noise
Chapter 4 presents a simulation and comparison of the P-Aloha, CSMA/CA, and MACAW MAC protocols in an underwater acoustic channel environment The objective is to evaluate the performance of these MAC protocols through simulation results, specifically focusing on their effectiveness in underwater settings.
Chapter 5 introduces a new MAC protocol tailored for Unmanned Aerial Communication Networks (UACN), featuring modifications to the Floor Acquisition Multiple Access (FAMA) protocol This revised approach, termed Slotted FAMA-CTS, incorporates a time slot specifically for Clear-To-Send (CTS) packets Additionally, an energy optimization strategy for data transmission is proposed, aiming to enhance throughput rates, reduce packet collisions caused by hidden node propagation delays, and achieve energy savings.
Chapter 6 introduces the MACA-C protocol, a novel MAC protocol based on MACA that effectively addresses the long propagation delay issues in underwater acoustic channels by integrating data and control packets.
Chapter 7 discusses a study focused on traditional Dynamic Voltage Scaling (DVS) energy-saving technology and introduces a novel approach that combines DVS with Dynamic Power Management (DPM) to enhance energy efficiency in underwater acoustic sensor nodes The effectiveness of this combined DVS-DPM strategy is evaluated and analyzed through simulation, highlighting its potential to significantly reduce energy consumption in these sensor systems.
Chapter 8, the final chapter, concludes this thesis and recommends future study.
FUNDAMENTALS OF UNDERWATER ACOUSTIC CHANNEL
Physical characteristic of the sea
Electromagnetic waves, such as light and radar, quickly attenuate in salt water, limiting their range, while sound waves experience much less attenuation, making them the primary method for underwater sensing and communication However, the lack of comprehensive oceanographic data on the significant spatial and temporal variability of ocean conditions has posed challenges for underwater acousticians in predicting sound propagation To improve predictions, it is essential to gain a deeper understanding of ocean characteristics that traditional oceanographic instruments measure inadequately and at high costs.
Figure 2- 1 Spatial and temporal scales of physical and biological parameters and processes in the sea
Figure 2- 2 Marine biological pyramid with diameter of equivalent spherical volume of the plants or animals
Acoustical oceanographers leverage the intricate nature of sound propagation to gain insights into the ocean, achieving significant advancements in the field This innovative science enables the identification and quantification of both physical and biological entities, including microbubbles, zooplankton, fish, and marine mammals Additionally, it facilitates remote measurements of phenomena such as distant rainfall, sea surface roughness, deep-sea topography, and the characteristics of internal waves and ocean eddies spanning hundreds of kilometers Visual aids like marine biological pyramids illustrate the vast diversity of ocean life and the scale of various oceanic features, all of which can be effectively measured through acoustical oceanography techniques.
Sound propagation
Acoustical oceanography began in 1912 after the Titanic disaster, which prompted the use of sound to detect underwater objects L R Richardson filed two patent applications in the UK for a method to locate large underwater objects using echo from compressional waves This technique relies on the speed of sound in water and the travel time of sound to calculate distances to scatterers By 1935, acoustical sounding was employed to measure ocean depth and locate fish schools Recently, advancements have shown that the physical characteristics of scatterers can be inferred from the statistical properties of scattered sound, enabling high-resolution imaging in turbid, optically opaque waters.
Sound is a mechanical disturbance that moves through a fluid, primarily characterized by incremental acoustic pressure that is significantly lower than the surrounding ambient pressure Additionally, sound can also be described in terms of changes in density, temperature, material displacement from equilibrium, or the transient particle velocity within the medium.
Acoustic intensity refers to the energy transmitted per unit time through a unit area The total energy of a sound pulse is determined by integrating the intensity over time and the spherical surface it traverses As illustrated in Figure 2-3, the wave front expands at two distinct radii By applying the principle of energy conservation, the energy that passes through a sphere of radius R0 is equal to that through a sphere of radius R For a pulse duration of δt, this conservation leads to a relationship between sound intensities at R0 and R, denoted as i0 and iR, respectively.
Figure 2- 3 Spherical spreading of an impulse wave front
The instantaneous intensity is i 0 at the radius R 0 and later is i R at the radius R Solving for the intensity at R, one gets
Sound intensity diminishes with distance, following the inverse square law (1/R²), due to spherical spreading In contrast, sound pressure decreases in a spherically diverging wave according to the inverse relationship (1/R) If a spherical pulse were to implode rather than explode, we would observe an increase in intensity proportional to 1/R Additionally, a rarefaction pulse, characterized by a region of lower density than the surrounding environment, can also be generated and transmitted.
A sinusoidally excited source creates a continuous wave through repeated expansion and contraction, generating condensations (above ambient density and pressure) and rarefactions (below ambient) that propagate away from the source at the speed of sound, c This behavior mirrors the disturbances caused by an impulse source At any given moment, the sinusoidal fluctuations can be visually represented, with the distance between adjacent condensations or rarefactions defining the wavelength, λ.
Figure 2-4 illustrates radiation from a tiny sinusoidal source, showcasing the pressure field at a specific moment, where dark condensations indicate a decrease in acoustic pressure with increasing distance Additionally, it presents the range-dependent pressure at that instant and the time-dependent pressure signal observed at a particular point in space.
The disturbances illustrated in Fig 2-4 emanate from a point source that is significantly smaller than the wavelength (λ) As the condensation propagates outward, the acoustic energy disperses across increasingly larger spheres, resulting in a decrease in pressure amplitude, which refers to the peak acoustic pressure of the sinusoidal wave.
