# Performance-Aware Green Algorithm for Clustering of Underwater Wireless Sensor Network Based on Optical Signal-to-Noise Ratio

Authors: Pooja Ashok Shelar; Parikshit Narendra Mahalle; Gitanjali Rahul Shinde; Haribhau Rambhau Bhapkar; Minyechil Alehegn Tefera
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Underwater wireless sensor network (UWSN) has limited bandwidth, long propagation delays, and unrechargeable batteries, which forces the development of techniques minimizing the energy consumption of UWSNs. One solution is clustering that outperforms in terms of energy optimization and, in addition, can also provide scalability and data reliability. The development of clustering algorithms depends on UWSN architectures. In our case, we have proposed a hybrid UWSN network architecture named ACOP-UWSN (ACoustic OPtical Underwater Wireless Sensor Network), which aims for reliable data transmission at the fastest speed with higher data rates and minimized propagation delays. In ACOP-UWSN, optical sensors sense data, whereas acoustic sensors only relay data towards the surface buoy. Clustering of all these nodes may result in unnecessary wastage of the node’s energy and memory. Therefore, we undergo clustering of only optical sensors, as comparatively, sensing and processing of data consume maximum energy and memory than relaying data. In the proposed clustering algorithm, the criteria for cluster head selection are OSNR (optical signal-to-noise ratio) and residual energy. The mathematical derivation of OSNR for underwater wireless optical communication is a key part of the algorithm. The results achieved using the simulation tool show that our proposed OSNR-based energy efficient clustering algorithm (OECA) is efficient in terms of energy and as well data reliability as compared to state of art. The quantitative analysis of the results showed that the total energy consumed for the existing scheme is 450 J, which is more as compared to 400 J of OECA. Similarly, the residual energy of nodes using OECA is 1% greater than the existing scheme. To our knowledge, research on the clustering of optical sensors in UWSN is least available, and we are among the few to work on an OSNR-based clustering algorithm for underwater optical nodes.


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## Body

## 1. Introduction

A WSN developed below water with intention of monitoring the underwater environment is known as UWSN [1]. The sensors in underwater environments are wirelessly connected through acoustic, optical, and electromagnetic underwater communication medium. The advancements in sensor technology and efficient underwater protocols have made UWSN an important part of various underwater applications. These applications are tsunami monitoring, oil spill monitoring, quality monitoring of drinking water, surveillance of water territory, enemy submarine detection, mine detection, the study of coral reefs and aquatic animals, underwater research labs, and so on. The UWSN applications mainly notify the emergencies. For example, if the temperature or pH value goes above a threshold value, then the deployed UWSN notifies the offshore station. Will such a wide range of underwater applications that are paramount for the government has forced the researchers to increase their studies about UWSN. There are also some real time deployed underwater applications, like Seaweb [2], which is a surveillance underwater acoustic network deployed in the US and NEPTUNE [3] a Canada-based underwater acoustic network. One more example of underwater networks is the US-based PLUSNet (Persistent Littoral Undersea Surveillance Network) [4], which is used for antisubmarine warfare and is supported by US naval research office. The PLUSNet network is designed with sensors and automated vehicles like autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). All the above-listed surveillance networks aim to provide surveillance with an unblinking eye. Most surveillance networks use acoustic waves as the communication medium and are semi-automatic means it is a combination of fixed and mobile sensors along with intelligent autonomous vehicles. In general, the drawbacks of surveillance networks are weak data security and unreliability. Hence in our work, we proposed the UWSN model that can notify offshore stations with reliable data transmission and high level of data security. The propagation of electromagnetic or radiowaves in UWSN is affected by strong absorption due to the natural properties of water. A small ray of hope can still be kept by doing underwater communication using very low-frequency radio waves. But this low-frequency wave led to lower bandwidth and ultimately unreliable communication [5]. Hence, researchers came up with the next option, i.e., acoustic waves. The acoustic wave, also commonly known as the sound wave, is the most commonly used underwater communication medium [6]. The acoustic waves can transmit high-quality images and data via acoustic modems. In an underwater environment, acoustic waves can travel a distance of 100 kms. The bandwidth achieved depends on the distance travelled. The acoustic waves are capable to travel long distances at low frequencies and simultaneously also achieve data reliability in challenging underwater environments. However, an acoustic wave is not a full-fledged solution due to the blacker part of the acoustic signal. Like limited bandwidth, higher propagation delays, increased energy consumption, and smaller network lifetime have a bad impact on UWSN. Although acoustic underwater communication medium achieves data reliability, it consumes maximum node’s energy. Also recharging and replacing an underwater node’s battery is a costly affair.Then research came up with an attractive third option, “optical wave” as an underwater data communication medium [7], which provides higher bandwidth in Gbps and has added the feature of minimum propagation delay. But the scattering phenomenon and short distance travel from 10 m to 30 m are hurdles of optical waves. The different areas of the sea tend to have different optical properties. The optical wave travels with maximum bandwidth in deep clear oceans, whereas it is absorbed in coastal areas.The extensive literature survey then raised many questions, like which is the best communication medium for UWSN? How to achieve data reliability? How to conserve node batteries? How to get the fastest data transmission for time-critical underwater data applications? How to minimize propagation delays?, and finally how to customize the architecture design of UWSN, which will depict an answer to all the above questions.In this context, after studying different architectures, we came up with an idea of innovative underwater architecture, which will use a combination of acoustic and optical communication mediums. The below diagram depicts a diagonal correlation of acoustic and optical underwater communication mediums. As shown in Figure1, the negative points of the acoustic medium have become a point for the optical medium and vice versa.Figure 1
Diagonal comparison of acoustic and optical mediums. The minimum bandwidth that was a negative point of the acoustic medium has become a positive point for the optical medium as it provides maximum bandwidth. A similar type of diagonal correlation is shown for other points.The development of efficient UWSN architecture requires the study of challenging underwater environments and various underwater applications with different algorithmic strategies for routing, security, localization, and clustering. The proposed ACOP-UWSN architecture is a small effort towards building a full-fledged architectural solution for various underwater applications. The in-detail explanation of ACOP-UWSN from an architectural point of view is presented in the subsequent sections of this paper.This paper rotates around the question of how to achieve secure and reliable data transmission with minimum energy consumption? In literature, there are various strategies to deal with data reliability like routing algorithms, node localization, authentication, cryptographic algorithms, and many more. Among these, “clustering” is a basic and most powerful networking strategy that achieves three important parameters of network, i.e., reliability, systematic data aggregation, and increased network lifetime by minimizing energy consumption.Definition of Clustering: an algorithmic art to create optimal size groups of underwater sensors and then electing one single leader in each group is known as clustering.[Note: groups formed are known as clusters and the elected leader is known as cluster head (CH), whereas leftover nodes in a group are known as cluster members (CM).]In each cluster, data from all CMs are collected by CH, which collects and relays the sensed data to sink nodes or base station. In UWSN, the sink nodes are buoy or surface ships. The data are further forwarded to its destination, which is generally an offshore station or a satellite.In this research work, we used an OSNR and residual energy as clustering criteria for an underwater optical sensor network. The OSNR is a performance parameter to measure the quality of optical networks. The OSNR is like a quality of service (QoS) parameter at the physical layer. The residual energy is an important node evaluation method. The proposed algorithm aims for minimal energy consumption per node and thus moves towards increased network lifetime. About the motive of our research work, we have tried to solve the energy optimization issue of underwater nodes. Here, we have summarized our small contributions in the worth of UWSN, which are stated as follows:(1)
A new architecture for UWSN named ACOP-UWSN is our first contribution to this work. The proposed 2D ACOP-UWSN combines the effects of positive points of optical and acoustic underwater nodes. So, it can reliably transmit a large amount of data at the fastest speed.(2)
The proposed clustering algorithm for optical sensors is developed based on OSNR and the node’s residual energy. The algorithm has proven to be efficient in achieving the increased lifetime for the proposed ACOP-UWSN.(3)
A unique formula for the signal-to-noise function of optical sensors, i.e., OSNR, is introduced, which is used to decide the cluster head.(4)
The results achieved using simulation verify the efficiency and feasibility of the proposed work.To our knowledge, there is no scheme or research available for clustering underwater optical sensors using high OSNR values. In the CH selection process, we also consider other parameters like residual energy value that should be greater than a predetermined threshold energy value. The motivation behind the presented work was the literature analysis done in Section2, which pointed out that almost all underwater wireless networks are built with only acoustic sensors. Due to this acoustic, UWSN faced various issues like unreliable data transmission, wastage of nodes energy, security attacks, and increased propagation delays. All the listed problems motivated us to design a new architecture and a clustering algorithm that will resolve all issues to a certain extent.The further paper is organized into six sections. In Section2, we explore the literature for underwater clustering of optical sensors but found less work in the domain, so have gone through various underwater clustering of acoustic sensors. In Section 3, there is an in-detail explanation of the proposed system model with the construction of the OSNR formula. The supportive proof for the efficiency of the proposed research work is shown in Section 4. Section 5 is a discussion on various tools available to work on UWSN. Finally, we conclude with the future scope of the work in Section 6.


## 2. Related Work

### 2.1. Improving Study of Clustering Techniques

The proposed UWSN design is based on the clustering of optical sensors, this is something rarely done in the state of art. After exploring the literature, it was found that underwater clustering is 99% for acoustic sensors and very less work speaks about underwater optical sensors clustering. But the fact is related work study of clustering schemes for acoustic nodes has helped us to develop a new methodology for clustering optical nodes. Hence, to enhance our knowledge of generalized clustering schemes in UWSN, we studied the literature on clustering schemes for acoustic sensors. The first observation from related work was that most clustering algorithms developed for UWSNs are manipulated versions of clustering strategies developed for TWSNs. For example, the LEACH [8], GAF [9], HEED [10], and K-means [11] algorithms originally developed for the terrestrial environments are also used in the underwater environment, but with some parametric changes as per the requirement of the underwater environment. There are mainly two reasons behind, “why TWSN clustering algorithms are not directly applicable in UWSN?”. First, the high propagation delays due to water and nonrechargeable nodes of underwater networks demand an improved, concise, and efficient clustering algorithm. Second, GPS can never be a part of UWSN. As the high-frequency radio waves cannot travel underwater below a certain depth. Therefore, a new thought toward designing of reliable and energy-effective clustering algorithm according to features of UWSN will always be appreciated.[Note: The underwater network architectures are divided into three types 2D, 3D, and 4D. The clustering strategy is developed by also considering the spatial coverage of these underwater networks].The clustering algorithm for a three-dimensional grid-based underwater acoustic wireless sensor network [UAWSN] was developed by authors in [12]. The clustering algorithm first divides the complete UAWSN network into multiple grids and then further selects cluster heads by using a sleep wake up mechanism. The cluster head then gets connected to the nodes that send a response message to its broadcasted message, forming a cluster structure.The GAF algorithm [13] of TWSN was improved and made suitable for three-dimensional underwater acoustic sensor networks. The original GAF algorithm was based on a 2D structure. They named it BGAF, i.e., based on the GAF algorithm. The cluster head selection is based on residual energy and distance between acoustic sensors and surface buoys. In the case of excess energy consumption of head nodes near to buoy, the BGAF algorithm implements a transit mechanism.Another improved version of the TWSN clustering algorithm is K-means for underwater acoustic sensor networks, presented by authors in [14]. The cluster head selection is based on the depth and density of underwater nodes. The term “improved” is used as it overcomes the drawbacks of the original algorithm like high energy consumption and unstable clustering.The next comes improved LEACH algorithm, proposed by authors in [15, 16]. They have overcome the problem of collision and randomness of the original LEACH algorithm of TWSN. In former research work, nodes maintain a status updating table, were previously selected cluster head nodes are marked. This reduces the randomness in the selection of CH. The second research work on LEACH develops a slot table for each cluster head with the purpose to solve collision problems and also avoiding a scenario where cluster members receive multiple broadcast messages from multiple clusters at the same time.The clustering algorithm for three-dimensional mesh topology was proposed by authors in [17]. The algorithm aims to improve the node’s life and propagation delay. The CH selection based on the signal-to-noise ratio for UAWSN was developed in [18]. The algorithmic design was able to achieve increased fault tolerance and data reliability. The clustering algorithm to improve the network life based on the murmuration algorithm was proposed in [19]. There are various clustering schemes developed so far. Table 1 gives an overview of different clustering protocols.Table 1
Comparisons of clustering techniques studied from state-of-art.
Clustering AlgorithmsClustering StrategySpatial CoverageAdvantagesInformation-based grid clustering algorithm [12]The network is partitioned into grids, where each grid represents a cluster. The CH is located at the center. One grid can have multiple CH.3DPer grid with multiple head nodes assures recovery from failures.Fuzzy-based clustering algorithm [20]The criteria for getting elected as CH is its residual energy, its distance with sink, and quality of the link.2DImproved average energy consumption, packet delivery ratio, and delay.CBKU clustering algorithm [21]The criteria for getting elected as CH is residual energy and node-BS distance3DOvercomes previous K-means algorithm and assures balanced consumption of energy across the UWSN.TIME matrix clustering algorithm (TMCA) [22]The cluster member in columns chooses a head node in a row depending on the value of each matrix element.2DOvercomes the LEACH protocol by minimizing energy consumption.Data aggregation-based energy efficient clustering [23]Frames a priority table3DVerified clustering accompanied by data aggregation.LEACH-L clustering technique [24]Initial clusters formed using conventional LEACH3DOutperforms original LEACHS-LEACH and C-LEACH protocols [25]Only CH nodes can use slot table and control node ICH packet2DEnergy efficient as compared to LEACH.Two-tier clustering-based routing protocol [26]CH is present at two levels, and CH selection is based on the eligibility threshold2DOutperforms DUCSFuzzy C-Means clustering algorithm [27]The criteria for getting elected as CH is distance and residual energy.3DEfficient average energy consumption.LUM-HEED protocol [28]CH selection is based on residual energy and coverage area.2DSuitable for real-time applicationsEnhanced K-Means ANOVA [29]CH selection by using an enhanced K-means algorithm2DReduces the data redundancyOptical-acoustic cluster-based routing protocol [30]Selection of CH based on residual energy.2DMagnificent decrease in average energy consumption.


