SC 2 R: Strategic cooperative cluster-based routing for Internet of Underwater Things

SC 2 R: Strategic cooperative cluster-based routing for Internet of Underwater Things | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SC 2 R: Strategic cooperative cluster-based routing for Internet of Underwater Things Swati Gupta, Niraj Pratap Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2314480/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Oceanographic data gathering, pollution monitoring, offshore exploration, catastrophe avoidance, aided navigation, and tactical surveillance are all expected to benefit from the Internet of Underwater Things (IoUT). However, the viability of various applications in underwater scenarios is possible only if the routing among the sensor nodes employed is strategically optimized. This paper proposes Strategic Cooperative Routing for IoUT (SC 2 R) that employs a cooperative node for data collection from each Cluster-Head (CH). CH selection is done through the energy and distance parameters. The cooperative nodes are positioned on water surface, other nodes being placed in the underwater terrain. This cooperative routing helps in the data collection for the time-critical scenario as it avoids multi-hop communication among the sensor nodes underwater. Due to decreased number of hops of communication, the delay in data transmission is reduced. The simulation results illustrate the efficacy of the proposed routing technique in comparison to competitive algorithms. The proposed protocol outperforms state of art routing protocols in terms of Network Lifetime and End to End Delay (EED). IoUT time-critical underwater sensor network cooperative routing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Exploration of the undersea environment is essential since water covers a large portion of our globe. A sink node with both acoustic and radio modems and sensor nodes with acoustic modems make up the Underwater Wireless Sensor Network (UWSN) and when it is associated with Internet of Things (IoT), it becomes the IoUT [ 1 ]. IoUT is a new category of IoT and is considered to be one of the recent technologies towards the building of a smart city. IoUT is defined as “the network of smart interconnected underwater objects”. For this UWSNs have surfaced as a promising system. Rivers, lakes, and abysses are all covered via these networks. Naval data gathering, military training, aquatic pollution monitoring, politic surveillance, submarine discovery, disaster avoidance, submarine ecosystem monitoring and other applications are only a few examples [ 2 ]. Figure 1 shows the basic architecture of IoUT. For IoUT, the basic architectural design is of UWSN. The UWSN is made up of sensor nodes that are linked together by acoustic connections and interact via protocols. The nodes collect data from the hostile underwater environment and send it to offshore stations, also known as sinks, that are installed at the water's surface. The underwater sensor nodes are placed meagerly from the layer of the onshore surface to seabed-layer for bringing the information from the submerged callous environmental conditions by utilizing an acoustic modem [ 3 ]. Sensors in UWSN have the ability of detecting, and communicating with neighboring or to say other sensors. In UWSNs, the sensors are spread in a three-dimensional (3D) space in underwater climates [ 4 ]. Since the principal reason for UWSNs is to gather information at whatever point an incident happens, the sensor nodes should be placed to cover the entire region under observation. Subsequently, a path between a source to the expected sink should be set up for powerful and dependable information transmission. Communication is performed with their neighbors using acoustic modems, whereas sink nodes use combined radio and acoustic modems [ 5 ]. Acoustic signals are used by UWSNs for communication because they are very less attenuated in water than conventional radio waves, which are substantially attenuated. There are different advantages of underwater sensor networks. First is real-time monitoring, something that is very important [ 6 ]. Standalone sensor nodes which are dipped underwater can only record information that is occurring around them once, but that is a traditional way in which real-time monitoring is not possible. By recovering these nodes which are sinking underwater and the stored data can be obtained from them, but using underwater sensor networks one can do real-time monitoring of the region or the oceanic column around which these underwater sensor networks are deployed. There are various issues with underwater acoustic communication [ 7 ] [ 8 ]. Firstly, the speed of