Acoustic propagation in underwater environments is mathematically represented by the wave equation, which incorporates specific boundary conditions and parameters relevant to these settings As illustrated in Fig 2-5, there are approximately five distinct models used to characterize acoustic propagation underwater, including the Fast Field Program (FFP) model.
Figure 2- 5 Hierarchy of underwater acoustic models
The wave equation in an ideal fluid is derived from hydrodynamics and the adiabatic relationship between pressure and density This derivation incorporates the conservation of mass, Euler’s equation (which reflects Newton’s second law), and the adiabatic equation of state.
Figure 2-6 is used to describe the motion of a particle in a water column from where in the entire derivation of the wave equation has been done
Figure 2- 6 Schematic diagram indicating displacement of a particle from x to x + dx in water column
In deriving all the following equations the terms have been used as p g and g
[24] These two terms are defined as follows:
0 p g p p (2-3) Where p g Total pressure p 0 Static pressure p Change in pressure
2.2.1.1 Equation for conservation of mass
A sudden expansion of a small spherical source creates a disturbance in the medium, leading to an increase in local density and pressure as the surrounding medium cannot immediately accommodate the expansion In a distant region where the plane wave approximation is applicable, the variations in pressure, velocity, and acceleration of fluid particles primarily depend on the direction of propagation, referred to as x.
A x dx v - Outward Volume stream at a displacement of dx
- Outward Mass Stream at a displacement of dx v ( )
A x - Inward Volume stream at position x
- Inward Mass stream at position x
2.2.1.2 Euler’s equation (Newton’s 2 nd Law )
In fluid dynamics, the Euler equations govern inviscid flow and are named after Leonhard Euler These equations represent the conservation of mass, momentum, and energy, analogous to the Navier–Stokes equations but with zero viscosity and heat conduction While only the continuity and momentum equations were originally derived by Euler, the term "Euler equations" commonly refers to the complete set, including the energy equation.
From Fig.2-6, the Newton’s 2 nd law F ma can be written as:
dt (2-9) With v v dv dt dx t x
(2-10) dv v v dx dv v v dt t x dt dt t v x
Eq (2-6) can be re-written as p g p v v x x t v x
Eq (2-5), which is known as the equation of continuity can be written as [18]
Adiabatic processes occur in systems enclosed by thermally insulating and impermeable walls, known as adiabatic walls These processes involve the transfer of energy as work across these adiabatic boundaries.
(2-16) And for convenience we define the quantity [24]
The speed of sound in an ideal fluid, denoted as c, can be derived from the equations where ρ represents density, v indicates particle velocity, and p signifies pressure The subscript s indicates that the thermodynamic partial derivatives are calculated at constant entropy.
In oceanography, the time scale of changes is significantly longer than that of acoustic propagation, allowing us to treat the material properties, density (ρ₀) and sound speed (c²), as constant over time By applying partial derivatives to Euler's equation and the continuity equation, we can derive important relationships that enhance our understanding of fluid dynamics in oceanic environments.
For lower speeds, in Eq (2-19) we can ignore, g v x v x
in Eq (2-20) can be ignored Now, Eq’s (2-19) and (2-20) can be written as [23-24]
(2-22) Combining Eq’s (2-21) and (2-22), we get the one dimensional linear wave equation [23-24]
2-23) Extending it to three dimensional equation we get [19, 24]
The Helmholtz equation is commonly encountered in physical problems involving partial differential equations (PDEs) related to both space and time This equation, which represents the time-independent version of the original PDE, is derived through the separation of variables technique, simplifying the analysis process.
For pP x y z t( , , , ) exp(jwt), it can be obtained
In spherical coordinates the Laplacian can be expressed by
, if taken into account that P only depends upon R
Spherical wave solution of the Helmholtz Equation is given by [23-24] , exp( )
Where x y z 0 , 0 , 0 are the coordinates of an omni directional point source (pulsating sphere of small radius) [23-24] Another simple and important solution is given by plane wave, exp ( x y z )
P A j k x k y k z (2-28) Where, k x , k y and k z denote the wave numbers that satisfy,
T x y z k kk k k (2-29) With, k ( ,k k k x y , z ) T the wave number vector
2.2.3 Sound propagation in homogenous waveguide
Issues of UACN research
The issues facing UACN researchers in the following aspects: network topology, physical layer, MAC layer, Network layer, and Application layer
Underwater Acoustic Communication Networks (UACN) have distinct topologies due to the unique properties of acoustic signals and underwater channels, setting them apart from terrestrial networks Despite these differences, UACN shares fundamental design goals with ground-based networks, including ensuring reliable connectivity among acoustic nodes, minimizing energy consumption, and enhancing overall network capacity.
There are two primary types of network topologies: hierarchy mode and ad hoc mode In ad hoc mode, acoustic nodes form a self-organized peer-to-peer network, which can be categorized into point-to-point connection topology and multi-hop connection topology Point-to-point connections involve a single hop between nodes, eliminating the need for a routing protocol In contrast, hierarchy mode requires additional nodes to relay data messages from the source to the destination, necessitating a routing protocol Research indicates that multi-hop topology is more energy-efficient in terrestrial wireless radio networks, a finding that warrants further investigation in Unmanned Aerial Communication Networks (UACNs).