### 2.2. Gap Analysis

The section helps to frame out the gap or research problems discovered in the state of art. Some of the UWSN clustering strategies are discussed in Table1 is limited to technical improvements.Network Lifetime: it is required to develop a UWSN clustering program that emphasizes on reduction of protocol control data. Because reduction in control data packets will indirectly reduce the total network power consumption. This approach will gradually increase the network lifetime. This technique can be implemented during inter and intracluster communication and also between sink node and cluster heads communication.Clustering Scenarios: in state of art, UWSN clustering algorithms are developed as per the requirements of the underwater application. For example, clustering is done for 2D,3D, and 4D underwater networks, homogenous and heterogeneous networks, and clustering of acoustic nodes. But there is no algorithm developed specifically for clustering underwater optical nodes, which are densely deployed in a 3D heterogeneous network. To bridge this gap, a new clustering approach with only optical sensors needed to be designed.Battery consumption: in UWSN, re-transmission of data is required due to adversary attacks or due to a challenging underwater environment in turn it results in additional energy consumption. Hence, it is very important to focus on a reduction in data retransmissions to reduce the total energy consumption.Deepwater clustering: the ocean is generally categorized into shallow and deep water. In deep oceans, the water is crystal clear and remains steady. This property helps the clusters to remain stable despite underwater movements. But this is not the case with shallow water, due to frequently changing underwater movements (i.e., wave current), the cluster members may go out of the cluster or the cluster head may outperform. In literature, these criteria are given minimal weightage. Therefore, a clustering algorithm should be preferably implemented in deep oceans.Payload Reduction: the clustering technique followed by the data aggregation technique is key for a successful UWSN. It is very much important to embed a data compression or aggregation technique within a cluster head, to reduce the payload of transmitting signals and thus conserve transmission energy.CH Selection: it was observed that in most of the research work, CH was chosen randomly. The random selection leads to an unequal or insignificant distance between cluster members and cluster heads. The farther distance requires double transmission power and so the energy. Therefore, during CH selection, approximate equal distance must be one of the selection criteria.Energy Consumed Calculation: in the clustering technique, the network longevity is calculated based on the rate of energy dissipated by sensors. Therefore, to increase network life, residual energy calculations per clustering cycle are of prime importance.


### 2.3. Research Contributions

(1)
In our extensive work on state of art, we found that this is the first work on underwater optical sensor clustering, which improves the UWSN performance.(2)
The key contribution of the OSNR formula used for clustering of optical nodes.(3)
To evaluate the performance of our proposed method, we have compared it with some similar work.Table2 shows how we have tried to overcome the gaps in the literature survey with our small contribution to the field of underwater networks.Table 2
Mapping of a gap analysis with contributions of research work.
Gaps AnalysisContributionsNetwork lifetimeIncreased, as no extra control data packets are transmitted.Clustering scenariosOnly for optical sensorsBattery consumptionOSNR value predetermines the signal strength and hence reduces data retransmission.CH selectionOSNR, residual energy, and distance criteria perfectly select cluster head.Energy consumed calculationIn the proposed work, residual energy is calculated and not consumed energy.


### 2.4. Research Questions

Q.1 How to design a UWSN network that will sustain against challenging underwater environment?There is a need to think of a network-boosting permutation combination of different advanced underwater wireless network components (like optical sensors, AUVs, and ROVs).Q.2 How to decide on a clustering strategy?In UWSN nodes energy and sensed data are very precious. Therefore, the clustering strategy for underwater networks should be designed in a way that minimizes energy consumption and at the same time must also achieve data reliability.Q.3 Which is best, machine learning based or manual parametric clustering?In an ML-based clustering algorithm for UWSN, it is difficult to find thek-value in advance. Also, they are high in time complexity and space complexity. Hence, manual parametric clustering is a better choice for underwater networks.Q.4 How clustering will save underwater nodes energy?Clustering technology reduces the amount and distance of data transmission by selecting the CH. The cluster members only need to send collected data to CH, which greatly reduces the transmission distance and saves the energy of a single node.Q.5 How to implement UWSN?Real-time implementation in oceans or prototype model deployment in small tanks is possible. But before moving towards that, the first step should be a simulation, so as to have pre-understanding of various pros and consof the proposed network.


## 2.1. Improving Study of Clustering Techniques

The proposed UWSN design is based on the clustering of optical sensors, this is something rarely done in the state of art. After exploring the literature, it was found that underwater clustering is 99% for acoustic sensors and very less work speaks about underwater optical sensors clustering. But the fact is related work study of clustering schemes for acoustic nodes has helped us to develop a new methodology for clustering optical nodes. Hence, to enhance our knowledge of generalized clustering schemes in UWSN, we studied the literature on clustering schemes for acoustic sensors. The first observation from related work was that most clustering algorithms developed for UWSNs are manipulated versions of clustering strategies developed for TWSNs. For example, the LEACH [8], GAF [9], HEED [10], and K-means [11] algorithms originally developed for the terrestrial environments are also used in the underwater environment, but with some parametric changes as per the requirement of the underwater environment. There are mainly two reasons behind, “why TWSN clustering algorithms are not directly applicable in UWSN?”. First, the high propagation delays due to water and nonrechargeable nodes of underwater networks demand an improved, concise, and efficient clustering algorithm. Second, GPS can never be a part of UWSN. As the high-frequency radio waves cannot travel underwater below a certain depth. Therefore, a new thought toward designing of reliable and energy-effective clustering algorithm according to features of UWSN will always be appreciated.[Note: The underwater network architectures are divided into three types 2D, 3D, and 4D. The clustering strategy is developed by also considering the spatial coverage of these underwater networks].The clustering algorithm for a three-dimensional grid-based underwater acoustic wireless sensor network [UAWSN] was developed by authors in [12]. The clustering algorithm first divides the complete UAWSN network into multiple grids and then further selects cluster heads by using a sleep wake up mechanism. The cluster head then gets connected to the nodes that send a response message to its broadcasted message, forming a cluster structure.The GAF algorithm [13] of TWSN was improved and made suitable for three-dimensional underwater acoustic sensor networks. The original GAF algorithm was based on a 2D structure. They named it BGAF, i.e., based on the GAF algorithm. The cluster head selection is based on residual energy and distance between acoustic sensors and surface buoys. In the case of excess energy consumption of head nodes near to buoy, the BGAF algorithm implements a transit mechanism.Another improved version of the TWSN clustering algorithm is K-means for underwater acoustic sensor networks, presented by authors in [14]. The cluster head selection is based on the depth and density of underwater nodes. The term “improved” is used as it overcomes the drawbacks of the original algorithm like high energy consumption and unstable clustering.The next comes improved LEACH algorithm, proposed by authors in [15, 16]. They have overcome the problem of collision and randomness of the original LEACH algorithm of TWSN. In former research work, nodes maintain a status updating table, were previously selected cluster head nodes are marked. This reduces the randomness in the selection of CH. The second research work on LEACH develops a slot table for each cluster head with the purpose to solve collision problems and also avoiding a scenario where cluster members receive multiple broadcast messages from multiple clusters at the same time.The clustering algorithm for three-dimensional mesh topology was proposed by authors in [17]. The algorithm aims to improve the node’s life and propagation delay. The CH selection based on the signal-to-noise ratio for UAWSN was developed in [18]. The algorithmic design was able to achieve increased fault tolerance and data reliability. The clustering algorithm to improve the network life based on the murmuration algorithm was proposed in [19]. There are various clustering schemes developed so far. Table 1 gives an overview of different clustering protocols.Table 1
Comparisons of clustering techniques studied from state-of-art.
Clustering AlgorithmsClustering StrategySpatial CoverageAdvantagesInformation-based grid clustering algorithm [12]The network is partitioned into grids, where each grid represents a cluster. The CH is located at the center. One grid can have multiple CH.3DPer grid with multiple head nodes assures recovery from failures.Fuzzy-based clustering algorithm [20]The criteria for getting elected as CH is its residual energy, its distance with sink, and quality of the link.2DImproved average energy consumption, packet delivery ratio, and delay.CBKU clustering algorithm [21]The criteria for getting elected as CH is residual energy and node-BS distance3DOvercomes previous K-means algorithm and assures balanced consumption of energy across the UWSN.TIME matrix clustering algorithm (TMCA) [22]The cluster member in columns chooses a head node in a row depending on the value of each matrix element.2DOvercomes the LEACH protocol by minimizing energy consumption.Data aggregation-based energy efficient clustering [23]Frames a priority table3DVerified clustering accompanied by data aggregation.LEACH-L clustering technique [24]Initial clusters formed using conventional LEACH3DOutperforms original LEACHS-LEACH and C-LEACH protocols [25]Only CH nodes can use slot table and control node ICH packet2DEnergy efficient as compared to LEACH.Two-tier clustering-based routing protocol [26]CH is present at two levels, and CH selection is based on the eligibility threshold2DOutperforms DUCSFuzzy C-Means clustering algorithm [27]The criteria for getting elected as CH is distance and residual energy.3DEfficient average energy consumption.LUM-HEED protocol [28]CH selection is based on residual energy and coverage area.2DSuitable for real-time applicationsEnhanced K-Means ANOVA [29]CH selection by using an enhanced K-means algorithm2DReduces the data redundancyOptical-acoustic cluster-based routing protocol [30]Selection of CH based on residual energy.2DMagnificent decrease in average energy consumption.


## 2.2. Gap Analysis

The section helps to frame out the gap or research problems discovered in the state of art. Some of the UWSN clustering strategies are discussed in Table1 is limited to technical improvements.Network Lifetime: it is required to develop a UWSN clustering program that emphasizes on reduction of protocol control data. Because reduction in control data packets will indirectly reduce the total network power consumption. This approach will gradually increase the network lifetime. This technique can be implemented during inter and intracluster communication and also between sink node and cluster heads communication.Clustering Scenarios: in state of art, UWSN clustering algorithms are developed as per the requirements of the underwater application. For example, clustering is done for 2D,3D, and 4D underwater networks, homogenous and heterogeneous networks, and clustering of acoustic nodes. But there is no algorithm developed specifically for clustering underwater optical nodes, which are densely deployed in a 3D heterogeneous network. To bridge this gap, a new clustering approach with only optical sensors needed to be designed.Battery consumption: in UWSN, re-transmission of data is required due to adversary attacks or due to a challenging underwater environment in turn it results in additional energy consumption. Hence, it is very important to focus on a reduction in data retransmissions to reduce the total energy consumption.Deepwater clustering: the ocean is generally categorized into shallow and deep water. In deep oceans, the water is crystal clear and remains steady. This property helps the clusters to remain stable despite underwater movements. But this is not the case with shallow water, due to frequently changing underwater movements (i.e., wave current), the cluster members may go out of the cluster or the cluster head may outperform. In literature, these criteria are given minimal weightage. Therefore, a clustering algorithm should be preferably implemented in deep oceans.Payload Reduction: the clustering technique followed by the data aggregation technique is key for a successful UWSN. It is very much important to embed a data compression or aggregation technique within a cluster head, to reduce the payload of transmitting signals and thus conserve transmission energy.CH Selection: it was observed that in most of the research work, CH was chosen randomly. The random selection leads to an unequal or insignificant distance between cluster members and cluster heads. The farther distance requires double transmission power and so the energy. Therefore, during CH selection, approximate equal distance must be one of the selection criteria.Energy Consumed Calculation: in the clustering technique, the network longevity is calculated based on the rate of energy dissipated by sensors. Therefore, to increase network life, residual energy calculations per clustering cycle are of prime importance.


## 2.3. Research Contributions

(1)
In our extensive work on state of art, we found that this is the first work on underwater optical sensor clustering, which improves the UWSN performance.(2)
The key contribution of the OSNR formula used for clustering of optical nodes.(3)
To evaluate the performance of our proposed method, we have compared it with some similar work.Table2 shows how we have tried to overcome the gaps in the literature survey with our small contribution to the field of underwater networks.Table 2
Mapping of a gap analysis with contributions of research work.
Gaps AnalysisContributionsNetwork lifetimeIncreased, as no extra control data packets are transmitted.Clustering scenariosOnly for optical sensorsBattery consumptionOSNR value predetermines the signal strength and hence reduces data retransmission.CH selectionOSNR, residual energy, and distance criteria perfectly select cluster head.Energy consumed calculationIn the proposed work, residual energy is calculated and not consumed energy.


## 2.4. Research Questions

Q.1 How to design a UWSN network that will sustain against challenging underwater environment?There is a need to think of a network-boosting permutation combination of different advanced underwater wireless network components (like optical sensors, AUVs, and ROVs).Q.2 How to decide on a clustering strategy?In UWSN nodes energy and sensed data are very precious. Therefore, the clustering strategy for underwater networks should be designed in a way that minimizes energy consumption and at the same time must also achieve data reliability.Q.3 Which is best, machine learning based or manual parametric clustering?In an ML-based clustering algorithm for UWSN, it is difficult to find thek-value in advance. Also, they are high in time complexity and space complexity. Hence, manual parametric clustering is a better choice for underwater networks.Q.4 How clustering will save underwater nodes energy?Clustering technology reduces the amount and distance of data transmission by selecting the CH. The cluster members only need to send collected data to CH, which greatly reduces the transmission distance and saves the energy of a single node.Q.5 How to implement UWSN?Real-time implementation in oceans or prototype model deployment in small tanks is possible. But before moving towards that, the first step should be a simulation, so as to have pre-understanding of various pros and consof the proposed network.