propagation of an acoustic transmission is several orders lower than that of a radio signal, leading to a longer end-to-end delay (E2ED).Due to repeated topological changes of sensor nodes caused by water currents, node densities in various sections of the network become unbalanced. Further, nodes in low-density regions degrade in a relatively short period. Acoustic communication has a low bandwidth, which translates to a low data rate and, as a result, a sluggish information transmission method. Diffraction, Reflection, and refraction processes have a significant impact on the physical characteristics of auditory waves. Changes in pressure, salinity, and temperature for example, affect the speed of acoustic transmissions [ 9 ]. Since underwater sensors are power-driven by batteries, which are expensive to replenish or replace, energy is a limited resource. Water currents modify the network topology often, and based on the pace of change, the system must adapt to new topology control regularly. Inter Symbol Interference (ISI) is caused by delays and Doppler spreads, and the trustworthiness of data originality diminishes as a result. The routing methods built for traditional sensor networks cannot operate adequately for UWSNs due to the aforementioned concerns. Furthermore, environmental factors such as fouling and corrosion alter the physical characteristics of sensor nodes, limiting the lifetime of the network of underwater sensor devices. Moreover, because of node mobility and also their unpredictable failures, the topology of the network keeps on changing. Also, the architecture being 3-dimensional (3D), being different from terrestrial wireless sensor networks (TWSNs) requires various issues to be dealt with differently. End-to-end delay is a crucial aspect that needs to be addressed while considering the critical applications for which IoUT is designed [ 10 ]. A sprinkling of efforts is reported in this direction, however, it is anticipated that once the end-to-end delay is reduced, the data delivery will help in serving the human or marine resources from any catastrophe. Once, the number of packets being transmitted is known, it will set up the time taken to deliver those packets. With technological advances in underwater networks, sensors are becoming smaller, smarter, and flexible with less power consumption, an increase in the processing capabilities, and also, their capability to operate in underwater scenarios. Also, UWSN technology can be incorporated along with Internet Protocol-based systems for supporting the IoT and also machine-to-machine (M2M) frameworks for monitoring. Since the development of IoUT, the energy-limited sensor-based IoT devices have been facing challenges of limited network lifetime.3D deployment of nodes imposes further challenges [ 11 ]. Sensor nodes must have the capability of transmitting data to the sink. The nodes should be able to adjust their depths for finding a suitable relay if no accessible relay could be found using the current transmission range. Because the nodes are distributed at random and energy harvesting is not possible, the void hole problem develops, which is considered the most difficult challenge in routing protocol design. Further, it is also observed that the data transmission in acoustic medium follows the multi-hop transmission which eventually leads to energy hole problem [ 12 ]. It has been studied that cooperative routing has helped tremendously in bringing energy balancing to the network. Hence, if the cooperative nodes are used along with the CH nodes which will be placed above the surface of the water, may help in the early delivery of data and also can lead to early delivery of data to the sink. The routing protocol hence designed should be robust and self-adaptive, an important requirement for networks operating in ruthless underwater environments [ 13 ]. The key contributions of the paper are listed below. It discusses the latest papers investigating the intermingling for Internet of Underwater Things applications. It proposes strategic cooperative cluster-based routing for time-critical applications for IoUT. The proposed method deploy cooperative nodes which are kept higher in energy to the other nodes, these nodes collect data from the nearest CH in a single hop and hence, relay data to sink through the intermediate cooperative nodes. The CH selection is done based on the node energy and distance from the nearest cooperative node. Finally, performance validation is performed against recently proposed routing algorithms in underwater WSN. Improvement in network lifetime and end-to-end delay is achieved with the proposed algorithm. The rest of the paper is given as follows. Section 2 presents the literature work, in Section 3, the proposed work is explained, Section 4 discusses the results of the proposed work and finally, Section 5 concludes the paper. 