Figure 2-10 illustrates a hierarchical underwater network topology featuring multiple structural levels This topology allows for various deployment strategies of acoustic nodes, whether on-demand or permanent, influenced by application-specific time constraints Additionally, different topologies and data volumes can be effectively managed within an Underwater Acoustic Sensor Network (UACN).
Figure 2- 9 An example of peer-to-peer topology
Figure 2- 10 An example of Hierarchy topology
The physical layer of Underwater Acoustic Communication Networks (UACN) encompasses the essential hardware transmission technologies that define its unique underwater channel Underwater acoustic channels exhibit distinct characteristics, notably high attenuation of electromagnetic waves, with only a small portion of the long-wave band capable of penetrating this environment with relatively low loss For instance, at 122 kHz, data rates between 1-8 kbits/sec can reach distances of 6-10 meters, necessitating high transmitter power and large antennas Additionally, optical signals in underwater channels face significant absorption and scattering, requiring precise alignment of narrow laser beams for effective transmission.
Underwater acoustic networks rely on acoustic wave signals due to the unique characteristics of underwater channels, offering reliable communication over longer distances with less attenuation However, these acoustic waves face significant limitations in available bandwidth and experience increased attenuation at higher frequencies Additionally, acoustic signals travel much slower in seawater, at approximately 1500 m/s, which is significantly slower than electromagnetic waves in terrestrial environments.
The Medium Access Control (MAC) layer is essential for managing node access to the underwater acoustic channel in Underwater Acoustic Communication Networks (UACNs) It schedules each node's access to the physical medium and allocates necessary resources, facilitating the transfer of data packets between layers Due to challenges such as long propagation delays, high bit error rates, and limited bandwidth, underwater acoustic nodes must share channel resources effectively The MAC layer plays a crucial role in ensuring Quality of Service (QoS) and efficient resource allocation, while also optimizing performance based on available resources and current channel conditions Additionally, it determines the channel resources available to the physical layer and adjusts its parameters accordingly.
In communication networks with limited range, single-hop transmission effectively delivers information without the need for data relaying However, as the range increases, multi-hop transmission becomes necessary to relay messages from the source to the destination node Research indicates that multi-hop delivery is more energy-efficient than single-hop delivery in underwater acoustic networks.
In a communication network, the network layer is crucial for determining the optimal path from a source node to a destination node, especially in multi-hop scenarios It is responsible for routing packet delivery through intermediate routers There are two primary methods of routing protocols: packet-switch routing and virtual circuit routing In packet-switch routing, each node independently makes routing decisions for relaying packets, while virtual circuit routing establishes a predetermined path at the start of the network operation Packet-switch routing can be further divided into proactive and reactive routing protocols, with most terrestrial wireless networks relying on packet-switch methods.
Proactive routing protocols aim to reduce message latency by continuously maintaining up-to-date routing information between nodes These protocols broadcast control packets containing routing table data to neighboring nodes, with examples including the Temporally Ordered Routing Algorithm (TORA) and Destination Sequence Distance Vector (DSDV) However, proactive routing can incur significant signaling overhead when establishing routes for the first time or whenever the network topology changes This approach may not be suitable for underwater environments due to their limited bandwidth and high likelihood of link failures.
Reactive routing: Reactive routing protocols only initiate a route discovery process upon request In this protocol, each node does not need to maintain a sizable
Routing protocols that utilize a "look-up" table are particularly effective in dynamic channel environments, such as ad hoc wireless networks Notable examples of these protocols include Dynamic Source Routing (DSR) and Ad hoc On-demand Distance Vector (AODV).
The primary drawback of reactive routing protocols is their high latency in establishing routes, which is exacerbated in underwater environments due to the slower propagation speed of acoustic signals compared to radio waves in air Similar to proactive protocols, reactive methods require the flooding of control packets to create paths, resulting in considerable signal overhead.
For underwater acoustic communication networks, virtual-circuit-switch routing protocols can be a better choice The reasons are:
Underwater acoustic communication networks are typical asymmetric instead of symmetric, but packet switched routing protocols are employed for symmetric network architecture;
For Virtual-circuit-switch routing protocols, these protocols work robust against link failure, which is critical in underwater channel environment; and
Virtual-circuit-switch routing protocols have low latency and less signal overhead, which are needed for UACN environment
UACNs face a challenge with the rigidity of virtual-circuit-switch routing protocols To enhance their effectiveness, it is essential for UACN network layer research to explore ways to introduce flexibility into these protocols.
In Underwater Acoustic Sensor Networks (UACNs), the exploration of application layer protocols is an emerging field The application layer aims to establish a network management protocol that ensures the lower layer's software and hardware details remain transparent to management applications Key functionalities of this layer include facilitating efficient network management and enhancing overall system performance.
Some practical examples of application layer protocols for terrestrial wireless networks are File Transport Protocol (FTP), Simple Mail Transfer Protocol (SMTP), and Telnet [34]
Conclusion
A varied selection of example soundings showcases the complexity of acoustic propagation channels, emphasizing the difficulties in developing communication systems that can withstand environmental variations Additionally, it is evident that channel simulation and modeling encounter significant obstacles, as the assumptions and simplifications made can restrict the applicability of a model to only a limited range of existing channels.
MODELING AND SIMULATION OF UNDERWATER ACOUSTIC
The channel model
The underwater acoustic channel is a complex environment influenced by various factors, including water temperature, salinity, pressure, and pH levels These elements can be interdependent and vary across different ocean regions Therefore, developing an accurate model to understand how these underwater parameters affect sound speed is crucial for effective acoustic communication and research in marine environments.