## 3. Proposed System Design

The proposed model is a three-step process,(1)
ACOP UWSN architectural assumptions and notations(2)
Deployment of optical-acoustic sensor(3)
OSNR-based energy efficient clustering algorithm [OECA]


### 3.1. ACOP UWSN Architectural Assumptions and Notations

The optical nodes deployed are anchored to the sea bed, whereas acoustic nodes are mobile.All the optical and acoustic nodes are homogenous. The distance between two optical sensors will be 50 m, whereas the distance between two acoustic sensors will be 100 m.Each acoustic node is implemented with equal voltage, capacitance, frequency, amplifier power dissipation capability, electron energy dissipation, leakage current in the node’s battery, and weight factor.Each optical node is implemented with equal voltage, capacitance, frequency, amplifier power dissipation capability, photon energy dissipation, leakage current in the node’s battery, weight factor, and initial power.The location of the sink node is assumed to be fixed, and the proposed UWSN is a single sink architecture.The clustering algorithm is used to form clusters of only optical sensors and not acoustic sensors.If “dt” is data to be sensed within range “r,” then each optical sensor is responsible to sense and transmit the data to the corresponding CH. Then the CH will further relay the data to the sink node via acoustic sensors. The notations used throughout the research work are explained in Table 3.Table 3
Notations used in the research work.
SymbolMeaningPsSignal power at sinkPnNoise powerSLSignal power at source levelTLSignal loss during transmissionTnTurbulence noiseSnShipment noiseHnThermal noiseWnWind noiseEoA thresh value of energyT{}Set of nodes with energy value greater than Eo.OMOSNR maximumCn{}Set of cluster members of the nth cluster formedfFrequencyEdEuclidian distance


### 3.2. Deployment of Optical-Acoustic Sensors

The accurate choice of network topology is directly proportional to minimum energy consumption and reliable connectivity. For example, a mesh is a multihop network, whereas a star is a single hop. And clustering is said as the multimesh network. So, star and mesh topologies can be chosen for a small and single sink architecture, but for multiple-sink architecture or a network with a large sensing area, clustering is the best choice. The UWSN architecture is generally classified according to node mobility, i.e., static and dynamic. In static networks, nodes are at fixed locations, whereas in dynamic networks, the nodes move with ocean currents. Depending on communication coverage, the networks are further classified as two-dimensional and three-dimensional UWSN. The proper engineering of underwater network topology will prevent node failures. As underwater node replacement is a very costly affair, so prevention is always better than cure. Also, the energy of underwater nodes is a very precious resource as it is not rechargeable by using solar energy. Therefore, it is important to design underwater network architecture in such a way that energy is saved and also used wisely. The research work on underwater network architecture is not enough and hence needs more investigations.Following the above thought, we came up with an innovative network architecture named ACOP UWSN [Acoustic and Optical Underwater Wireless Sensor Network]. This network is the combination of two communication mediums acoustic and optical. The optical sensors are deployed just above the sea bed covering the required area. Whereas the acoustic sensors are deployed above the optical nodes layer. For example, as shown in Figure2 if we consider the ocean as a cube, then more than three-fourth portion of the cube will be covered by optical sensors, whereas the remaining area will be covered by acoustic sensors. The nodes will be equally distributed along the x-axis and y-axis.Figure 2
Cuboid representation of proposed architecture.


#### 3.2.1. ACOP UWSN Model

The ACOP UWSN is modeled asG  = (V, E) graph for network visualization where E is a communication link and V is a node in the network. The data to be sensed are denoted as “d,” which is sensed and relayed only if it is in the sensing range “Sr” of an optical sensor. The data “d” are further forwarded to the node, which is in communication range “c.” In case if “d” is out of range of “Sr,” then the node Ni is unable to sense “d.” Similarly, a node Ni can communicate with another optical node Nj, if and only if the distance (Ni, Nj) ≤ c, where c is the communicable distance between two nodes. The underwater optical sensors can communicate at a distance of one hop only. The OSNR values of each optical sensor are calculated concerning the sink node. Depending on OSNR and energy calculations, cluster heads are selected. The pictorial view of ACO-UWSN is as shown in Figure 3.Figure 3
Deployment of optical-acoustic sensor in proposed ACOP-UWSN architecture. The brown color nodes denoted as optical sensors are deployed at distance equivalent to its communication range “c”. The data denoted as “d” can only be sensed if it is within the sensing range “r” of that particular optical sensor.


#### 3.2.2. Proposed Deployment Algorithm

In the deployment phase, acoustic and optical nodes are deployed in the cuboid region. Each deployed node is then assigned a unique address, with the help of an address assigning function. This address generation is a random process and is done upon a predetermined range set in the system. The different parameters of all nodes like transmission and receiving power, initial energy, and frequency are initialized. The sink node is a surface buoy that communicates with cluster heads. The base station is a surface ship whose location is kept fixed. In the proposed algorithm (Algorithm 1), two variables are the number of clusters formed (numclust), and the number of cluster heads in the network (cluster index) are initialized to zero. The zero indicates that no cluster is still formed. The OSNR value of each node is computed concerning the buoy. The OSNR value is calculated by using equation (21).Algorithm 1: Proposed deployment algorithm.
(1)
Assigning address to all nodes using assign_address ()(2)
Set number of acoustic nodesAn = 100(3)
Set number of optical nodesOn = 50(4)
numclust = 0(5)
clustindex = 0(6)
Initialized information of all nodes such as(7)
for (i = 0; i! = On; i ++) {(8)
Node[i].ienergy ← initial energy(9)
Node[i].txpower ← transmission power of optical sensors(10)
Node[i].rxpower ← receiving power of optical sensors(11)
Node[i]. band ← bandwidth(12)
Node[i].eThres ← Threshold energy value.(13)
Node[i]. minspeed ← minimum speed of node(14)
Node[i]. maxspeed ← maximum speed of node(15)
Node[i]. position-update-interval ← position update interval(16)
} end for(17)
for (j = 0; j! = An; j++) {(18)
Node[j].jenergy ← initial energy(19)
Node[j].txpower ← transmission power of acoustic sensors(20)
Node[j].rxpower ← receiving power of acoustic sensors(21)
Node[j]. band ← bandwidth(22)
Node[j]. position-update-interval ← position update interval(23)
} end for(24)
Set BS at fixed location (250, 200, 10).


### 3.3. OSNR-Based Energy Efficient Clustering Algorithm [OECA]

The ACOP UWSN architecture model is described in Section2, for which we propose an OSNR-based clustering algorithm named OECA, which is an enhanced work of the EOCA algorithm presented in [31, 32]. As represented in Figure 4, in OECA, we built a decentralized clustering strategy for only optical sensors covering the half portion of ACOP UWSN. The word decentralized means that the optical sensors will only communicate with their neighboring nodes. Also, there will be multiple controllers (i.e., cluster heads) rather than a central controller as in the centralized approach. This approach indirectly avoids data packet collision, the number of global data packets generated by a single controller, and also lower the propagation delays in underwater wireless networks. The optical sensors will be deployed at the bottom of the ocean covering the complete monitoring area. Then the acoustic sensors are deployed from the next layer up to the surface buoy. Then for load balancing, the clustering algorithm will be embedded only in optical sensors, which sense, process, and relay data to a shorter distance, whereas the job of acoustic sensors is to only relay data to longer distances. The most critical task of clustering algorithms is the CH selection process. The technique used behind electing cluster heads is directly going to affect the network’s communication parameters like delays, network traffic, number of packet drops, and so on. It also affects the average energy consumption and stability of UWSN topology. Therefore, after studying various parameters, we decided to select the CH based on OSNR and residual energy. Now in subsequent sections, we will discuss the reason behind using OSNR as a prime clustering parameter. There will be also an in-detail explanation of the construction of the OSNR formula. In Figure 4, sensors are represented in three colors. The blue color circle represents acoustic nodes, the brown represents optical nodes, whereas the half brown and half green color nodes represent cluster heads. For example, node N [1] is denoted as cluster head because it is a node with the highest energy and highest OSNR value. The elected CH then selects its cluster members by broadcasting a HELLO message. The nodes within the communication range of CH will reply and then will be elected as cluster members.Figure 4
Diagrammatic representation of cluster member selection technique. The surface buoy will maintain a table of each node’s address and its corresponding OSNR values. Then node with the highest OSNR value and energy will be elected as the cluster head, in the above diagram it is N [1]. The elected node broadcasts the HELLO message, and in response, nodes sending message “R” are selected as cluster members, like nodes N [2], N [4], and N [3] in the above example.


#### 3.3.1. Proposed Clustering Algorithm

Algorithm 2: OECA Algorithm.
(1)
Initializing each node with parameters like initial energy, transmission power, receiving power, bandwidth, and position update interval.(2)
Declare numclust = 0,n = 0, Eo = Threshold value, ON = Optimal Number of Clusters.(3)
Calculate energy for each node, i.e.,Ei where i = 1, 2, 3, 4, …, n(4)
if (Ei ≥ Eo), then{Add that node, saym to set T {j1, j2, j3, …, jm}} (i! = n)end if(5)
for{Calculate OSNR for each node in setT {}, by using below formulaOn=OSNRn=Pke−cλz − (Tn + Sn + Hn + Wn)wheren € T,}end for(6)
Create a table of nodes “d” and their corresponding OSNR value(7)
Select the first maximum Ok value-form table and denote it as (OM)1(OM)1 = Max{On | n € T}(8)
Elect nodeb as Cluster Head and broadcast HELLO message(9)
The nodes which send response messages are considered NCHC1 = {x |b − x| ≤ 30 meters}Suppose |C1| = r1(10)
Deleter1 nodes of Cn from T(11)
increment numclust ++(12)
if (numclust! = ON)Repeat from step 3, untilT =  null.(13)
Proceed to Data sensing


#### 3.3.2. Mathematical Modelling

(i)
Cluster Head (CH) SelectionLet 1, 2, 3, …,n be n nodesLetEi, i = 1, 2, 3, …, n be the energy of ith nodesAndEo be the threshold value of energyLetEj1, Ej2, Ej3, …, Ejm, be m nodes having energy greater than or equal to Eo.LetT = {j1, j2, j3, …, jm}Therefore, find the OSNR of the nodes which are inT by the following formulaOn=OSNRn=Poe−cλz − (Tn + Sn + Hn + Wn) for all n € TNow, select the node having maximum value ofOnorOSNRnLet (OM)1 = Max{On | k € T}Suppose (OM)1 corresponds to node b € TTherefore, nodeb is the cluster head of the first clusterC1 = {x |b − x| ≤ 50 meters}Suppose |C1| = r1Now deleter1 nodes of C1 from TT = T − C1Apply the same procedure for nodes inT1, T2, T3, …, TnAnd find (OM)2, (OM)3, …, (OM)n(ii)
Cluster Member (CM) Selection.Continue the same procedure until we get theTp =  ∅Ifz be any the node in Cx∩Cy≠∅,Then find the distance betweenz and cluster heads of Cx and Cy say nodes x and y, respectively, as shown in Figure 5,These are the following two cases,Figure 5
Venn diagram of two overlapping clustersCx and Cy.Case 1.
If |x-z| > |y-z|, then z € Cy, so remove z from CxCase 2.
If |x-z| < |y-z|, then z € Cx, so remove z from CyCase 3.
If nodez is temporary in an inactive state, then it will not be considered for communication as a cluster member of any cluster.
Note: If the energy or OSNR of two nodes is the same, then select the first node whose distance from the base station is minimum.