2. Related Work There has been a great magnitude of research as far as an energy-efficient routing in underwater scenarios is concerned [ 14 ]. Designing a routing protocol is a critical task since it provides varied needs for acoustic communication to spot and maintain network routes. By capturing data and sending it to ground stations, UWSNs serve an important role in ocean research and monitoring. The sensor nodes when deployed underwater needs to sustain for a longer period to deliver optimized performance [ 15 ]. It is due to the reason that circumstances in the underwater scenario are dynamic and needs to be given special care when it comes to routing [ 16 ]. Energy-efficient routing has been targeted by various researchers under the umbrella of UWSN [ 17 , 18 ]. In several ways, UWSN differs from standard land-based WSNs, in terms of bandwidth, long propagation delays, floating nodes, and power efficiency. Because the underwater environment is so complicated, nodes' batteries cannot be changed recurrently. As a result, the energy constraint is the most important aspect in underwater routing systems [ 19 ]. These protocols demand that the nodes in the path consume as little energy as possible, which is critical for enhancing the lifetime of the network. Given the available energy of the sensor or the energy demand on the transmission network, energy-efficient routing methods pick the most optimal route for data forwarding from the source to the destination [ 21 , 22 ]. As a result, effective routing protocols are required because they provide stable and dependable data transport from source to destination nodes. Several protocols are developed and investigated within the past and gift to boost UWSN performance. The authors considered the latest research of routing protocols in UWSNs and discovered that the primary consideration while designing the routing protocol is its energy efficiency, its lifetime, and also EED where time-critical applications are considered. The major task is to keep the network operational with whatever little energy in nodes is there [ 20 ]. Several protocols have been discussed considering all these factors. Many underwater communication routing algorithms have been suggested over the last few decades, and they may be loosely split into various categories. MCOBR [ 23 ]: To address the concerns of multi-path fading, low throughput, sinkhole, etc. the authors proposed the MCBOR algorithm. In this authors exploited the sense of butterflies for delivering data without any loss. The increase in PDR and reduction in transmission loss have been targeted. IBSO-VFA [ 24 ]:In this author discussed the sparsely populated scenario which may lead to energy holes and making it difficult for target location. The author proposed an enhanced version of brain storm optimization which is integrated with the virtual force algorithm (IBSO-VFA) which helped in improvement of the UWSN coverage performance. CSO [ 25 ]: In this, the author explored the concept of water current which makes the node move from their position of deployment. The CSO-based approach is adapted to identify articulation points in the network. The delay and threshold parameters are targeted in this work. However, due to an excessive number of hops among the nodes, it suffers from the hot-spot problem. CUWSN [ 26 ]: The authors proposed a multi-hop-based communication for the reduction of energy expenditure. The authors’ select cluster coordinator and CH and various parameters are examined as the performance check for the proposed work by the authors. However, as this protocol follows multi-hop communication, it faces the hot-spot problem. The authors have given no provision to deal with such a scenario. MFOBR [ 27 ]: The authors worked on the fault resilience for the UWSN. These faults to the nodes could be caused by various creatures, disasters, etc. The authors proposed MFOBR which uses Autonomous Underwater Vehicles (AUVs) for dealing with link failure problems. The authors selected the best possible node for the collection of data and hence, forwarding to the nearest AUVs. However, due to multi-hop communication, it suffers from a hot-spot problem. MLCEE [ 28 ]: This protocol works in three steps. The first step is division of network into layers, starting from sea bed to sea surface, making clusters within the layer, and finally data forwarding to the sink node. The layer next to the surface remains unclustered and the nodes present in it relay the data directly to sink. In this