Figure 3- 1 Colladon and Sturm's apparatus for measuring the speed of sound in water
Understanding sound speed in water is essential for various applications in acoustical oceanography The first measurement, conducted by Colladon and Sturm in 1827 in Lake Geneva, recorded a speed of 1435 m/s However, it was later discovered that sound travels faster in saline water, and temperature plays a significant role in determining sound speed Extensive laboratory and field studies have demonstrated that sound speed increases in a complex manner with rising temperature, hydrostatic pressure, and the concentration of dissolved salts in the water.
The formula for the speed in m/s was given by MacKenzie with an error in the range of approximately 0.07 m/s
(3-1) Here, the temperature T is expressed in o C, salinity S in parts per thousand
[ 000], depth D in meters, and sound velocity v in meters per second
The sonar parameter transmission loss in UANs channels refers to the reduction in acoustic intensity as a pressure wave travels from its source A critical factor affecting the signal-to-noise ratio of receiver losses is the signal energy The primary contributor to attenuation loss is the absorption of sound energy, which typically involves absorption by seawater and the interface medium.
The path loss in underwater channels, which have to be added to the average power level, can be expressed as [48]
The equation TL = k * l + α * l describes the relationship between transmission loss (TL), geometrical spreading factor (k), range (l in meters), and the absorption coefficient (α in dB/km) The absorption coefficient is determined through specialized models, while the values for the geometrical spreading factor can be found in Table 3-1.
Table 3-1 Values for representing types of geometrical spreading via the geometrical spreading coefficient k
The total absorption coefficient in dB/km is derived in [23], the absorption coefficient can be calculated using Thorp model [27] , Schulkin and Marsh model, Francois and Garrison model as follows:
The Thorp model calculates the absorption coefficient in dB/km, relying solely on the signal frequency It is specifically optimized for accuracy at a temperature of 4°C and a depth of around 1000 meters.
[ dB km / ] (3-3) Where ( )f is given in dB/km, f is the center frequency of the transmitted signal in kHz
Schulkin and Marsh model [49] : The attenuation coefficient in dB km / can be obtained from:
S is the salinity in [ ppt ],
P z is hydrostatic pressure [kg cm/ 2 ], f is frequency [ kHz ],
Where T is the temperature in [ o C], the temperature ranges from 0 o to 30 o C, f T varies approximately from 59 to 210 kHz
Francois and Garrison model [48] : The Francois and Garrison model takes into account the effect of Boric Acid B OH( ) 3 , Magnesium Sulphate MgSO 4 , Pure water
H O 2 , while also introducing the effects of temperature and depth The result obtained in dB km / from this model can be obtained from:
Where T is the temperature in [ o C],pH is the acidity of water, S is the salinity in parts per trillion,z max is the depth in meters
Underwater acoustic channels experience various types of noise, including disturbance noise, environmental noise, discrete ship noise, and emission receiver noise Among these, environmental noise significantly impacts the Signal-to-Noise Ratio (SNR) at the receiver, playing a crucial role in determining the required transmitting power.
Underwater ambient noise is characterized by a Gaussian distribution and exhibits a continuous power spectral density The primary contributors to this ambient noise include wind-driven waves, shipping activities, turbulence, and thermal noise The power spectral density for each of these sources is quantified in dB re µPa Hz according to specific formulas.
(3-17) The overall noise power spectral density may be obtained in Pa from:
Turbulence noise (N t) primarily affects low-frequency ranges, specifically below 10 Hz, while shipping noise (N s), which is influenced by a shipping factor between 0 and 1, impacts higher frequency regions Additionally, wind-driven wave noise (N w) is determined by wind speed in meters per second, and thermal noise (N th) also plays a role in the overall sound profile.
10 Hz 100 Hz The wind driven wave noise influences at frequency region
100 Hz 100 kHz (this is the mainly operating region of acoustic systems) And finally, thermal noise is dominant in f 100kHz.
MACAW protocol
The MACAW protocol, proposed by Vaduvur Bharghavan, utilizes three types of short, fixed-size signal packets to facilitate communication between stations When station A intends to transmit data to station B, it initiates the process by sending a Request-to-Send (RTS) packet, which includes the length of the intended transmission If station B is available and receives the RTS, it responds with a Clear-to-Send (CTS) packet, also detailing the transmission length Following the receipt of the CTS, station A promptly sends its data Upon receiving the data packet, station B acknowledges the successful reception by sending an Acknowledgement (Ack) back to station A.
Simulation of underwater acoustic channel
The wireless communication channel is represented through 14 pipeline stages in OPNET, with the initial 6 stages implemented in the transmitter and the remaining 8 stages in the receiver.
Figure 3- 2 Radio Transceiver Pipeline Execution Sequence for One Transmission
For the purpose of modeling of the underwater acoustic channel, the following stages have to be modified:
Propagation Delay (stage 5): In this stage, MacKenzie’s formula is used to simulate the transmission speed
In the Receiver Power stage, three propagation loss models are utilized: the Thorp model, the Schulkin and Marsh model, and the Francois and Garrison model These models are simulated and compared to determine the most suitable option for effectively simulating an underwater network.
Background Noise (stage 9): In this stage four types of ambient noise are used, which is affected by the wind driven waves, shipping, turbulence, and thermal noise
The simulation network consists of eight nodes randomly distributed within a 10km x 10km area at a depth of 1000 meters, each assigned a unique ID number for intercommunication Figure 3-3 illustrates the network topology, while Table 3-2 provides the simulation parameters.