#### 3.3.3. Algorithmic Description

An OSNR value defines a greater value of signal power of an optical wave about the noise power. Thus, the higher OSNR value allows maximum data rate and minimum data retransmissions and thus a good network performance. An OSNR value higher than 40 dB is considered excellent, whereas an OSNR lower than 15 dB can result in a slow, unreliable connection [33]. The most important point is that the node with the greatest OSNR value is also an energy saver node.In other words, we say that impaired blood pressure or temperature values speak about human body health and help in predetermining the health complications in the same way OSNR values help in predetermining the healthy optical nodes and networking complications. Here healthy node means it can achieve reliability with higher bandwidth.The importance of OSNR value suggests selecting the CH based on OSNR values. The node that has the highest OSNR value among all the nodes will be elected as CH. But this is not enough!Further, we found a scenario where the node with 15 dB OSNR was the only node with the highest OSNR value. Then it could not have been a good choice to select that node as CH may lead to data loss, increased propagation delays, and so on. This will also eventually lead to increased energy consumption and also irrelevant loss of node’s energy. With described situation, it is important to crosscheck the best node with some other network parameter. In UWSN, the biggest fight is for energy, and so our second clustering parameter is residual energy. Therefore, a node with the combination of the highest OSNR among all nodes and having residual energy higher than a threshold value will be elected as CH.In the next section, there is a complete explanation of other detailing like whether to calculate residual energy for all nodes or how to calculate OSNR, how to select noncluster members or what are number of CH allowed, and so on.(i)
Optical Signal-to-Noise Ratio [OSNR]:Definition: “Optical signal-to-noise ratio is a measure of optical signal power to the noise power in the underwater optical channel.”Example: OSNR is one of the key performance indicators of the health of the fastest optical submarine network’s reliability and performance.The OSNR may change due to transmission power changes or high amplifier noise levels due to the aging of the in-service underwater network. Such changes make it necessary to test OSNR as part of network maintenance.Importance of OSNR(1)
OSNR is one of the important performance parameters while designing an underwater network as it helps in deciding the communication capacity of a node.(2)
Periodical measuring of OSNR can help underwater application stakeholders, vendors, and network engineers address network failures before network breakdown occurs. Therefore, OSNR indirectly helps in minimizing network downtime.(3)
Network outage can occur due for multiple reasons, and OSNR calculations allow quick localization of defective optical nodes. Hence, with OSNR values, it is possible to predetermine a node that can ensure data reliability. In such a case, a node with a higher OSNR value is then capable of becoming a cluster head.(ii)
Construction of OSNR formulaIn general, the OSNR is the ratio between the useful optical signal (i.e., carrying data) to unwanted background noise. It is represented as a ratio in [34], which is shown in the below formula,(1)OSNR=PsignalPnoise.Calculating SNR ratio on a logarithmic basis using decibels as in [34], which is shown in equation (1):(2)OSNRdB=10dB.log10PsignalPnoise.If signal and noise powers are already expressed in decibels, then the formula can be further simplified as in [34]. Therefore, equation (2) can be rewritten as equation (3),(3)OSNRdB=PsignaldB−PnoisedB.wherePsignal represents meaningful information in the signal received and Pnoise represents the unwanted background noise in dB.FurtherPsignal is computed by taking away the transmission loss from the signal generated at the source level [35], as shown in equation (4)(4)Psignal=SL−TL.The direct implication of existing OSNR formulas of TWSN into UWSNs is not practically possible. Therefore, deep studies and rethinking of formula construction were required. To construct a formula for OSNR, we first studied the physiology of optical waves propagating in the underwater environment.(1) Study of Basic Properties of the Optical Wave as an Underwater Communication Medium. According to light transfer in natural water [36], the properties of optical waves are recognized into two types,(i)
Inherent Optical Properties (IOP): the properties of the optical signal that only depend on water (i.e., transmission medium), and more precisely on composition and particles present within water [37] are known as IOPs. They are independent of the characteristic of optical wave sources like LED, Blue Green, laser beam, and many more. The three major IOPs of the underwater medium are absorption coefficient, scattering coefficient, and attenuation coefficient [38].(ii)
Apparent Optical Properties (AOP): the properties of the optical signal that not only depend on the transmission medium like IOPs but also on collimation and diffusion are known as AOPs. The most important AOPs are reflection, radiation, and irradiation [37, 39].In an underwater optical communication system, link cost is determined by IOPs, whereas ambient light levels are calculated by AOPs. However, IOPs have a strong impact on the optical channel, and thus will focus more on IOPs (absorption and scattering) to derive our OSNR formula.Absorption: it is a phenomenon in which photons present in the optical wave are absorbed by water molecules and marine hydrosols. Absorption is a type of photosynthesis in which a photon loses its energy and gets converted into heat and chemicals [40]. It is also one of the major IOPs that determines light attenuation. Absorption blocks the communication range of optical waves by forcing emitted light beam to continuously decrease its total propagation energy.Scattering: for understanding, imagine a scenario where an optical beam strikes an object due to which it undergoes a reflection or refraction process. Such a process of optical wave deflection from its original path caused due to change in refractive index is known as scattering [41]. Scattering scatters the photons in multiple different random directions in such a way that some parts of the photon are not received at the receiver end, whereas other parts are delayed due to multipath propagation. These will lead to the degradation of the OSNR value of UWSN. Therefore, scattering results in depletion of received signal strength, and the bonus multipath effect causes intersymbol interference phenomena and timing jitter.Absorption and scattering coefficients are derived based on an IOPs geometric model proposed by authors in [37]. The model is replicated in Figure 6.Figure 6
IOPs Geometric Model [37]. The incident optical beam power is OPI, whereas the absorbed and scattered optical beam power are denoted as OPA and OPs. The optical beam travelled to its destination is denoted as OPt.There are some assumptions made in the above model that are ΔV is a volume of water having thickness ΔD and the wavelength of optical light is λ. The power of the incident optical beam is denoted as OPI. The part of the incident optical beam is absorbed and is denoted by OPA, whereas another part of the incident optical beam is scattered, i.e., OPs. The remaining incident optical beam will travel as expected, which is denoted as OPt.Now, OPA is a(λ), i.e., absorption coefficient or also said as absorbance factor. And OPs are b(λ), which is the scattering coefficient or scatterance factor.By using the law of energy conservation [37, 40, 41], we can say that(5)OPI=OPA+OPs+OPt.From above equation (5), the optical-absorbance A(λ) can be defined as the ratio of absorption power [OPA] to incident power of optical wave OPI. Similarly, optical- scatterance B(λ) is defined as the ratio of optical waves scattering power OPs to incident power OPI.(6)Aλ=OPAOPI,Bλ=OPsOPI.Further, the coefficients of absorption and scattering are then calculated by setting a limit to the thickness of water, denoted as ∆D [37, 38].(7)aλ=limΔD⟶0ΔAλΔD,bλ=limΔD⟶0ΔBλΔD.According to Haltrin’s model [40], the attenuation of optical waves in an underwater environment can be described as the coefficient of attenuation denoted by c(λ), which is the total loss of optical wave due to absorption and scattering expressed as follows:Total attenuation = Absorption coefficient + Scattering coefficient(8)cλ=aλ+bλ.(a) Absorption Coefficient a(λ). Now, further, the coefficient of absorption can be represented as the addition of four absorption factors [37, 42].(9)aλ=apλ+adλ+awλ+aCDOMλ,where(10)aCDOMλ=afλ+ahλ.The underwater medium is the composition of almost eighty different particles dissolved in pure seawater [43], with various different concentrations. In equation (9), it is shown that optical beam absorbance is due to factors like dissolved salts in pure water is denoted as awλ, apλ denotes absorbance due to phytoplankton, adλ represent absorbance of light due to detritus, and aCDOMλ is the absorbance due to color dissolved organic matters (CDOM).In pure seawater, the absorption is mainly due to two sources, molecules of water and the presence of salts in seawater like NaCl, MgCl2, and KCl [44]. In pure seawater, absorption coefficient is high as compared to the scattering coefficient. The presence of chlorophyll in corals undergoes photosynthesis which is the main reason for the absorption of light due to phytoplankton. The absorption effects are different due to different phytoplankton species. The higher absorption is shown by apλ in the blue-red region (400–500 nm) and scatters green light. The type of water (pure, coastal, clear, harbor murky water) plays an important role in estimating concentration of chlorophyll and subsequently specific geographic location-based absorption and scattering effect as shown in Table 4. The detritus contributes to both absorption and scattering, it includes organic matters like bacteria, viruses, zooplankton and inorganic particle like sand and metal oxides [45]. The CDOM are fulvic acid afλ and humic acids ahλ mainly in blue (420 nm–450 nm) [46] and ultraviolet wavelengths.Table 4
Simulation Parameter settings.
Area of sensing field500 × 500 × 500Number of acoustic nodesAn100Number of optical nodesOn50Simulation time200 sBandwidth3 × 108Initial energy of optical nodes100 JThreshold energy for optical nodesTe0.1Antenna typeOmnidirectionalQueue typeDropTail QueueMinimum speed of node0.2Maximum speed of node3Speed0.5Physical layer protocolUnderwaterPhyMAC layer protocolBroadcastMacRouting protocolOECATransmission power ofOn2.0 WReceiving power ofOn0.75 WIdle power ofOn0.008Queue length50Packet size50Routing control packet size20Coverage area100Minimum delay50 usDelay25 usWind (m/s)5Shipping noise (dB)0.2Ee50 nJ/bitEa1.3 × 10−15 J/bit/m4Note. As we are performing clustering algorithm only for optical sensors so focusing on simulation parameters of optical sensors and not of acoustic one.(b) Scattering Coefficient b(λ). Scattering depends on the density of matter and various particles present underwater. It is not dependent on wavelength as in absorption. Therefore, scattering effect is more in coastal areas as compared to open oceans [37]. The b(λ) for underwater optical beam propagation is the addition of three different scattering factors,(11)bλ=bpλ+bwλ+bdλ,where bpλ denotes the scattering due to phytoplankton, bwλ is the scattering due to pure seawater, and bdλ denotes scattering due to detritus. Among the total scattering effect, more than 40% is due to phytoplankton and detritus. In practicality, the exact calculations of bλ are highly dependent on the density of phytoplankton and detritus [47].As per the Beer-Lambert Law, [48] provides simplified formula to describe optical beam attenuation or loss in an underwater medium. The Beer-law formulates the transmitted power of a signal as(12)Pt=Po.TLλ,z,where the transmission loss (TL) of the optical beam propagating in the underwater medium is expressed as a function decaying exponentially with z as path length. (13)TLλ,z=e−Cλz,(14)Pt=Poe−Cλz,where Pt is the transmitted power of the optical beam, z is the distance travelled by optical light, from above equation (8), c(λ) is the attenuation coefficient, TLλ,z is the transmission loss, and Po is the power of optical source/received signal power/source level in milliwatt (mW).From equation (4) and (14), we can say that(15)Psignal=Pt,we can denote Psignal as Pt that is the power received at the receiver. From equation (15), we can rewrite the equation (3) as (16)OSNR=Pt−Pnoise,OSNR=Poe−Cλz–Pnoise.The value ofc(λ) changes with water types. In coastal water, optical beam mostly suffers from scattering, whereas in pure ocean water absorption is the main limiting factor. This is because the pure ocean waters have a high concentration of dissolved elements that results in scattering, whereas minerals, detritus, and plankton present in coastal water are prominent sources of absorption. The harbour’ murky water is composition of in-suspension matters that highly attenuate the optical beam.(2) Noise [Pnoise]. Oceans are pandemonium of different background noises. The noise generated in underwater environment is mainly due to two main sources, natural and man-made. The noise from crashing waves, motion of water molecules, bubbles, thermal noise, heavy rain, undersea volcanoes and earthquakes, and marine life’s (i.e., sound generated by humpback whales, toadfish, snapping shrimp, and dolphins) are all natural sources of sound. The noise generated by human activities is also an important source of background noise. The primary sources of noise generated through human activities are fishing activity, military sonars, surface-ships, oil drilling, and many other functional industrial projects in deep sea [49]. Underwater noise is function of time, depth, and wavelength. The noise sources can be further classified depending on frequency band of sound. If range is 20–500 Hz, then source of noise is shipping activity, in 500–100,000 Hz, the primary source of noise is bubbles due to splashing waves. The thermal noise is above 100 kHz, and its source is Brownian motion of water molecules. The harbour sides are noisier due to human activity as compare to deep sea. The solar is another addition to background noise that restricts the performance of optical wave operating at a depth of 10 m. And at larger depth, the noise sources are biological luminance. The noise is also generated by seismic movements of water that include wind, tides, and ocean currents. The mathematical description of noise can be given by combining the above noise terminology into an aggregated underwater noise as expressed in equation (17) referred from reference [50].(17)Pnoise=10logTn+Sn+Hn+Wn,where Tn denotes seismic movement, Sn is for shipping activity, Hn represents wind in m/sec, and Wn denotes molecular agitation.Turbulence Noise: the large or small form of water currents that are generally irregular and random and have a tendency to generate background noise in different ways, which is popularly known as turbulence noise. The common sources of turbulence noise in underwater environment are hydrophones and water currents resulting to pressure changes. Turbulence noise [51] Tnf is formulated in the equation (18), (18)10logfTn=17−30logf.Shipping Noise: the human-interfered shipping activities and traffic, also creates a background noise, known as shipping noise. The ambient noise level in 100 Hz is predominant source of shipping noise [50].It is described by the formula in equation (19)(19)10logSnf=40+20∗s−0.5+26logf−60logf+0.03,where the value of shipping activity factor (s) is between 0 and 1. If s is 0, then it indicates low shipping activity, whereas if s is 1, then it represents high shipping activity.Thermal Noise [51]: the other name for thermal noise is molecular agitation, and it is another source of background noise in underwater environment. There is a limit to the sensitivity of hydrophone for high frequencies due to temperature of water molecules. It can be calculated as shown in equation (21), (20)10logWnf=−15+20logf,where w is the speed of wind measure in m/s.Wave Noise: the action of formation of wave due to wind on sea surface is also one of the sources of noise, most commonly known as wave noise. The wind speed determines the level of noise over the hydrophone. The waves are mostly turbulent on sea surface, due to which each wave travels at different speeds depending on their different wavelengths. The wave with long wavelength will travel at great speed which then induce pressure waves in oceans. Hence, the noise generated by wave formation is directly proportional to wind speed and frequency. These are properties of wave noise [52] depending on which the equation (19) is formulated as follows: (21)10logHnf=50+7.5w0.5+20logf−40logf+0.4.Finally, we can construct our OSNR formula from equation (16) and (17),(22)OSNRdB=Pte−Cλz–10logTn+Sn+Hn+Wn.The above formula will be further used for finding appropriate cluster heads.(iii)
Energy model for ACOP UWSNThe maximum energy of underwater node is used in data transmission and data reception that directly decreases the amount of remaining energy. In case, if network is implementing selective data forwarding strategy, then underwater nodes require the processing of data. Thus, residual energy will now decrease by sum of transmission energy (ET) and processing energy (Epro). The residual energy calculations are required to perform after each cycle of transmitting and receiving data. The nodes energy consumption is mainly divided into three types [53],(1)
Transmission energy (ET-CH)(2)
Reception energy (ER-CH)(3)
Processing energy (Epro-CH)The below are formulas for energy calculation of cluster head [53],(23)ET−CH=h∗b∗Ee+b1∗d∗Ea,ER−CH=h∗b1∗Ee,Epro−CH=h∗Epro−N.whereEe is the energy consumed by the transmitter and receiver; b1 = b ∗  is the number of cluster members; d is the distance between transmitter and receiver; and Epro-N is the processing/aggregation b bit packet.The below are formulas used for energy calculation of cluster members [53],(24)ET−CM=b∗Ee+b∗d∗Ea,ER−CM=b∗Ee,Epro−CM=b∗Nc∗CavgVsup+b∗Vsup∗I0∗eVsupNc∗Vt∗Ncf,whereNc is the number of clock cycles per task, Cavg is the average capacitance switched per cycle, and I0 is the leakage current.


## 3.1. ACOP UWSN Architectural Assumptions and Notations

The optical nodes deployed are anchored to the sea bed, whereas acoustic nodes are mobile.All the optical and acoustic nodes are homogenous. The distance between two optical sensors will be 50 m, whereas the distance between two acoustic sensors will be 100 m.Each acoustic node is implemented with equal voltage, capacitance, frequency, amplifier power dissipation capability, electron energy dissipation, leakage current in the node’s battery, and weight factor.Each optical node is implemented with equal voltage, capacitance, frequency, amplifier power dissipation capability, photon energy dissipation, leakage current in the node’s battery, weight factor, and initial power.The location of the sink node is assumed to be fixed, and the proposed UWSN is a single sink architecture.The clustering algorithm is used to form clusters of only optical sensors and not acoustic sensors.If “dt” is data to be sensed within range “r,” then each optical sensor is responsible to sense and transmit the data to the corresponding CH. Then the CH will further relay the data to the sink node via acoustic sensors. The notations used throughout the research work are explained in Table 3.Table 3
Notations used in the research work.
SymbolMeaningPsSignal power at sinkPnNoise powerSLSignal power at source levelTLSignal loss during transmissionTnTurbulence noiseSnShipment noiseHnThermal noiseWnWind noiseEoA thresh value of energyT{}Set of nodes with energy value greater than Eo.OMOSNR maximumCn{}Set of cluster members of the nth cluster formedfFrequencyEdEuclidian distance


## 3.2. Deployment of Optical-Acoustic Sensors

The accurate choice of network topology is directly proportional to minimum energy consumption and reliable connectivity. For example, a mesh is a multihop network, whereas a star is a single hop. And clustering is said as the multimesh network. So, star and mesh topologies can be chosen for a small and single sink architecture, but for multiple-sink architecture or a network with a large sensing area, clustering is the best choice. The UWSN architecture is generally classified according to node mobility, i.e., static and dynamic. In static networks, nodes are at fixed locations, whereas in dynamic networks, the nodes move with ocean currents. Depending on communication coverage, the networks are further classified as two-dimensional and three-dimensional UWSN. The proper engineering of underwater network topology will prevent node failures. As underwater node replacement is a very costly affair, so prevention is always better than cure. Also, the energy of underwater nodes is a very precious resource as it is not rechargeable by using solar energy. Therefore, it is important to design underwater network architecture in such a way that energy is saved and also used wisely. The research work on underwater network architecture is not enough and hence needs more investigations.Following the above thought, we came up with an innovative network architecture named ACOP UWSN [Acoustic and Optical Underwater Wireless Sensor Network]. This network is the combination of two communication mediums acoustic and optical. The optical sensors are deployed just above the sea bed covering the required area. Whereas the acoustic sensors are deployed above the optical nodes layer. For example, as shown in Figure2 if we consider the ocean as a cube, then more than three-fourth portion of the cube will be covered by optical sensors, whereas the remaining area will be covered by acoustic sensors. The nodes will be equally distributed along the x-axis and y-axis.Figure 2
Cuboid representation of proposed architecture.