algorithm, CH selection was based on Bayesian probability. JCRP [ 29 ]: There are two methods of CH selection which are probabilistic and non-probabilistic methods. JCRP used non-probabilistic method for CH selection which depends on the residual energy and network connectivity. There are multiple levels of clusters from sea to surface. CEB2R [ 30 ]:Cluster-based energy-efficient routing works by dividing the depth of the water into seven layers. The protocol itself is divided into three phases. These are the formation of clusters, the development of routes, and the transmission of data. During the first phase, the node with the highest battery power and also the highest memory is chosen as the cluster head. Route selection is done based on weighted values of neighboring nodes. RJLS [ 31 ]: In Robust joint localization and synchronization, the authors suggested a strategy based on time synchronization for forecasting underwater node position. To anticipate the position of nodes, they use the RWP model. Their method adjusts for the impacts of stratification while removing the bias produced by straight-line propagation. Stratification is induced by differences in pressure and temperature at different depths in the water. Their method, however, is not assessed in terms of energy efficiency. DMR [ 32 ]: The Delay Minimization Routing technique decreases latency while also saving energy. Each part of the network is split into four equal sections, each having a minor sink node in the middle. The primary sink node, located in the center of the sea surface, is connected to these subordinate sinks. A node with a minor sink node in its transmission range delivers data straight to the sink in the DMR algorithm. The network has a hotspot problem as a result of multi-hop. MS-GAOC [ 33 ]: The authors utilized multiple sinks in the network to address the hot-spot problem. The optimization method GA; was used by the authors for the selection of the CH node. The parameters used include energy, distance, and density of nodes. However, the use of four sinks increased the cost of the network tremendously. Table 1 gives the comparison of various routing protocols based on different performance metrics. Table 1 PERFORMANCE COMAPRISON OF ROUTING PROTOCOLS Protocol Network Lifetime EED delay Energy Efficiency Throughput MCOBR [ 23 ] POOR AVERAGE GOOD AVERAGE IBSO-VFA [ 24 ] POOR AVERAGE GOOD AVERAGE CSO [ 25 ] AVERAGE AVERAGE GOOD GOOD CUWSN [ 26 ] GOOD POOR POOR GOOD MFOBR [ 27 ] GOOD GOOD AVERAGE POOR MLCEE [ 28 ] POOR AVERAGE GOOD AVERAGE JCRP [ 29 ] GOOD POOR GOOD POOR CEB2R [ 30 ] AVERAGE POOR GOOD AVERAGE RJLS [ 31 ] GOOD AVERAGE AVERAGE POOR DMR [ 32 ] AVERAGE POOR GOOD GOOD MS-GAOC [ 33 ] GOOD AVERAGE GOOD POOR 3. Proposed Methodology 3.1 Assumptions for the proposed work Some network assumptions are considered for the proposed work. These assumptions need to be listed to understand the feasibility and circumstances of the implementation of the proposed work. 1. The nodes communicate in the acoustic medium wherein the low range and low data rate data transmission is performed. 2. The sensor nodes deployed underwater are energy limited and also subjected to hardware constraints due to the challenging conditions of the water environment. 3. The data transmission is subjected to the multi-path effect, propagation delay, and also fading. However, in this work, the primary focus is on the network layer which deals with the routing. The network consideration related to the physical layer is out of scope for this work. 4. Although, security is a major concern for IoUT as UWSN acoustic channel is open and is vulnerable to malicious activities. However, the network is assumed to be a secured one. As the focus is on energy-efficient routing. 5. The cooperative nodes are placed on the surface of the water and are fixed in number. For this work, 10 cooperative nodes, and 90 nodes are deployed beneath the surface. 6. The sink or data collecting node is energy unlimited. Hence, energy availability is not a concern with this node. The proposed work is performed in 3D i.e., three-dimensional scenario as the surface nodes have been considered and also the nodes placed underneath the surface of the water. Figure 2 . give the proposed routing scenario. The nodes are divided into clusters having a Cluster Head (CH). Each CH transmits the gathered information to the next CH which is lower in depth to itself and finally, the CHs close to the surface sent it to relay or cooperative nodes placed on the surface using the acoustic links. These cooperative nodes send the data eventually to the sink using the radio links. Table 2 gives the symbols used for mathematical modeling of the