RTS，CTS and Ack packets size /bit 64
(b) Francois & Garrison and Schulkin & Marsh model
Figure 3- 4 The propagation loss curves vs Depth
Figure 3-4 illustrates the simulation results of attenuation loss across three models: Thorp, Schulkin & Marsh, and Francois & Garrison, at varying depths The Thorp model remains unaffected by underwater sound velocity, while the Schulkin & Marsh and Francois & Garrison models account for channel variations such as temperature, salinity, pH, depth, and surface/bottom roughness As depth increases, the underwater sound velocity rises, resulting in a decrease in attenuation loss, as depicted in Figure 3-4.
Figure 3- 5 The propagation loss curve vs Frequency
Figure 3-5 illustrates the simulation results of propagation loss as a function of frequency It shows that all three attenuation loss models exhibit an increase in loss with rising fundamental frequency However, the Francois & Garrison model demonstrates a slower increase in loss, resulting in the lowest propagation loss among the models analyzed.
From the above results, Francois & Garrison is chosen to implement a simulation of an underwater network by using OPNET tool
Figure 3- 6 The propagation loss curve vs The distance between two nodes
Figure 3-6 illustrates the simulation results of propagation loss as a function of distance As the distance between two nodes increases, the propagation loss rises according to a logarithmic equation This phenomenon occurs because sound wave propagation in underwater channels is significantly influenced by various factors, including channel variations, multi-path propagation, water temperature, wind speed, salinity, sound velocity, and the roughness of the seawater surface and bottom.
Figure 3- 7 The throughput of network
Figure 3-7 illustrates the simulation results of the MACAW protocol's throughput, utilizing MacKenzie’s formula for transmission speed, the Francois & Garrison model for attenuation loss, and considering ambient noise factors such as wind-driven waves, shipping, turbulence, and thermal noise The findings indicate that the channel's throughput experiences fluctuations.
The maximum throughput of underwater acoustic channels is limited, achieving only 200 bits per second due to various factors such as low transmission speeds, environmental variations (including salinity, wind speed, and seawater temperature), and narrow bandwidth To enhance the throughput of underwater networks, it is essential to implement effective medium access control protocols and advanced acoustic signal processing techniques.
Figure 3- 8 The bit error rate and packet loss ratio of network
The bit error rate is approximately 0.32, while the packet loss ratio fluctuates around 0.45, as illustrated in Fig 3-8 These high rates are primarily due to channel variations, multi-path propagation, and Doppler shift in underwater sound channels, which pose significant challenges for achieving high data rates Additionally, the usable bandwidth in these channels is limited Key noise sources, including turbulence, shipping, waves, and thermal noise, contribute to the elevated bit error rate Furthermore, collisions at the receiver exacerbate the high packet loss ratio To mitigate this issue, implementing an effective medium access control protocol and advanced acoustic signal processing methods is essential for reducing collision occurrences.
Conclusion
This chapter presents the design of an underwater acoustic channel model using OPNET software, incorporating three attenuation loss models specific to underwater acoustic characteristics The findings reveal that the Francois and Garrison attenuation loss model exhibits the lowest propagation loss Consequently, we selected this model along with MacKenzie’s propagation speed formula and four ambient noise types influenced by wind-driven waves, shipping, turbulence, and thermal noise to simulate an underwater acoustic mobile network The simulation results encompass key performance metrics such as propagation loss, throughput, error bit rate, bit errors per packet, and end-to-end delay, offering valuable insights for modeling and simulating underwater acoustic mobile networks.
MODELING AND SIMULATION OF P-ALOHA,
CSMA/CA AND MACAW PROTOCOLS FOR
Underwater communication is essential for various applications, including military operations, the oil industry, environmental monitoring, scientific data collection, and resource discovery Unlike terrestrial wireless networks that rely on radio waves, underwater acoustic networks employ acoustic waves, which face significant challenges at both the physical and data-link layers A key drawback is the slow speed of acoustic wave travel in water, approximately 1500 m/s, which is five times slower than radio waves, resulting in high propagation delays that diminish the throughput of Underwater Acoustic Networks (UAN) Additionally, wave propagation in underwater acoustic channels is influenced by factors such as temperature, salinity, pH, water depth, surface and bottom roughness, multipath propagation, and Doppler shift.
The design and deployment of Underwater Acoustic Networks (UANs) encounter unique challenges, particularly in developing accurate underwater acoustic channel models for simulation Nan et al (2020) introduced a shallow water acoustic channel model that incorporates real acoustic propagation characteristics, including path attenuation, ambient noise, multipath effects, and Doppler shifts Research analyzing at-sea data in varying shallow water conditions demonstrates how ocean environments influence signal properties such as amplitude, phase variations, and the temporal coherence of multipath signals Additionally, Fan et al (2020) focused on the performance of the P-Aloha protocol within UANs using OPNET-based modeling Other studies, including those by King et al (2020) and Pan et al (2020), further explored channel models based on the Thorp and Wenz models and improved models utilizing NS-2 and BELLHOP beam tracing Bouzoualegh et al (2020) contributed to the understanding of UAN communication systems by modeling and simulating physical layer characteristics and communication protocols using state-flow and SIMULINK tools.