### 3.2.1. ACOP UWSN Model

The ACOP UWSN is modeled asG  = (V, E) graph for network visualization where E is a communication link and V is a node in the network. The data to be sensed are denoted as “d,” which is sensed and relayed only if it is in the sensing range “Sr” of an optical sensor. The data “d” are further forwarded to the node, which is in communication range “c.” In case if “d” is out of range of “Sr,” then the node Ni is unable to sense “d.” Similarly, a node Ni can communicate with another optical node Nj, if and only if the distance (Ni, Nj) ≤ c, where c is the communicable distance between two nodes. The underwater optical sensors can communicate at a distance of one hop only. The OSNR values of each optical sensor are calculated concerning the sink node. Depending on OSNR and energy calculations, cluster heads are selected. The pictorial view of ACO-UWSN is as shown in Figure 3.Figure 3
Deployment of optical-acoustic sensor in proposed ACOP-UWSN architecture. The brown color nodes denoted as optical sensors are deployed at distance equivalent to its communication range “c”. The data denoted as “d” can only be sensed if it is within the sensing range “r” of that particular optical sensor.


### 3.2.2. Proposed Deployment Algorithm

In the deployment phase, acoustic and optical nodes are deployed in the cuboid region. Each deployed node is then assigned a unique address, with the help of an address assigning function. This address generation is a random process and is done upon a predetermined range set in the system. The different parameters of all nodes like transmission and receiving power, initial energy, and frequency are initialized. The sink node is a surface buoy that communicates with cluster heads. The base station is a surface ship whose location is kept fixed. In the proposed algorithm (Algorithm 1), two variables are the number of clusters formed (numclust), and the number of cluster heads in the network (cluster index) are initialized to zero. The zero indicates that no cluster is still formed. The OSNR value of each node is computed concerning the buoy. The OSNR value is calculated by using equation (21).Algorithm 1: Proposed deployment algorithm.
(1)
Assigning address to all nodes using assign_address ()(2)
Set number of acoustic nodesAn = 100(3)
Set number of optical nodesOn = 50(4)
numclust = 0(5)
clustindex = 0(6)
Initialized information of all nodes such as(7)
for (i = 0; i! = On; i ++) {(8)
Node[i].ienergy ← initial energy(9)
Node[i].txpower ← transmission power of optical sensors(10)
Node[i].rxpower ← receiving power of optical sensors(11)
Node[i]. band ← bandwidth(12)
Node[i].eThres ← Threshold energy value.(13)
Node[i]. minspeed ← minimum speed of node(14)
Node[i]. maxspeed ← maximum speed of node(15)
Node[i]. position-update-interval ← position update interval(16)
} end for(17)
for (j = 0; j! = An; j++) {(18)
Node[j].jenergy ← initial energy(19)
Node[j].txpower ← transmission power of acoustic sensors(20)
Node[j].rxpower ← receiving power of acoustic sensors(21)
Node[j]. band ← bandwidth(22)
Node[j]. position-update-interval ← position update interval(23)
} end for(24)
Set BS at fixed location (250, 200, 10).


## 3.2.1. ACOP UWSN Model

The ACOP UWSN is modeled asG  = (V, E) graph for network visualization where E is a communication link and V is a node in the network. The data to be sensed are denoted as “d,” which is sensed and relayed only if it is in the sensing range “Sr” of an optical sensor. The data “d” are further forwarded to the node, which is in communication range “c.” In case if “d” is out of range of “Sr,” then the node Ni is unable to sense “d.” Similarly, a node Ni can communicate with another optical node Nj, if and only if the distance (Ni, Nj) ≤ c, where c is the communicable distance between two nodes. The underwater optical sensors can communicate at a distance of one hop only. The OSNR values of each optical sensor are calculated concerning the sink node. Depending on OSNR and energy calculations, cluster heads are selected. The pictorial view of ACO-UWSN is as shown in Figure 3.Figure 3
Deployment of optical-acoustic sensor in proposed ACOP-UWSN architecture. The brown color nodes denoted as optical sensors are deployed at distance equivalent to its communication range “c”. The data denoted as “d” can only be sensed if it is within the sensing range “r” of that particular optical sensor.


## 3.2.2. Proposed Deployment Algorithm

In the deployment phase, acoustic and optical nodes are deployed in the cuboid region. Each deployed node is then assigned a unique address, with the help of an address assigning function. This address generation is a random process and is done upon a predetermined range set in the system. The different parameters of all nodes like transmission and receiving power, initial energy, and frequency are initialized. The sink node is a surface buoy that communicates with cluster heads. The base station is a surface ship whose location is kept fixed. In the proposed algorithm (Algorithm 1), two variables are the number of clusters formed (numclust), and the number of cluster heads in the network (cluster index) are initialized to zero. The zero indicates that no cluster is still formed. The OSNR value of each node is computed concerning the buoy. The OSNR value is calculated by using equation (21).Algorithm 1: Proposed deployment algorithm.
(1)
Assigning address to all nodes using assign_address ()(2)
Set number of acoustic nodesAn = 100(3)
Set number of optical nodesOn = 50(4)
numclust = 0(5)
clustindex = 0(6)
Initialized information of all nodes such as(7)
for (i = 0; i! = On; i ++) {(8)
Node[i].ienergy ← initial energy(9)
Node[i].txpower ← transmission power of optical sensors(10)
Node[i].rxpower ← receiving power of optical sensors(11)
Node[i]. band ← bandwidth(12)
Node[i].eThres ← Threshold energy value.(13)
Node[i]. minspeed ← minimum speed of node(14)
Node[i]. maxspeed ← maximum speed of node(15)
Node[i]. position-update-interval ← position update interval(16)
} end for(17)
for (j = 0; j! = An; j++) {(18)
Node[j].jenergy ← initial energy(19)
Node[j].txpower ← transmission power of acoustic sensors(20)
Node[j].rxpower ← receiving power of acoustic sensors(21)
Node[j]. band ← bandwidth(22)
Node[j]. position-update-interval ← position update interval(23)
} end for(24)
Set BS at fixed location (250, 200, 10).


## 3.3. OSNR-Based Energy Efficient Clustering Algorithm [OECA]

The ACOP UWSN architecture model is described in Section2, for which we propose an OSNR-based clustering algorithm named OECA, which is an enhanced work of the EOCA algorithm presented in [31, 32]. As represented in Figure 4, in OECA, we built a decentralized clustering strategy for only optical sensors covering the half portion of ACOP UWSN. The word decentralized means that the optical sensors will only communicate with their neighboring nodes. Also, there will be multiple controllers (i.e., cluster heads) rather than a central controller as in the centralized approach. This approach indirectly avoids data packet collision, the number of global data packets generated by a single controller, and also lower the propagation delays in underwater wireless networks. The optical sensors will be deployed at the bottom of the ocean covering the complete monitoring area. Then the acoustic sensors are deployed from the next layer up to the surface buoy. Then for load balancing, the clustering algorithm will be embedded only in optical sensors, which sense, process, and relay data to a shorter distance, whereas the job of acoustic sensors is to only relay data to longer distances. The most critical task of clustering algorithms is the CH selection process. The technique used behind electing cluster heads is directly going to affect the network’s communication parameters like delays, network traffic, number of packet drops, and so on. It also affects the average energy consumption and stability of UWSN topology. Therefore, after studying various parameters, we decided to select the CH based on OSNR and residual energy. Now in subsequent sections, we will discuss the reason behind using OSNR as a prime clustering parameter. There will be also an in-detail explanation of the construction of the OSNR formula. In Figure 4, sensors are represented in three colors. The blue color circle represents acoustic nodes, the brown represents optical nodes, whereas the half brown and half green color nodes represent cluster heads. For example, node N [1] is denoted as cluster head because it is a node with the highest energy and highest OSNR value. The elected CH then selects its cluster members by broadcasting a HELLO message. The nodes within the communication range of CH will reply and then will be elected as cluster members.Figure 4
Diagrammatic representation of cluster member selection technique. The surface buoy will maintain a table of each node’s address and its corresponding OSNR values. Then node with the highest OSNR value and energy will be elected as the cluster head, in the above diagram it is N [1]. The elected node broadcasts the HELLO message, and in response, nodes sending message “R” are selected as cluster members, like nodes N [2], N [4], and N [3] in the above example.


### 3.3.1. Proposed Clustering Algorithm

Algorithm 2: OECA Algorithm.
(1)
Initializing each node with parameters like initial energy, transmission power, receiving power, bandwidth, and position update interval.(2)
Declare numclust = 0,n = 0, Eo = Threshold value, ON = Optimal Number of Clusters.(3)
Calculate energy for each node, i.e.,Ei where i = 1, 2, 3, 4, …, n(4)
if (Ei ≥ Eo), then{Add that node, saym to set T {j1, j2, j3, …, jm}} (i! = n)end if(5)
for{Calculate OSNR for each node in setT {}, by using below formulaOn=OSNRn=Pke−cλz − (Tn + Sn + Hn + Wn)wheren € T,}end for(6)
Create a table of nodes “d” and their corresponding OSNR value(7)
Select the first maximum Ok value-form table and denote it as (OM)1(OM)1 = Max{On | n € T}(8)
Elect nodeb as Cluster Head and broadcast HELLO message(9)
The nodes which send response messages are considered NCHC1 = {x |b − x| ≤ 30 meters}Suppose |C1| = r1(10)
Deleter1 nodes of Cn from T(11)
increment numclust ++(12)
if (numclust! = ON)Repeat from step 3, untilT =  null.(13)
Proceed to Data sensing


### 3.3.2. Mathematical Modelling

(i)
Cluster Head (CH) SelectionLet 1, 2, 3, …,n be n nodesLetEi, i = 1, 2, 3, …, n be the energy of ith nodesAndEo be the threshold value of energyLetEj1, Ej2, Ej3, …, Ejm, be m nodes having energy greater than or equal to Eo.LetT = {j1, j2, j3, …, jm}Therefore, find the OSNR of the nodes which are inT by the following formulaOn=OSNRn=Poe−cλz − (Tn + Sn + Hn + Wn) for all n € TNow, select the node having maximum value ofOnorOSNRnLet (OM)1 = Max{On | k € T}Suppose (OM)1 corresponds to node b € TTherefore, nodeb is the cluster head of the first clusterC1 = {x |b − x| ≤ 50 meters}Suppose |C1| = r1Now deleter1 nodes of C1 from TT = T − C1Apply the same procedure for nodes inT1, T2, T3, …, TnAnd find (OM)2, (OM)3, …, (OM)n(ii)
Cluster Member (CM) Selection.Continue the same procedure until we get theTp =  ∅Ifz be any the node in Cx∩Cy≠∅,Then find the distance betweenz and cluster heads of Cx and Cy say nodes x and y, respectively, as shown in Figure 5,These are the following two cases,Figure 5
Venn diagram of two overlapping clustersCx and Cy.Case 1.
If |x-z| > |y-z|, then z € Cy, so remove z from CxCase 2.
If |x-z| < |y-z|, then z € Cx, so remove z from CyCase 3.
If nodez is temporary in an inactive state, then it will not be considered for communication as a cluster member of any cluster.
Note: If the energy or OSNR of two nodes is the same, then select the first node whose distance from the base station is minimum.