architecture. Table 2 Symbols used Symbols Meaning \({A}_{tt}\) Attenuation sp Spreading coefficient \({N}_{tur}\) Noise caused due to turbulence \({N}_{wave}\) Noise caused due to wave \({N}_{ship}\) Shipping noise \({N}_{ther}\) Thermal noise P Distance \({P}_{tx}\) Transmission power \({D}_{index}\) Directivity index 3.2 Energy model for underwater scenario The attenuation of the signal in UWSNs for the distance p is given as below. $$10logAtt\left(p,f\right)={s}_{p}\times logp+p\times log\alpha \left(f\right)$$ 1 In Eq. 1 , the spreading and absorption loss is illustrated. The spreading constant ‘s p ’ denotes the propagation geometry. For different values of s p , the spreading has different shapes. For cylindrical, s p =1, for spherical, s p =2, for practical scenarios, the s p =1.5. The noise prevailing in underwater communication is given below in Eq. 2 : $$Nl{N}_{T}\left(f\right)={N}_{tur}\left(f\right)+{N}_{ship}\left(f\right)+{N}_{wave}\left(f\right)+{N}_{therm}\left(f\right)$$ 2 where \({N}_{tur}\left(f\right)\) is turbulence noise, \({N}_{ship}\left(f\right)\) is shipping noise, \({N}_{wave}\left(f\right)\) is wave noise and \({N}_{therm}\left(f\right)\) is thermal noise. Signal to Noise Ratio(SNR) can be given as below in Eq. 3 : $$SNR\left(freq., p\right)={P}_{tx}\left(f\right)-{A}_{tt}\left(p,f\right)-{N}_{T}\left(f\right)+{D}_{index}$$ 3 3.3 Selection of Cluster Head The Cooperative nodes/relay nodes and CH nodes are selected based on the following parameters. 1. Energy of the nodes The nodes which are having the highest energy are considered CH. The function of the cluster head is to gather information from all the member nodes [ 33 ], remove the redundant information, and preprocess it, so the node with the highest energy is the best suitable candidate for cluster head selection. The parameter \({ P}_{1}\) , gives the energy value of a node to be selected as CH as shown in Eq. ( 4 ). $${P}_{1}={\sum }_{i=1}^{N}\frac{{E}_{rs}\left(i\right)}{{E}_{in}\left(i\right)}$$ 4 2. Distance of the node from the cooperative node The other parameter is the distance, as it is computed from the other nodes on the surface of the water. The cooperative nodes are located on the surface of the water and act as intermediary sinks for the cluster heads CHs. The more the distance from the cooperative node the more will be the energy expenditure by the CH. The parameter \({P}_{2}\) gives the distance value of a node to be selected as CH as shown in Eq. ( 5 ). $${P}_{2}={\sum }_{i=1}^{N}\frac{{D}_{ns}\left(i\right)}{{D}_{avg}\left(i\right)}$$ 5 3. Node density Finally, this factor is considered as the nodes with the highest node density, which will be preferred for the selection as CH. If the density around a node is higher, this means its connectivity with other nodes is good and has a large number of nodes in its vicinity. This also affects the energy consumed by the neighboring nodes in sending the packets to CH, because if a node with sparse deployment around itself is chosen as CH, then it leads to more energy expenditure by the member nodes of that cluster. The parameter \({P}_{3}\) gives the energy value of a node to be selected as CH as shown in Eq. (6). \({P}_{3}={\sum }_{i=1}^{N}N{D}_{ns}\left(i\right)\) (6) 4. Cumulative factor All the above-discussed parameters P 1 for residual energy of the nodes, P 2 for a distance of probable cluster head candidate from the cooperative node, and P 3 is the node density in the vicinity of probable candidate combined for determining the selection index for CH as shown in Eq. ( 7 ) $${C}_{f\left(i\right)}=\frac{{P}_{1}\left(i\right)+{P}_{3}\left(i\right)}{{P}_{2}\left(i\right)}$$ 7 It is noted that the nodes with the highest cumulative factor, are selected as Relay node/ cooperative nodes and the second-highest are termed as CH. 4. End-to-End Delay (EED) It is the time taken to deliver a data packet from a source node to the sink node, a very important parameter for time-critical applications is required for designing applications where time is a major consideration.EED is given by Eq. ( 8 ) $$EED= \frac{{\sum }_{n=1}^{{P}_{r}}({T}_{rp}-{T}_{sp})}{{P}_{r}}$$ 8 Where T rp is the time when the sink node receives a data packet and T sp is the time when the source node sends the packet. P r is the number of packets received successfully by the sink node. 3.4 Steady-state phase In this phase, the discussion for the data transmission from the sensor nodes to the sink is done. Communication occurs in two mediums; acoustic and radio wireless medium. The intra-cluster communication occurs in the acoustic medium. However, the data from the selected CH node is forwarded to the relay or the cooperative node is done