The researches above have focused on analyzing the characteristics of underwater environment [63] , the performance of the application of an individual MAC protocol
[42], channel model simulation, modeling and simulation of the physical layer characteristics [40, 44, 45]
Modeling and simulating the underwater acoustic channel is crucial for assessing the performance of Medium Access Control (MAC) protocols This chapter focuses on the characteristics of the underwater acoustic channel, utilizing the OPNET simulation tool for analysis The simulation evaluates the P-Aloha, CSMA/CA, and MACAW MAC protocols, comparing their performance in an underwater environment based on the results obtained.
Protocols description
P-Aloha protocol was proposed by Norman Abramson In pure Aloha, all stations will be in idle state in usual time When a station (sender) wants to send its data, it transmits data into channel, and enters the state of waiting for an ACK packet which sent by the receiver to confirm the successful receipt of the data packet At the receiver, when the proper receiver receives the data packet, it will send an ACK packet back to sender to acknowledge the receipt of the data packet After a time period, its timer is overtime and still hasn't get the ACK packet, the sender will retransmit the data packet after a period of random back off time, and enters the state of waiting for ACK packet again Due to the using of random time back off time, the probability of collision is reduced in the next transmission If the retransmission number of the packet is up to the threshold, the transmission will be aborted [42, 64] The Figure 4-1 shows the frames which have collided, Fig.4-2 is overlapped frames and Fig.4-3 shows the protocol process model in the Aloha protocol
Figure 4- 1 Pure Aloha protocol Shaded boxes indicate frames which have collided
Note that the Pure ALOHA does not check whether the channel is busy before transmitting
To assess Pure ALOHA, the throughput is needed to predict First, let's make a few simplifying assumptions:
All frames have the same length
Stations cannot generate a frame while transmitting or trying to transmit (That is, if a station keeps trying to send a frame, it cannot be allowed to generate more frames to send.)
The population of stations attempts to transmit (both new frames and old frames that collided) according to a Poisson distribution
Figure 4- 2 Overlapping frames in the pure Aloha protocol
In telecommunications, let T represent the time required to transmit a single frame, which we will define as "frame-time." The variable G denotes the average number of transmission attempts per frame-time, as described by the Poisson distribution, indicating that there are typically G attempts for each frame-time.
For successful frame transmission, it is essential to consider the timing of the process Denote the time for sending a frame as t, during which we aim to utilize the channel for one frame-time starting at t To achieve this, it is crucial that all other stations avoid transmitting during this designated time.
For any frame-time, the probability of there being k transmission-attempts during that frame-time is:
The average amount of transmission-attempts for 2 consecutive frame-times is
2G Hence, for any pair of consecutive frame-times, the probability of there being k transmission-attempts during those two frame-times is [64] :
Therefore, the probability P pure of there being zero transmission-attempts between t-T and t+T (and thus of a successful transmission for us) is:
The throughput can be calculated as the rate of transmission-attempts multiplied by the probability of success, and so we can conclude that the throughput S is [64] :
The maximum throughput is 0.5/e frames per frame-time (reached when G = 0.5), which is approximately 0.184 frames per frame-time This means that, in Pure
ALOHA, only about 18.4% of the time is used for successful transmissions [64]
Figure 4- 3 P-Aloha protocol process model
4.1.2 CSMA and CSMA/CA protocols [65]
In the CSMA protocol, a station checks for the absence of other traffic on a shared transmission medium before sending data The sender detects any existing carrier waves from other stations and, if one is present, it pauses until the ongoing transmission concludes before starting its own transmission.
The CSMA/CA protocol enhances CSMA performance by minimizing channel occupancy When the channel is detected as busy prior to transmission, the protocol defers the transmission for a random time interval, effectively decreasing the likelihood of collisions Figure 4-4 illustrates the CSMA/CA protocol process model.
Figure 4- 4 CSMA/CA protocol process model
Figure 4- 5 Busy and idle periods in CSMA protocol
G represents the arrival rate of new and rescheduled packets, where not all arrivals lead to actual transmissions; packets encountering a busy channel are rescheduled This means G reflects the "offered" traffic, with only a portion contributing to actual channel usage When a packet arrives at time t and finds the channel idle, any subsequent packets arriving within the next a seconds will also sense the channel as unused, potentially leading to transmission conflicts If no other terminal transmits during this vulnerable period, the initial packet will successfully transmit.
Let t + Y be the time of occurrence of the last packet arriving between t and t + a The transmission of all packets arriving in (t, t + Y) will be completed at t + Y + 1
In a transmission period (TP), which lasts from time t to t + Y + 1 + a, any terminal that becomes ready will detect the channel as busy and will reschedule its packet During this TP, only one successful transmission can occur The time between two consecutive TPs is referred to as an idle period, and together, a busy period and the subsequent idle period form a cycle The expected duration of the busy period is denoted as B, the expected duration of the idle period as I, and the total expected length of a cycle is represented as B + I.
U denote the time during a cycle that the channel is used without conflicts Using renewal theory arguments, the average channel utilization is simply given by
(4-5) The probability that a TP is successful is simply the probability that no terminal transmits during the first a seconds of the period and is equal to e aG Therefore
U e aG (4-6) The average duration of an idle period is simply l/G The average duration of a busy interval is 1 + P + a, where Y is the expected value of Y
Therefore, the equation for the throughput S is expressed in terms of a (the ratio of propagation delay to packet transmission time) and G (the offered traffic rate) as follows :
The MACAW protocol, proposed by Vaduvur Bharghavan, utilizes three types of short, fixed-size signal packets to facilitate communication between stations When station A intends to transmit data to station B, it initiates the process by sending a Request-to-Send (RTS) packet, which includes the length of the intended transmission If station B, upon receiving the RTS and not deferring, responds with a Clear-to-Send (CTS) packet that also specifies the transmission length, station A can then proceed to send its data Following the successful reception of the data packet, station B acknowledges the receipt by sending an Acknowledgement (ACK) packet back to station A This handshake process is illustrated in Figure 4-6, while Figure 4-7 depicts the overall MACAW protocol process model.