### 3.3.3. Algorithmic Description

An OSNR value defines a greater value of signal power of an optical wave about the noise power. Thus, the higher OSNR value allows maximum data rate and minimum data retransmissions and thus a good network performance. An OSNR value higher than 40 dB is considered excellent, whereas an OSNR lower than 15 dB can result in a slow, unreliable connection [33]. The most important point is that the node with the greatest OSNR value is also an energy saver node.In other words, we say that impaired blood pressure or temperature values speak about human body health and help in predetermining the health complications in the same way OSNR values help in predetermining the healthy optical nodes and networking complications. Here healthy node means it can achieve reliability with higher bandwidth.The importance of OSNR value suggests selecting the CH based on OSNR values. The node that has the highest OSNR value among all the nodes will be elected as CH. But this is not enough!Further, we found a scenario where the node with 15 dB OSNR was the only node with the highest OSNR value. Then it could not have been a good choice to select that node as CH may lead to data loss, increased propagation delays, and so on. This will also eventually lead to increased energy consumption and also irrelevant loss of node’s energy. With described situation, it is important to crosscheck the best node with some other network parameter. In UWSN, the biggest fight is for energy, and so our second clustering parameter is residual energy. Therefore, a node with the combination of the highest OSNR among all nodes and having residual energy higher than a threshold value will be elected as CH.In the next section, there is a complete explanation of other detailing like whether to calculate residual energy for all nodes or how to calculate OSNR, how to select noncluster members or what are number of CH allowed, and so on.(i)
Optical Signal-to-Noise Ratio [OSNR]:Definition: “Optical signal-to-noise ratio is a measure of optical signal power to the noise power in the underwater optical channel.”Example: OSNR is one of the key performance indicators of the health of the fastest optical submarine network’s reliability and performance.The OSNR may change due to transmission power changes or high amplifier noise levels due to the aging of the in-service underwater network. Such changes make it necessary to test OSNR as part of network maintenance.Importance of OSNR(1)
OSNR is one of the important performance parameters while designing an underwater network as it helps in deciding the communication capacity of a node.(2)
Periodical measuring of OSNR can help underwater application stakeholders, vendors, and network engineers address network failures before network breakdown occurs. Therefore, OSNR indirectly helps in minimizing network downtime.(3)
Network outage can occur due for multiple reasons, and OSNR calculations allow quick localization of defective optical nodes. Hence, with OSNR values, it is possible to predetermine a node that can ensure data reliability. In such a case, a node with a higher OSNR value is then capable of becoming a cluster head.(ii)
Construction of OSNR formulaIn general, the OSNR is the ratio between the useful optical signal (i.e., carrying data) to unwanted background noise. It is represented as a ratio in [34], which is shown in the below formula,(1)OSNR=PsignalPnoise.Calculating SNR ratio on a logarithmic basis using decibels as in [34], which is shown in equation (1):(2)OSNRdB=10dB.log10PsignalPnoise.If signal and noise powers are already expressed in decibels, then the formula can be further simplified as in [34]. Therefore, equation (2) can be rewritten as equation (3),(3)OSNRdB=PsignaldB−PnoisedB.wherePsignal represents meaningful information in the signal received and Pnoise represents the unwanted background noise in dB.FurtherPsignal is computed by taking away the transmission loss from the signal generated at the source level [35], as shown in equation (4)(4)Psignal=SL−TL.The direct implication of existing OSNR formulas of TWSN into UWSNs is not practically possible. Therefore, deep studies and rethinking of formula construction were required. To construct a formula for OSNR, we first studied the physiology of optical waves propagating in the underwater environment.(1) Study of Basic Properties of the Optical Wave as an Underwater Communication Medium. According to light transfer in natural water [36], the properties of optical waves are recognized into two types,(i)
Inherent Optical Properties (IOP): the properties of the optical signal that only depend on water (i.e., transmission medium), and more precisely on composition and particles present within water [37] are known as IOPs. They are independent of the characteristic of optical wave sources like LED, Blue Green, laser beam, and many more. The three major IOPs of the underwater medium are absorption coefficient, scattering coefficient, and attenuation coefficient [38].(ii)
Apparent Optical Properties (AOP): the properties of the optical signal that not only depend on the transmission medium like IOPs but also on collimation and diffusion are known as AOPs. The most important AOPs are reflection, radiation, and irradiation [37, 39].In an underwater optical communication system, link cost is determined by IOPs, whereas ambient light levels are calculated by AOPs. However, IOPs have a strong impact on the optical channel, and thus will focus more on IOPs (absorption and scattering) to derive our OSNR formula.Absorption: it is a phenomenon in which photons present in the optical wave are absorbed by water molecules and marine hydrosols. Absorption is a type of photosynthesis in which a photon loses its energy and gets converted into heat and chemicals [40]. It is also one of the major IOPs that determines light attenuation. Absorption blocks the communication range of optical waves by forcing emitted light beam to continuously decrease its total propagation energy.Scattering: for understanding, imagine a scenario where an optical beam strikes an object due to which it undergoes a reflection or refraction process. Such a process of optical wave deflection from its original path caused due to change in refractive index is known as scattering [41]. Scattering scatters the photons in multiple different random directions in such a way that some parts of the photon are not received at the receiver end, whereas other parts are delayed due to multipath propagation. These will lead to the degradation of the OSNR value of UWSN. Therefore, scattering results in depletion of received signal strength, and the bonus multipath effect causes intersymbol interference phenomena and timing jitter.Absorption and scattering coefficients are derived based on an IOPs geometric model proposed by authors in [37]. The model is replicated in Figure 6.Figure 6
IOPs Geometric Model [37]. The incident optical beam power is OPI, whereas the absorbed and scattered optical beam power are denoted as OPA and OPs. The optical beam travelled to its destination is denoted as OPt.There are some assumptions made in the above model that are ΔV is a volume of water having thickness ΔD and the wavelength of optical light is λ. The power of the incident optical beam is denoted as OPI. The part of the incident optical beam is absorbed and is denoted by OPA, whereas another part of the incident optical beam is scattered, i.e., OPs. The remaining incident optical beam will travel as expected, which is denoted as OPt.Now, OPA is a(λ), i.e., absorption coefficient or also said as absorbance factor. And OPs are b(λ), which is the scattering coefficient or scatterance factor.By using the law of energy conservation [37, 40, 41], we can say that(5)OPI=OPA+OPs+OPt.From above equation (5), the optical-absorbance A(λ) can be defined as the ratio of absorption power [OPA] to incident power of optical wave OPI. Similarly, optical- scatterance B(λ) is defined as the ratio of optical waves scattering power OPs to incident power OPI.(6)Aλ=OPAOPI,Bλ=OPsOPI.Further, the coefficients of absorption and scattering are then calculated by setting a limit to the thickness of water, denoted as ∆D [37, 38].(7)aλ=limΔD⟶0ΔAλΔD,bλ=limΔD⟶0ΔBλΔD.According to Haltrin’s model [40], the attenuation of optical waves in an underwater environment can be described as the coefficient of attenuation denoted by c(λ), which is the total loss of optical wave due to absorption and scattering expressed as follows:Total attenuation = Absorption coefficient + Scattering coefficient(8)cλ=aλ+bλ.(a) Absorption Coefficient a(λ). Now, further, the coefficient of absorption can be represented as the addition of four absorption factors [37, 42].(9)aλ=apλ+adλ+awλ+aCDOMλ,where(10)aCDOMλ=afλ+ahλ.The underwater medium is the composition of almost eighty different particles dissolved in pure seawater [43], with various different concentrations. In equation (9), it is shown that optical beam absorbance is due to factors like dissolved salts in pure water is denoted as awλ, apλ denotes absorbance due to phytoplankton, adλ represent absorbance of light due to detritus, and aCDOMλ is the absorbance due to color dissolved organic matters (CDOM).In pure seawater, the absorption is mainly due to two sources, molecules of water and the presence of salts in seawater like NaCl, MgCl2, and KCl [44]. In pure seawater, absorption coefficient is high as compared to the scattering coefficient. The presence of chlorophyll in corals undergoes photosynthesis which is the main reason for the absorption of light due to phytoplankton. The absorption effects are different due to different phytoplankton species. The higher absorption is shown by apλ in the blue-red region (400–500 nm) and scatters green light. The type of water (pure, coastal, clear, harbor murky water) plays an important role in estimating concentration of chlorophyll and subsequently specific geographic location-based absorption and scattering effect as shown in Table 4. The detritus contributes to both absorption and scattering, it includes organic matters like bacteria, viruses, zooplankton and inorganic particle like sand and metal oxides [45]. The CDOM are fulvic acid afλ and humic acids ahλ mainly in blue (420 nm–450 nm) [46] and ultraviolet wavelengths.Table 4
Simulation Parameter settings.
Area of sensing field500 × 500 × 500Number of acoustic nodesAn100Number of optical nodesOn50Simulation time200 sBandwidth3 × 108Initial energy of optical nodes100 JThreshold energy for optical nodesTe0.1Antenna typeOmnidirectionalQueue typeDropTail QueueMinimum speed of node0.2Maximum speed of node3Speed0.5Physical layer protocolUnderwaterPhyMAC layer protocolBroadcastMacRouting protocolOECATransmission power ofOn2.0 WReceiving power ofOn0.75 WIdle power ofOn0.008Queue length50Packet size50Routing control packet size20Coverage area100Minimum delay50 usDelay25 usWind (m/s)5Shipping noise (dB)0.2Ee50 nJ/bitEa1.3 × 10−15 J/bit/m4Note. As we are performing clustering algorithm only for optical sensors so focusing on simulation parameters of optical sensors and not of acoustic one.(b) Scattering Coefficient b(λ). Scattering depends on the density of matter and various particles present underwater. It is not dependent on wavelength as in absorption. Therefore, scattering effect is more in coastal areas as compared to open oceans [37]. The b(λ) for underwater optical beam propagation is the addition of three different scattering factors,(11)bλ=bpλ+bwλ+bdλ,where bpλ denotes the scattering due to phytoplankton, bwλ is the scattering due to pure seawater, and bdλ denotes scattering due to detritus. Among the total scattering effect, more than 40% is due to phytoplankton and detritus. In practicality, the exact calculations of bλ are highly dependent on the density of phytoplankton and detritus [47].As per the Beer-Lambert Law, [48] provides simplified formula to describe optical beam attenuation or loss in an underwater medium. The Beer-law formulates the transmitted power of a signal as(12)Pt=Po.TLλ,z,where the transmission loss (TL) of the optical beam propagating in the underwater medium is expressed as a function decaying exponentially with z as path length. (13)TLλ,z=e−Cλz,(14)Pt=Poe−Cλz,where Pt is the transmitted power of the optical beam, z is the distance travelled by optical light, from above equation (8), c(λ) is the attenuation coefficient, TLλ,z is the transmission loss, and Po is the power of optical source/received signal power/source level in milliwatt (mW).From equation (4) and (14), we can say that(15)Psignal=Pt,we can denote Psignal as Pt that is the power received at the receiver. From equation (15), we can rewrite the equation (3) as (16)OSNR=Pt−Pnoise,OSNR=Poe−Cλz–Pnoise.The value ofc(λ) changes with water types. In coastal water, optical beam mostly suffers from scattering, whereas in pure ocean water absorption is the main limiting factor. This is because the pure ocean waters have a high concentration of dissolved elements that results in scattering, whereas minerals, detritus, and plankton present in coastal water are prominent sources of absorption. The harbour’ murky water is composition of in-suspension matters that highly attenuate the optical beam.(2) Noise [Pnoise]. Oceans are pandemonium of different background noises. The noise generated in underwater environment is mainly due to two main sources, natural and man-made. The noise from crashing waves, motion of water molecules, bubbles, thermal noise, heavy rain, undersea volcanoes and earthquakes, and marine life’s (i.e., sound generated by humpback whales, toadfish, snapping shrimp, and dolphins) are all natural sources of sound. The noise generated by human activities is also an important source of background noise. The primary sources of noise generated through human activities are fishing activity, military sonars, surface-ships, oil drilling, and many other functional industrial projects in deep sea [49]. Underwater noise is function of time, depth, and wavelength. The noise sources can be further classified depending on frequency band of sound. If range is 20–500 Hz, then source of noise is shipping activity, in 500–100,000 Hz, the primary source of noise is bubbles due to splashing waves. The thermal noise is above 100 kHz, and its source is Brownian motion of water molecules. The harbour sides are noisier due to human activity as compare to deep sea. The solar is another addition to background noise that restricts the performance of optical wave operating at a depth of 10 m. And at larger depth, the noise sources are biological luminance. The noise is also generated by seismic movements of water that include wind, tides, and ocean currents. The mathematical description of noise can be given by combining the above noise terminology into an aggregated underwater noise as expressed in equation (17) referred from reference [50].(17)Pnoise=10logTn+Sn+Hn+Wn,where Tn denotes seismic movement, Sn is for shipping activity, Hn represents wind in m/sec, and Wn denotes molecular agitation.Turbulence Noise: the large or small form of water currents that are generally irregular and random and have a tendency to generate background noise in different ways, which is popularly known as turbulence noise. The common sources of turbulence noise in underwater environment are hydrophones and water currents resulting to pressure changes. Turbulence noise [51] Tnf is formulated in the equation (18), (18)10logfTn=17−30logf.Shipping Noise: the human-interfered shipping activities and traffic, also creates a background noise, known as shipping noise. The ambient noise level in 100 Hz is predominant source of shipping noise [50].It is described by the formula in equation (19)(19)10logSnf=40+20∗s−0.5+26logf−60logf+0.03,where the value of shipping activity factor (s) is between 0 and 1. If s is 0, then it indicates low shipping activity, whereas if s is 1, then it represents high shipping activity.Thermal Noise [51]: the other name for thermal noise is molecular agitation, and it is another source of background noise in underwater environment. There is a limit to the sensitivity of hydrophone for high frequencies due to temperature of water molecules. It can be calculated as shown in equation (21), (20)10logWnf=−15+20logf,where w is the speed of wind measure in m/s.Wave Noise: the action of formation of wave due to wind on sea surface is also one of the sources of noise, most commonly known as wave noise. The wind speed determines the level of noise over the hydrophone. The waves are mostly turbulent on sea surface, due to which each wave travels at different speeds depending on their different wavelengths. The wave with long wavelength will travel at great speed which then induce pressure waves in oceans. Hence, the noise generated by wave formation is directly proportional to wind speed and frequency. These are properties of wave noise [52] depending on which the equation (19) is formulated as follows: (21)10logHnf=50+7.5w0.5+20logf−40logf+0.4.Finally, we can construct our OSNR formula from equation (16) and (17),(22)OSNRdB=Pte−Cλz–10logTn+Sn+Hn+Wn.The above formula will be further used for finding appropriate cluster heads.(iii)
Energy model for ACOP UWSNThe maximum energy of underwater node is used in data transmission and data reception that directly decreases the amount of remaining energy. In case, if network is implementing selective data forwarding strategy, then underwater nodes require the processing of data. Thus, residual energy will now decrease by sum of transmission energy (ET) and processing energy (Epro). The residual energy calculations are required to perform after each cycle of transmitting and receiving data. The nodes energy consumption is mainly divided into three types [53],(1)
Transmission energy (ET-CH)(2)
Reception energy (ER-CH)(3)
Processing energy (Epro-CH)The below are formulas for energy calculation of cluster head [53],(23)ET−CH=h∗b∗Ee+b1∗d∗Ea,ER−CH=h∗b1∗Ee,Epro−CH=h∗Epro−N.whereEe is the energy consumed by the transmitter and receiver; b1 = b ∗  is the number of cluster members; d is the distance between transmitter and receiver; and Epro-N is the processing/aggregation b bit packet.The below are formulas used for energy calculation of cluster members [53],(24)ET−CM=b∗Ee+b∗d∗Ea,ER−CM=b∗Ee,Epro−CM=b∗Nc∗CavgVsup+b∗Vsup∗I0∗eVsupNc∗Vt∗Ncf,whereNc is the number of clock cycles per task, Cavg is the average capacitance switched per cycle, and I0 is the leakage current.


## 3.3.1. Proposed Clustering Algorithm

Algorithm 2: OECA Algorithm.
(1)
Initializing each node with parameters like initial energy, transmission power, receiving power, bandwidth, and position update interval.(2)
Declare numclust = 0,n = 0, Eo = Threshold value, ON = Optimal Number of Clusters.(3)
Calculate energy for each node, i.e.,Ei where i = 1, 2, 3, 4, …, n(4)
if (Ei ≥ Eo), then{Add that node, saym to set T {j1, j2, j3, …, jm}} (i! = n)end if(5)
for{Calculate OSNR for each node in setT {}, by using below formulaOn=OSNRn=Pke−cλz − (Tn + Sn + Hn + Wn)wheren € T,}end for(6)
Create a table of nodes “d” and their corresponding OSNR value(7)
Select the first maximum Ok value-form table and denote it as (OM)1(OM)1 = Max{On | n € T}(8)
Elect nodeb as Cluster Head and broadcast HELLO message(9)
The nodes which send response messages are considered NCHC1 = {x |b − x| ≤ 30 meters}Suppose |C1| = r1(10)
Deleter1 nodes of Cn from T(11)
increment numclust ++(12)
if (numclust! = ON)Repeat from step 3, untilT =  null.(13)
Proceed to Data sensing


## 3.3.2. Mathematical Modelling

(i)
Cluster Head (CH) SelectionLet 1, 2, 3, …,n be n nodesLetEi, i = 1, 2, 3, …, n be the energy of ith nodesAndEo be the threshold value of energyLetEj1, Ej2, Ej3, …, Ejm, be m nodes having energy greater than or equal to Eo.LetT = {j1, j2, j3, …, jm}Therefore, find the OSNR of the nodes which are inT by the following formulaOn=OSNRn=Poe−cλz − (Tn + Sn + Hn + Wn) for all n € TNow, select the node having maximum value ofOnorOSNRnLet (OM)1 = Max{On | k € T}Suppose (OM)1 corresponds to node b € TTherefore, nodeb is the cluster head of the first clusterC1 = {x |b − x| ≤ 50 meters}Suppose |C1| = r1Now deleter1 nodes of C1 from TT = T − C1Apply the same procedure for nodes inT1, T2, T3, …, TnAnd find (OM)2, (OM)3, …, (OM)n(ii)
Cluster Member (CM) Selection.Continue the same procedure until we get theTp =  ∅Ifz be any the node in Cx∩Cy≠∅,Then find the distance betweenz and cluster heads of Cx and Cy say nodes x and y, respectively, as shown in Figure 5,These are the following two cases,Figure 5
Venn diagram of two overlapping clustersCx and Cy.Case 1.
If |x-z| > |y-z|, then z € Cy, so remove z from CxCase 2.
If |x-z| < |y-z|, then z € Cx, so remove z from CyCase 3.
If nodez is temporary in an inactive state, then it will not be considered for communication as a cluster member of any cluster.
Note: If the energy or OSNR of two nodes is the same, then select the first node whose distance from the base station is minimum.