by considering the various parameters discussed above. In the end, multi-hop communication is adapted for the transmission of data. 4. Simulation And Results In this work, the simulations are performed in MATLAB Software version R2019a. The parameters and their values considered in this work are mentioned in Table 3 . The performance comparison of the proposed protocol is done with some state-of-the-art protocols such as CSO, MFPBR, and DMR. The same number of nodes are utilized during all simulations for all four schemes (CSO, MFOBR, DMR, and proposed). In the 3D network area, 100 sensor nodes are deployed. For every node, the initial energy is 0.5 joules. And for cooperative nodes, the initial energy is 1 joule. There is some performance evaluating parameters that are discussed as follows. Table 3 Simulation parameters for proposed work Parameters Values Number of sinks 1 Number of nodes 100 Network area 100, 100, 100 Underwater sensors 100 Underwater sensor nodes’ energy 0.5 Joules Number of surface nodes acting as a cooperative node 10 Energy of the cooperative node 1 Joule Packet size 4 byte 4.1 Reliability index This index determines the stability of the network in the context of all nodes working. In other words, this parameter is computed in terms of rounds completed before any one node of the network is found to be dead. As shown in Fig. 3 , the stability period of the proposed protocol is 2275 rounds whereas the protocols DMR, MFOBR, and CSO acquire the stability period of 1778, 1160, and 813 rounds, respectively. The reason for the improvement for the proposed work is the use of energy-efficient CH and Relay node selection which explores residual energy, distance, and other crucial parameters. 4.2 Network survival period This parameter helps in the evaluation of the network in terms of the operational period of all sensor nodes. Hence, the moment the last node is dead, the rounds completed by then are said to be the network survival period. This is an important factor because it keeps the network fully connected. Also, it points to the sustainability of the network. As shown in Fig. 3 , Fig. 4 , and Fig. 5 , the network survival period of the proposed protocol is 4238 rounds whereas the protocols DMR, MFOBR, and CSO acquire the stability period of 3511, 2125, and 1617 rounds, respectively. The reason for acquiring enhanced network lifetime is the concept of relay node incorporated in the network. 4.3 End to End Delay The delay involved in transmitting data packets is highly not affordable in critical applications. In this work, the End-to-end delay involved in the data transmission by the proposed work is calculated in comparison to the state-of-the-artwork. In the underwater scenario, this parameter also holds great significance as it determines in what unit time, the number of packets are being sent. As shown in Fig. 6 , the proposed protocol sends 88289 data packets whereas the other protocols DMR, MFOBR, and CSO send 70722, 40865, and 36754 packets, respectively. This transmission of packets is considered in different intervals. As it is clear from Fig. 5 , the proposed protocols at any given period, i.e., if it is 2000 rounds or seconds, the data packets transmitted are more than any other protocol. It is due to the use of radio wireless medium by a number of relay nodes which are present on the water surface and also because multihop transmission among sensor nodes is avoided by optimal CH selection and only these CHs communicate with the relay nodes for the data transmission to the sink whereas the medium of data transmission is acoustic in the other. 4.4 Status of network energy This parameter determines the status of energy of the whole network. In every round, the total energy is the energy consumed in forwarding the packet, receiving the packet and its overhearing. This parameter helps in understanding the rate at which the energy of the network decreases. As shown in Fig. 7 , the proposed protocol covers more rounds as compared to other protocols with the available stock of energy. It is due to the fact that routing is not performed n multiple hops but through cluster heads and also because relay nodes deployed on the water surface communicate using radio medium which is faster than acoustic communication. 