Figure 4- 6 A successful handshake MACAW protocol
Figure 4- 7 MACAW protocol process model
The simulation of protocols
The simulations will focus on three MAC protocols: P-Aloha, CSMA/CA, and MACAW, within a 6-node static network comprising one receiver node and five sender nodes The receiver, acting as an Access Point (AP), collects data from nodes 1 to 5, which are distributed over a 10 km by 10 km area with a depth of 1000 m, while the receiver node is positioned at the surface of the seawater Each node is assigned a unique ID for intercommunication purposes, as illustrated in Figure 4-8, which depicts the simulation network's topology.
Figure 4- 8 Underwater acoustic network topology
Generally, the simulation parameters are set as Table 4-1:
RTS, CTS, ACK packets size 100 bit
The packet generator generates 1000 bit packets that arrive at mean rate of 1 packets/second with a exponential inter-arrival time.
Simulation results
In underwater acoustic networks, key performance indicators include throughput, average end-to-end delay, channel utilization, collision status, and packet loss ratio The simulation results presented below utilize a multi-sequence simulation mechanism with the OPNET tool.
Figure 4- 9 The average of collision status of P-Aloha, CSMA/CA and MACAW protocols in UAN
Figure 4- 10 Throughput of P-Aloha, CSMA/CA and MACAW protocols in UAN
The pure Aloha protocol allows nodes to transmit packets immediately, leading to a higher rate of collisions at the receiver compared to CSMA/CA and MACAW protocols While CSMA/CA employs a carrier sensing mechanism to reduce collisions, its effectiveness is diminished in underwater environments due to propagation delays, resulting in a higher collision rate than MACAW In contrast, the MACAW protocol utilizes a four-way handshake with RTS, CTS, DATA, and ACK packets, which further minimizes collisions.
The throughput performance of three protocols, as illustrated in Figure 4-10, is notably low due to the limited bandwidth of underwater acoustic channels Factors such as channel variations, multi-path propagation, and Doppler shift hinder the achievement of high data rates Among the protocols, P-Aloha and CSMA/CA demonstrate higher throughput compared to the MACAW protocol, which utilizes a RTS-CTS-DATA-ACK handshaking mechanism before data transmission Additionally, the collision rate of the MACAW protocol is lower than that of P-Aloha and CSMA/CA, as evidenced by Figures 4-9 and 4-10.
Figure 4- 11 The average of channel utilization of P-Aloha, CSMA/CA and MACAW protocols in
Figure 4- 12 The average of End-to-End delay of P-Aloha, CSMA/CA and MACAW protocols in
The average channel utilization for the P-Aloha and CSMA/CA protocols is approximately 54, while the MACAW protocol averages around 34, indicating overall low performance due to the high propagation delay and limited bandwidth of underwater acoustic channels The significantly lower utilization of the MACAW protocol is attributed to its handshaking mechanism, which involves the exchange of RTS, CTS, DATA, and ACK packets before data transmission This process increases queue delays, especially given the low sound velocity in seawater, leading to higher End-to-End delays for the MACAW protocol compared to P-Aloha and CSMA/CA, as illustrated in Figure 4-12.
Figure 4- 13 The average of bit error rate of P-Aloha, CSMA/CA and MACAW protocols in UAN
The bit error rate in underwater acoustic channels is approximately 0.33, primarily due to various factors affecting wave propagation These factors include channel variations, multi-path propagation, Doppler effects, and environmental conditions such as water temperature, shipping density, salinity, pH, depth, and surface and bottom roughness Additionally, four main noise sources—turbulence, shipping, waves, and thermal noise—significantly contribute to the elevated bit error rate in these channels.
SLOTTED FLOOR ACQUISITION MULTIPLE ACCESS IN MEDIUM
Slotted FAMA-CTS MAC protocol
To prevent collisions caused by varying packet delays, Fullmer and Garcia [88] detail the FAMA-CTS protocol, which utilizes a four-way handshake process This handshake includes Request-To-Send (RTS), Clear-To-Send (CTS), Data-Length (DL), and Data (DATA), as illustrated in Figure 5-2.
In a communication process, a sender initiates a handshake with the intended receiver by broadcasting a Request to Send (RTS) packet Upon receiving the RTS, the receiver responds with a Clear to Send (CTS) packet, provided it is not engaged in another handshake and can transmit The receiver then enters a silent state for a predetermined duration, setting its silent timer to ε seconds, ensuring that the CTS packet is received by all nearby stations Once the silent timer expires, the receiver resumes normal operation Meanwhile, the sender, upon receiving the CTS packet, calculates the optimal timing for broadcasting Data Link (DL) packets to avoid overlapping with the receiver's silent period The sender also adjusts its transmission power based on the distance to the receiver to conserve energy After sending the DL packet, which informs neighbors and the receiver about the upcoming data packets, the sender sets its silent timer to γ/2 seconds The data packets are transmitted once this silent period concludes The receiver, upon receiving the DL packet, broadcasts it to neighboring stations and prepares to receive the data from the sender This successful handshake process is depicted in Fig 5-2.