## 3.3.3. Algorithmic Description

An OSNR value defines a greater value of signal power of an optical wave about the noise power. Thus, the higher OSNR value allows maximum data rate and minimum data retransmissions and thus a good network performance. An OSNR value higher than 40 dB is considered excellent, whereas an OSNR lower than 15 dB can result in a slow, unreliable connection [33]. The most important point is that the node with the greatest OSNR value is also an energy saver node.In other words, we say that impaired blood pressure or temperature values speak about human body health and help in predetermining the health complications in the same way OSNR values help in predetermining the healthy optical nodes and networking complications. Here healthy node means it can achieve reliability with higher bandwidth.The importance of OSNR value suggests selecting the CH based on OSNR values. The node that has the highest OSNR value among all the nodes will be elected as CH. But this is not enough!Further, we found a scenario where the node with 15 dB OSNR was the only node with the highest OSNR value. Then it could not have been a good choice to select that node as CH may lead to data loss, increased propagation delays, and so on. This will also eventually lead to increased energy consumption and also irrelevant loss of node’s energy. With described situation, it is important to crosscheck the best node with some other network parameter. In UWSN, the biggest fight is for energy, and so our second clustering parameter is residual energy. Therefore, a node with the combination of the highest OSNR among all nodes and having residual energy higher than a threshold value will be elected as CH.In the next section, there is a complete explanation of other detailing like whether to calculate residual energy for all nodes or how to calculate OSNR, how to select noncluster members or what are number of CH allowed, and so on.(i)
Optical Signal-to-Noise Ratio [OSNR]:Definition: “Optical signal-to-noise ratio is a measure of optical signal power to the noise power in the underwater optical channel.”Example: OSNR is one of the key performance indicators of the health of the fastest optical submarine network’s reliability and performance.The OSNR may change due to transmission power changes or high amplifier noise levels due to the aging of the in-service underwater network. Such changes make it necessary to test OSNR as part of network maintenance.Importance of OSNR(1)
OSNR is one of the important performance parameters while designing an underwater network as it helps in deciding the communication capacity of a node.(2)
Periodical measuring of OSNR can help underwater application stakeholders, vendors, and network engineers address network failures before network breakdown occurs. Therefore, OSNR indirectly helps in minimizing network downtime.(3)
Network outage can occur due for multiple reasons, and OSNR calculations allow quick localization of defective optical nodes. Hence, with OSNR values, it is possible to predetermine a node that can ensure data reliability. In such a case, a node with a higher OSNR value is then capable of becoming a cluster head.(ii)
Construction of OSNR formulaIn general, the OSNR is the ratio between the useful optical signal (i.e., carrying data) to unwanted background noise. It is represented as a ratio in [34], which is shown in the below formula,(1)OSNR=PsignalPnoise.Calculating SNR ratio on a logarithmic basis using decibels as in [34], which is shown in equation (1):(2)OSNRdB=10dB.log10PsignalPnoise.If signal and noise powers are already expressed in decibels, then the formula can be further simplified as in [34]. Therefore, equation (2) can be rewritten as equation (3),(3)OSNRdB=PsignaldB−PnoisedB.wherePsignal represents meaningful information in the signal received and Pnoise represents the unwanted background noise in dB.FurtherPsignal is computed by taking away the transmission loss from the signal generated at the source level [35], as shown in equation (4)(4)Psignal=SL−TL.The direct implication of existing OSNR formulas of TWSN into UWSNs is not practically possible. Therefore, deep studies and rethinking of formula construction were required. To construct a formula for OSNR, we first studied the physiology of optical waves propagating in the underwater environment.(1) Study of Basic Properties of the Optical Wave as an Underwater Communication Medium. According to light transfer in natural water [36], the properties of optical waves are recognized into two types,(i)
Inherent Optical Properties (IOP): the properties of the optical signal that only depend on water (i.e., transmission medium), and more precisely on composition and particles present within water [37] are known as IOPs. They are independent of the characteristic of optical wave sources like LED, Blue Green, laser beam, and many more. The three major IOPs of the underwater medium are absorption coefficient, scattering coefficient, and attenuation coefficient [38].(ii)
Apparent Optical Properties (AOP): the properties of the optical signal that not only depend on the transmission medium like IOPs but also on collimation and diffusion are known as AOPs. The most important AOPs are reflection, radiation, and irradiation [37, 39].In an underwater optical communication system, link cost is determined by IOPs, whereas ambient light levels are calculated by AOPs. However, IOPs have a strong impact on the optical channel, and thus will focus more on IOPs (absorption and scattering) to derive our OSNR formula.Absorption: it is a phenomenon in which photons present in the optical wave are absorbed by water molecules and marine hydrosols. Absorption is a type of photosynthesis in which a photon loses its energy and gets converted into heat and chemicals [40]. It is also one of the major IOPs that determines light attenuation. Absorption blocks the communication range of optical waves by forcing emitted light beam to continuously decrease its total propagation energy.Scattering: for understanding, imagine a scenario where an optical beam strikes an object due to which it undergoes a reflection or refraction process. Such a process of optical wave deflection from its original path caused due to change in refractive index is known as scattering [41]. Scattering scatters the photons in multiple different random directions in such a way that some parts of the photon are not received at the receiver end, whereas other parts are delayed due to multipath propagation. These will lead to the degradation of the OSNR value of UWSN. Therefore, scattering results in depletion of received signal strength, and the bonus multipath effect causes intersymbol interference phenomena and timing jitter.Absorption and scattering coefficients are derived based on an IOPs geometric model proposed by authors in [37]. The model is replicated in Figure 6.Figure 6
IOPs Geometric Model [37]. The incident optical beam power is OPI, whereas the absorbed and scattered optical beam power are denoted as OPA and OPs. The optical beam travelled to its destination is denoted as OPt.There are some assumptions made in the above model that are ΔV is a volume of water having thickness ΔD and the wavelength of optical light is λ. The power of the incident optical beam is denoted as OPI. The part of the incident optical beam is absorbed and is denoted by OPA, whereas another part of the incident optical beam is scattered, i.e., OPs. The remaining incident optical beam will travel as expected, which is denoted as OPt.Now, OPA is a(λ), i.e., absorption coefficient or also said as absorbance factor. And OPs are b(λ), which is the scattering coefficient or scatterance factor.By using the law of energy conservation [37, 40, 41], we can say that(5)OPI=OPA+OPs+OPt.From above equation (5), the optical-absorbance A(λ) can be defined as the ratio of absorption power [OPA] to incident power of optical wave OPI. Similarly, optical- scatterance B(λ) is defined as the ratio of optical waves scattering power OPs to incident power OPI.(6)Aλ=OPAOPI,Bλ=OPsOPI.Further, the coefficients of absorption and scattering are then calculated by setting a limit to the thickness of water, denoted as ∆D [37, 38].(7)aλ=limΔD⟶0ΔAλΔD,bλ=limΔD⟶0ΔBλΔD.According to Haltrin’s model [40], the attenuation of optical waves in an underwater environment can be described as the coefficient of attenuation denoted by c(λ), which is the total loss of optical wave due to absorption and scattering expressed as follows:Total attenuation = Absorption coefficient + Scattering coefficient(8)cλ=aλ+bλ.(a) Absorption Coefficient a(λ). Now, further, the coefficient of absorption can be represented as the addition of four absorption factors [37, 42].(9)aλ=apλ+adλ+awλ+aCDOMλ,where(10)aCDOMλ=afλ+ahλ.The underwater medium is the composition of almost eighty different particles dissolved in pure seawater [43], with various different concentrations. In equation (9), it is shown that optical beam absorbance is due to factors like dissolved salts in pure water is denoted as awλ, apλ denotes absorbance due to phytoplankton, adλ represent absorbance of light due to detritus, and aCDOMλ is the absorbance due to color dissolved organic matters (CDOM).In pure seawater, the absorption is mainly due to two sources, molecules of water and the presence of salts in seawater like NaCl, MgCl2, and KCl [44]. In pure seawater, absorption coefficient is high as compared to the scattering coefficient. The presence of chlorophyll in corals undergoes photosynthesis which is the main reason for the absorption of light due to phytoplankton. The absorption effects are different due to different phytoplankton species. The higher absorption is shown by apλ in the blue-red region (400–500 nm) and scatters green light. The type of water (pure, coastal, clear, harbor murky water) plays an important role in estimating concentration of chlorophyll and subsequently specific geographic location-based absorption and scattering effect as shown in Table 4. The detritus contributes to both absorption and scattering, it includes organic matters like bacteria, viruses, zooplankton and inorganic particle like sand and metal oxides [45]. The CDOM are fulvic acid afλ and humic acids ahλ mainly in blue (420 nm–450 nm) [46] and ultraviolet wavelengths.Table 4
Simulation Parameter settings.
Area of sensing field500 × 500 × 500Number of acoustic nodesAn100Number of optical nodesOn50Simulation time200 sBandwidth3 × 108Initial energy of optical nodes100 JThreshold energy for optical nodesTe0.1Antenna typeOmnidirectionalQueue typeDropTail QueueMinimum speed of node0.2Maximum speed of node3Speed0.5Physical layer protocolUnderwaterPhyMAC layer protocolBroadcastMacRouting protocolOECATransmission power ofOn2.0 WReceiving power ofOn0.75 WIdle power ofOn0.008Queue length50Packet size50Routing control packet size20Coverage area100Minimum delay50 usDelay25 usWind (m/s)5Shipping noise (dB)0.2Ee50 nJ/bitEa1.3 × 10−15 J/bit/m4Note. As we are performing clustering algorithm only for optical sensors so focusing on simulation parameters of optical sensors and not of acoustic one.(b) Scattering Coefficient b(λ). Scattering depends on the density of matter and various particles present underwater. It is not dependent on wavelength as in absorption. Therefore, scattering effect is more in coastal areas as compared to open oceans [37]. The b(λ) for underwater optical beam propagation is the addition of three different scattering factors,(11)bλ=bpλ+bwλ+bdλ,where bpλ denotes the scattering due to phytoplankton, bwλ is the scattering due to pure seawater, and bdλ denotes scattering due to detritus. Among the total scattering effect, more than 40% is due to phytoplankton and detritus. In practicality, the exact calculations of bλ are highly dependent on the density of phytoplankton and detritus [47].As per the Beer-Lambert Law, [48] provides simplified formula to describe optical beam attenuation or loss in an underwater medium. The Beer-law formulates the transmitted power of a signal as(12)Pt=Po.TLλ,z,where the transmission loss (TL) of the optical beam propagating in the underwater medium is expressed as a function decaying exponentially with z as path length. (13)TLλ,z=e−Cλz,(14)Pt=Poe−Cλz,where Pt is the transmitted power of the optical beam, z is the distance travelled by optical light, from above equation (8), c(λ) is the attenuation coefficient, TLλ,z is the transmission loss, and Po is the power of optical source/received signal power/source level in milliwatt (mW).From equation (4) and (14), we can say that(15)Psignal=Pt,we can denote Psignal as Pt that is the power received at the receiver. From equation (15), we can rewrite the equation (3) as (16)OSNR=Pt−Pnoise,OSNR=Poe−Cλz–Pnoise.The value ofc(λ) changes with water types. In coastal water, optical beam mostly suffers from scattering, whereas in pure ocean water absorption is the main limiting factor. This is because the pure ocean waters have a high concentration of dissolved elements that results in scattering, whereas minerals, detritus, and plankton present in coastal water are prominent sources of absorption. The harbour’ murky water is composition of in-suspension matters that highly attenuate the optical beam.(2) Noise [Pnoise]. Oceans are pandemonium of different background noises. The noise generated in underwater environment is mainly due to two main sources, natural and man-made. The noise from crashing waves, motion of water molecules, bubbles, thermal noise, heavy rain, undersea volcanoes and earthquakes, and marine life’s (i.e., sound generated by humpback whales, toadfish, snapping shrimp, and dolphins) are all natural sources of sound. The noise generated by human activities is also an important source of background noise. The primary sources of noise generated through human activities are fishing activity, military sonars, surface-ships, oil drilling, and many other functional industrial projects in deep sea [49]. Underwater noise is function of time, depth, and wavelength. The noise sources can be further classified depending on frequency band of sound. If range is 20–500 Hz, then source of noise is shipping activity, in 500–100,000 Hz, the primary source of noise is bubbles due to splashing waves. The thermal noise is above 100 kHz, and its source is Brownian motion of water molecules. The harbour sides are noisier due to human activity as compare to deep sea. The solar is another addition to background noise that restricts the performance of optical wave operating at a depth of 10 m. And at larger depth, the noise sources are biological luminance. The noise is also generated by seismic movements of water that include wind, tides, and ocean currents. The mathematical description of noise can be given by combining the above noise terminology into an aggregated underwater noise as expressed in equation (17) referred from reference [50].(17)Pnoise=10logTn+Sn+Hn+Wn,where Tn denotes seismic movement, Sn is for shipping activity, Hn represents wind in m/sec, and Wn denotes molecular agitation.Turbulence Noise: the large or small form of water currents that are generally irregular and random and have a tendency to generate background noise in different ways, which is popularly known as turbulence noise. The common sources of turbulence noise in underwater environment are hydrophones and water currents resulting to pressure changes. Turbulence noise [51] Tnf is formulated in the equation (18), (18)10logfTn=17−30logf.Shipping Noise: the human-interfered shipping activities and traffic, also creates a background noise, known as shipping noise. The ambient noise level in 100 Hz is predominant source of shipping noise [50].It is described by the formula in equation (19)(19)10logSnf=40+20∗s−0.5+26logf−60logf+0.03,where the value of shipping activity factor (s) is between 0 and 1. If s is 0, then it indicates low shipping activity, whereas if s is 1, then it represents high shipping activity.Thermal Noise [51]: the other name for thermal noise is molecular agitation, and it is another source of background noise in underwater environment. There is a limit to the sensitivity of hydrophone for high frequencies due to temperature of water molecules. It can be calculated as shown in equation (21), (20)10logWnf=−15+20logf,where w is the speed of wind measure in m/s.Wave Noise: the action of formation of wave due to wind on sea surface is also one of the sources of noise, most commonly known as wave noise. The wind speed determines the level of noise over the hydrophone. The waves are mostly turbulent on sea surface, due to which each wave travels at different speeds depending on their different wavelengths. The wave with long wavelength will travel at great speed which then induce pressure waves in oceans. Hence, the noise generated by wave formation is directly proportional to wind speed and frequency. These are properties of wave noise [52] depending on which the equation (19) is formulated as follows: (21)10logHnf=50+7.5w0.5+20logf−40logf+0.4.Finally, we can construct our OSNR formula from equation (16) and (17),(22)OSNRdB=Pte−Cλz–10logTn+Sn+Hn+Wn.The above formula will be further used for finding appropriate cluster heads.(iii)
Energy model for ACOP UWSNThe maximum energy of underwater node is used in data transmission and data reception that directly decreases the amount of remaining energy. In case, if network is implementing selective data forwarding strategy, then underwater nodes require the processing of data. Thus, residual energy will now decrease by sum of transmission energy (ET) and processing energy (Epro). The residual energy calculations are required to perform after each cycle of transmitting and receiving data. The nodes energy consumption is mainly divided into three types [53],(1)
Transmission energy (ET-CH)(2)
Reception energy (ER-CH)(3)
Processing energy (Epro-CH)The below are formulas for energy calculation of cluster head [53],(23)ET−CH=h∗b∗Ee+b1∗d∗Ea,ER−CH=h∗b1∗Ee,Epro−CH=h∗Epro−N.whereEe is the energy consumed by the transmitter and receiver; b1 = b ∗  is the number of cluster members; d is the distance between transmitter and receiver; and Epro-N is the processing/aggregation b bit packet.The below are formulas used for energy calculation of cluster members [53],(24)ET−CM=b∗Ee+b∗d∗Ea,ER−CM=b∗Ee,Epro−CM=b∗Nc∗CavgVsup+b∗Vsup∗I0∗eVsupNc∗Vt∗Ncf,whereNc is the number of clock cycles per task, Cavg is the average capacitance switched per cycle, and I0 is the leakage current.