When a station hears the initial dialogue between a sender and a receiver on the channel, the type of packet received will determine its actions as below:
Upon receiving an RTS packet and a DL packet meant for another station, a station recognizes it remains within the transmission range of the sender It then activates its silent timer for δ seconds, where δ corresponds to the average size of the data block indicated by the DL packet If the station has data prepared for transmission during this interval, it can exit the silent state and shift to the sending state to transmit its data packets.
When a station receives a CTS packet meant for another station, it realizes it remains within the transmission range of a receiver However, once the receiver adjusts its emission power, the station will be outside the receiving range Consequently, it sets a timer for δ max seconds, which represents the maximum size of a data block, allowing it to receive data during this interval Once the δ max time elapses, the station can either receive or transmit data.
Upon receiving an RTS packet, a CTS packet, and a DL packet meant for another station, a station recognizes that it remains within the transmission range of both the sender and the receiver Consequently, it activates its silent timer, set to δ, during which it refrains from sending or receiving any data.
Upon receiving a CTS packet and a DL packet meant for a different station, a station recognizes that it remains within the transmission range of a receiver Consequently, it activates its silent timer for δ seconds, refraining from sending or receiving any data during this period.
Figure 5- 2 The handshake procedure in Slotted FAMA-CTS protocol
Backoff algorithm
When a station has data to transmit, it first checks if the channel is available If the channel is idle, the station sends its data; if busy, it enters a backoff state with a randomly determined duration between a minimum and maximum time If the station does not receive a Clear To Send (CTS) response after a random interval, it re-enters the backoff state for a duration of δ seconds, which corresponds to the average size of a data block This cycle continues until the station successfully accesses the channel Additionally, if a station is within the coverage range of both the sender and receiver, the backoff time is set to ε seconds, where ε/2 represents the maximum propagation delay The backoff algorithm process is illustrated in Fig 5-3.
Figure 5- 3 The process of backoff algorithm
Throughput analysis
This protocol operates under the assumption that a station cannot transmit and receive signals simultaneously, and that the switch time between these states is negligible The only source of error in the channel arises from packet collisions, with no additional interference present, and stations are capable of perfectly detecting these collisions.
The Slotted FAMA-CTS transmission periods are illustrated in Fig 5-4, where each packet requires T seconds for transmission The average number of packets generated per transmission time, denoted as S, represents the channel throughput rate While the maximum throughput of the channel can reach 1 under ideal conditions, real-world interferences often result in a throughput that is less than this ideal value Key parameters include S for channel throughput, λ for offered channel traffic rate, a one-second transmission time for packets, τ for propagation delay, γ for the length of two RTS or CTS packets, δ for the length of one data block, and ε for the safety time slot allocated for the CTS packet.
Figure 5- 4 Slotted FAMA-CTS transmission periods
The average channel utilization is given by [65]
The expected duration of a busy period (B) refers to the time the channel actively transmits and receives data, while the expected duration of an idle period (I) indicates the interval between two busy periods Additionally, U represents the time during a busy period when the channel successfully transmits user data.
A successful transmission consists of several key time intervals, including propagation delay, the time required to send and receive RTS, CTS, DL, and DATA packets, as well as the duration of a safe time slot Consequently, the total time T for a successful transmission can be calculated by summing these individual time components.
This protocol ensures successful transmission of data packets following the successful sending of a Request to Send (RTS) packet A transmission is deemed unsuccessful if one or more RTS packets from other stations are sent within a time interval of t to t + Y seconds, where 0 ≤ Y ≤ τ The average duration of failed transmissions is provided in reference [65].
T Fail Y (5-3) The cumulative distribution function for Y is the probability that no arrival occurs in the interval of length y and equals F Y y ( ) e ( y ) [88] , where y; the expected value of Y is: [65]
So it can be obtained [65]
The success probability of an RTS packet is determined by the likelihood of no arrivals occurring within a specified time frame of τ seconds This arrival process follows a Poisson distribution characterized by the parameter λ.
P s = P{No arrivals in seconds} = e (5-6) The busy period consists of a single successful and failed transmission period, the average busy period is as follows:
The average channel utilization is the time in which the useful data are sent successfully during a busy period:
U P e (5-8) The average duration of an idle period is,
By substituting (5-7), (5-8), (5-9) into (5-1), we obtain (5-10)
Figure 5- 5 Throughputs of Slotted FAMA-CTS, FAMA-NTR and Slotted MACA protocol
This study aims to evaluate the throughput performance of the FAMA-CTS protocol in underwater acoustic communication networks Experiments were conducted using an Intel Pentium Dual CPU operating at 2.4 GHz with 1 GB of RAM, utilizing MATLAB (Version 7.9.0) as the programming environment.
The performance of the proposed FAMA-CTS protocol is evaluated by comparing its throughput with the FAMA-NTR and Slotted MACA protocols As illustrated in Figure 5-5, the throughput is analyzed against the offered load variable λ, with parameters set at δ = 3 packets and 1000 bits per packet, and τ = 0.5 seconds.
The simulation results indicate that the Slotted FAMA-CTS achieves a maximum throughput of approximately 42%, surpassing the FAMA-NTR's maximum throughput of 35% and the Slotted MACA's maximum throughput of 31% This enhanced throughput can be attributed to its efficient handshake mechanism, which minimizes collisions, and its effective utilization of channel resources.