## 4. Experimental Procedure

The simulation model used is based on Ns2. The results are based on the simulations of 140 wireless underwater sensor nodes over a 3D cubic grid space 500 m × 500 m × 500 m. Among 140 nodes, 100 nodes are embedded with acoustic characteristics and 40 nodes have optical features. In simulation routing protocol used is proposed OECA, whereas MAC layer protocol used is BroadcastMac protocol. The simulation parameters setting is as listed in Table4.


### 4.1. Performance Comparison between Proposed Clustering Algorithm (OECA) and Existing Algorithm (EOCA)

This section shows dynamics of network performance parameters with run time and also explains the results showcasing excellent performance of OECA (OSNR-based Energy Efficient Clustering Algorithm) over EOCA (Energy Optimization Clustering Algorithm) [31, 32]. All the graphs below show that proposed ACOP-UWSN with proposed clustering protocol algorithm OECA has good performance as compared to the existing scheme EOCA.[Note: There was no existing work available on clustering of underwater optical sensors that relates to our proposed idea. Therefore, for comparative study, we have selected a powerful clustering algorithm EOCA that is originally developed for underwater acoustic sensors. But we have implemented the EOCA model for optical sensors to have equal level of comparability.]The energy consumption graphs in Figures7 and 8 show that the amount of energy total and average energy consumed by OECA and EOCA are almost the same at starting and then the speed of OECA energy consumption lowers as compared to existing scheme.Figure 7
Plot of total energy consumption of nodes with respect to time. The bars represent that for 200 seconds total energy consumed by a node using proposed OECA scheme is 400 J, whereas that for EOCA it is 450 J.Figure 8
Plot for energy consumption of complete network with respect to time is denoted as average energy consumption. The bars represent that for 200 seconds average energy consumed by network using proposed OECA scheme is 4.2 J, whereas that for EOCA it is approximately 4.8 J.In Figures9 and 10, the graphs for average residual energy show that OECA has long lifecycle than EOCA. The total residual energy graph deeply showcases the efficiency of proposed scheme in increasing optical nodes life. The total and average remaining energy of OECA is comparatively more than that in the EOCA.Figure 9
Plot for residual energy of node with respect to time is denoted as total energy consumption. The bars represent that for 200 seconds total residual energy left by using proposed OECA scheme is 9.5J, whereas that for EOCA is comparatively lesser, 9.4 J.Figure 10
Plot for average remaining energy of network with respect to time is denoted as average remaining energy. The bars represent that for 150 seconds average residual energy left by using proposed OECA scheme is greater 96.4 J, whereas that for EOCA it is 97.1 J.Figure11 shows graph for packet delivery ratio of proposed ACOP-UWSN with the usage of two different clustering techniques. The PDR is defined as the ratio of data packets transmitted by sensors to number of data packets successfully received by receiver nodes. The results achieved show that PDR is equivalently good as existing scheme. Hence by adopting the scheme, the ACOP-UWSN has good network performance.Figure 11
Plot for packet delivery ratio for proposed and existing scheme. The PDR at 200 s for OECA is 88%, whereas for EOCA it is 82%. The results achieved show that PDR for proposed scheme is much greater as compared to existing scheme.The graph in Figure12 is a proof that the ACOP UWSN can function for long network lifetime using OECA. The network lifetime of ACOP-UWSN with EOCA reduces slowly with time, whereas the network lifetime of the ACOP-UWSN with OECA remains at same level with change in time.Figure 12
Plot showcasing time period for which network is alive using proposed and existing scheme.In summary, all the above simulation graphs depict that OECA is succeeded to reach the goal of minimal energy consumption with better network performance with ACOP-UWSN.The results in Figure13 show that the transmission loss of OECA is low. In cluster formation based on OSNR values, indirectly CH chooses the nodes within its communication range as cluster members. Hence, distance between CH and CM is reduced that eventually reduces decreases number of packets dropped.Figure 13
Plot shows transmission loss for proposed and existing scheme.The results for various network parameters like delay and throughput as shown in Figures14 and 15 demonstrate that ACOP-UWSN has long network life with high PDR, by using the OSNR-based energy efficient clustering algorithm.Figure 14
Plot for delay measured with respect to time using proposed and existing scheme. The bar shows that delay measure for OECA is lower as compare to EOCA.Figure 15
Plot showing the amount of data that can be transferred within the given amount of time. The network throughput varies with time. The bar shows that throughput for proposed scheme is higher.


## 4.1. Performance Comparison between Proposed Clustering Algorithm (OECA) and Existing Algorithm (EOCA)

This section shows dynamics of network performance parameters with run time and also explains the results showcasing excellent performance of OECA (OSNR-based Energy Efficient Clustering Algorithm) over EOCA (Energy Optimization Clustering Algorithm) [31, 32]. All the graphs below show that proposed ACOP-UWSN with proposed clustering protocol algorithm OECA has good performance as compared to the existing scheme EOCA.[Note: There was no existing work available on clustering of underwater optical sensors that relates to our proposed idea. Therefore, for comparative study, we have selected a powerful clustering algorithm EOCA that is originally developed for underwater acoustic sensors. But we have implemented the EOCA model for optical sensors to have equal level of comparability.]The energy consumption graphs in Figures7 and 8 show that the amount of energy total and average energy consumed by OECA and EOCA are almost the same at starting and then the speed of OECA energy consumption lowers as compared to existing scheme.Figure 7
Plot of total energy consumption of nodes with respect to time. The bars represent that for 200 seconds total energy consumed by a node using proposed OECA scheme is 400 J, whereas that for EOCA it is 450 J.Figure 8
Plot for energy consumption of complete network with respect to time is denoted as average energy consumption. The bars represent that for 200 seconds average energy consumed by network using proposed OECA scheme is 4.2 J, whereas that for EOCA it is approximately 4.8 J.In Figures9 and 10, the graphs for average residual energy show that OECA has long lifecycle than EOCA. The total residual energy graph deeply showcases the efficiency of proposed scheme in increasing optical nodes life. The total and average remaining energy of OECA is comparatively more than that in the EOCA.Figure 9
Plot for residual energy of node with respect to time is denoted as total energy consumption. The bars represent that for 200 seconds total residual energy left by using proposed OECA scheme is 9.5J, whereas that for EOCA is comparatively lesser, 9.4 J.Figure 10
Plot for average remaining energy of network with respect to time is denoted as average remaining energy. The bars represent that for 150 seconds average residual energy left by using proposed OECA scheme is greater 96.4 J, whereas that for EOCA it is 97.1 J.Figure11 shows graph for packet delivery ratio of proposed ACOP-UWSN with the usage of two different clustering techniques. The PDR is defined as the ratio of data packets transmitted by sensors to number of data packets successfully received by receiver nodes. The results achieved show that PDR is equivalently good as existing scheme. Hence by adopting the scheme, the ACOP-UWSN has good network performance.Figure 11
Plot for packet delivery ratio for proposed and existing scheme. The PDR at 200 s for OECA is 88%, whereas for EOCA it is 82%. The results achieved show that PDR for proposed scheme is much greater as compared to existing scheme.The graph in Figure12 is a proof that the ACOP UWSN can function for long network lifetime using OECA. The network lifetime of ACOP-UWSN with EOCA reduces slowly with time, whereas the network lifetime of the ACOP-UWSN with OECA remains at same level with change in time.Figure 12
Plot showcasing time period for which network is alive using proposed and existing scheme.In summary, all the above simulation graphs depict that OECA is succeeded to reach the goal of minimal energy consumption with better network performance with ACOP-UWSN.The results in Figure13 show that the transmission loss of OECA is low. In cluster formation based on OSNR values, indirectly CH chooses the nodes within its communication range as cluster members. Hence, distance between CH and CM is reduced that eventually reduces decreases number of packets dropped.Figure 13
Plot shows transmission loss for proposed and existing scheme.The results for various network parameters like delay and throughput as shown in Figures14 and 15 demonstrate that ACOP-UWSN has long network life with high PDR, by using the OSNR-based energy efficient clustering algorithm.Figure 14
Plot for delay measured with respect to time using proposed and existing scheme. The bar shows that delay measure for OECA is lower as compare to EOCA.Figure 15
Plot showing the amount of data that can be transferred within the given amount of time. The network throughput varies with time. The bar shows that throughput for proposed scheme is higher.


## 5. Discussion

In literature, almost all research work is based on acoustic communications and very few speak about a hybrid approach of combining acoustic and optical underwater communication mediums. Therefore, after studying pros and cons of both mediums, we came up with ACoustic OPtical-Underwater Wireless Sensor Network (ACOP-UWSN) architecture. The proposed network model aims to reduce propagation delays and to achieve data reliability. Then with the intention of conserving underwater nodes energy, we proposed clustering algorithm for optical sensors. The clustering criteria is Optical Signal-to- Noise Ratio and residual energy. The proposed research work uses a different formula for OSNR calculation of underwater optical networks. Based on the derived OSNR formula and residual energy, we have proposed a novel clustering algorithm named as “OECA.” The novelty of the algorithm lies in two things, first, it is the first work that presents an algorithm for clustering of underwater optical sensors and second the newly derived OSNR formula. The proposed work is simulation based, as the simulation tools help to review the proposed network protocols and also give a virtual idea of the pros and cons in the network before real-time deployment. The popular simulation tools for underwater networking like OPNET, NETSIM, OMNET++, AquaSim in Ns2, UAN model in Ns3, JSIM, and REAL, which are most commonly used. In state of art, among all tools, Ns2, MATLAB, and OPNET are the most widely used simulators for UWSN. The different characteristics of an acoustic or optical sensor can be simulated on Aqua Tool with appropriate simulation parameter setting [54]. Another Ns2-based simulation tool similar to Aqua-Tool is Aqua-Sim [55], which in addition provides simulation of underwater channel attenuation. The OPNET simulation tool is a licensed software that is commonly used to perform simulation work for communication networks. The underwater propagation delays, the transmitter and receiver power stage and the underwater background noise stage, and the nearer to realistic simulation of underwater communication channel characteristics are all provided in OPNET [56]. Finally, in [57], they have discussed the Underwater Sensor Network Simulation Tool [USNET], which was also developed to perform flawless simulation of different UWSNs architectures. In this discussion, we can conclude that Ns-2, Ns-3, and OPNET are the three reliable tools for simulating an underwater network. In our research work, we have used Ns-2 to simulate the proposed research work. And the analysis of results done in Section 4.1 shows that the proposed OECA algorithm (Algorithm 2) is comparatively more efficient in terms of energy consumption, remaining energy, delay, throughput, packet delivery ratio, and network lifetime. The practical implications of these work are possible in various fields like underwater monitoring and surveillance applications. One more aspect of applicability of this work is in building the communication world. The ACOP-UWSN can replace the mesh of Internet cables running through all over the oceans. The wireless underwater Internet connections by using the proposed architecture can reduce the huge maintenance cost of optical fibre cables.


## 6. Conclusion and Future Work

An underwater optical sensor clustering algorithm, named OSNR-based Energy Efficient Clustering Algorithm (OECA) for the proposed ACOP-UWSN, is developed in this work. The algorithm was designed with an intention of systematic data transmission with energy conservation and data reliability. The cluster head selection is done based on OSNR values and calculation of residual energy only for the nodes having the highest OSNR (i.e., setT{}). The nodes responding to the “HELLO” broadcast message is selected as cluster members. The sensed data transmission path for ACOP-UWSN is from cluster members to cluster head and then to surface buoy and lastly to a base station, which is the surface ship. As a result, the cluster head’s energy consumption is highest among all nodes, so the algorithm is designed in a way that the cluster heads for each cluster change periodically to balance the energy consumption of head nodes. The OSNR formula derivation gives uniqueness to the proposed algorithm. The simulation results show that the proposed work has overcome the gaps analysed in the literature.


## 7. Future Work

The Beer Lambert Law was used to derive the OSNR formula. That law is based on two assumptions, (i) receiver and transmitter are aligned and (ii) all the photons that are scattered are lost, although in reality few photons still can arrive at their destination even after multiple scattering events. This second assumption at a point dominates the power of the received optical signal in a scattering situation. Therefore, in future, we can use another volume scattering function (VSF) [35] to describe the scattering effects more precisely. Our future research work would be to extend this work to achieve data security for underwater data transmission in optical and acoustic channels.



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*Source: 1647028-2022-08-05.xml*