5. Conclusion In this paper, the strategic cooperative routing is performed by placing the cooperative nodes on the surface of the water, whereas the other nodes are deployed on underwater shores. The cooperative nodes collect data from the selected CH and forward it to the sink. The CH selection is performed by considering energy and distance factors. For designing energy-efficient underwater sensor networks, it is required that resource constraints of the underwater nodes and environment be taken into consideration. The operating conditions are harsher than in terrestrial networks. The applications of UWSNs are also expanding into areas that represent harsher and generally more dangerous environments. The simulation results demonstrate the effectiveness of the proposed work as the proposed routing increases the reliability index by 28% and 96% and the network survival period of the network by 20% and 99% as compared to DMR and MFOBR protocols. In the future, the heterogeneity levels can be enhanced to bring the energy balancing in the network. Further, the physical characteristics of the acoustic medium for the data transmission can be investigated for better throughput and less SNR. Declarations Ethical Approval Not Applicable Competing interests Not Applicable Authors' contributions Swati Gupta and Niraj Pratap Singh wrote the manuscript text. Swati Gupta prepared the figures and tables. Both authors reviewed the manuscript. Funding Not Applicable Availability of data and materials Not Applicable References S. Karim, F. K. Shaikh, K. Aurangzeb, B. S. Chowdhry, and M. 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K., & Anand, V, “Fault resilient routing based on moth flame optimization scheme for underwater wireless sensor networks.” in Wireless Networks , 26 (2), pp.1417-1431,2020 Khan, W., Wang, H., Anwar, M. S., Ayaz, M., Ahmad, S., & Ullah, I., “A multi-layer cluster based energy efficient routing scheme for UWSNs.” IEEE Access , 7 , pp. 77398-77410., 2019 Zhou, J., Tang, F., Li, J., Xu, W., & Guo, M., “Joint channel assignment, stable routing and adaptive power control in mobile cognitive networks.” In 2015 IEEE Wireless Communications and Networking Conference (WCNC) , IEEE, pp. 1183-1188, 2015 M. Ahmed, M. Salleh, and M. I. Channa, "CBE2R: Clustered-based energy efficient routing protocol for underwater wireless sensor network", Int. J. Electron. , vol. 105, no. 11, pp. 1916-1930, 2018. Mortazavi, E., Javidan, R., Dehghani, M. J., & Kavoosi, V. (2017). A robust method for underwater wireless sensor joint localization and synchronization. Ocean Engineering , 137 , 276-286. Ullah, U., Khan, A., Altowaijri, S. M., Ali, I., Rahman, A. U., Kumar V, V., ... & Mahmood, H., “Cooperative and delay minimization routing schemes for dense underwater wireless sensor networks.” Symmetry , 11 (2), 195.2019 Gupta, S., & Singh, N. P., “Residual Energy and Throughput Enhancement in Underwater Sensor Network Routing Using Backward Forwarding” in Computer Communication, Networking, and IoT Springer, Singapore,pp. 527-535.,2021 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2314480","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":155460263,"identity":"c04e6c59-248d-4df4-bbb3-07c11121265d","order_by":0,"name":"Swati Gupta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYLCCBDDJ2PzgA5BiYyesgbEBpIWHgbnNcAZICzMxWhjAWtgbpHlALEJa5GckP3/w4NfhPHv2gw3GNr+2yfMxMzB++JiDW4vBjTTDhsS+w8U8PIkNj3P7bhu2MTMwS87chkeLdAJQS8/hxB6GxAbj3J7bjEAtbMy8eLTIz07/CNHC/7BB2rLntj1BLQy3cwwbEn4AtUgkNkgz/LidSFCLwf03hTMSG9KLeW48bDPsbbid3MbM2IzXL/I9xzd8/PHHOo+9P/3xgx9/btvOb28++OEjPoeBAGMbLAG0gckGAupB4A9UC5AxCkbBKBgFowADAADqrVj0C9BD5wAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute of Technology Kurukshetra","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Swati","middleName":"","lastName":"Gupta","suffix":""},{"id":155460264,"identity":"ac8b9c70-afed-46c7-955a-69ecc930ae09","order_by":1,"name":"Niraj Pratap Singh","email":"","orcid":"","institution":"National Institute of Technology Kurukshetra","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Niraj","middleName":"Pratap","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2022-11-26 07:14:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2314480/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2314480/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29728562,"identity":"c5c729b2-ba79-4888-b8cd-85939a81b6d8","added_by":"auto","created_at":"2022-11-30 16:06:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84098,"visible":true,"origin":"","legend":"\u003cp\u003eBasic Architecture for IoUT\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2314480/v1/1cb22d50da5a70c51370772e.png"},{"id":29730338,"identity":"6d07ff4e-bd16-4e4f-bad4-74db86019adf","added_by":"auto","created_at":"2022-11-30 16:22:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197703,"visible":true,"origin":"","legend":"\u003cp\u003eProposed routing scenario for IoUT\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2314480/v1/2f9b408ec669dfbc1d55b8fd.png"},{"id":29729555,"identity":"950347e7-5474-44ff-80ad-f1b582e28cad","added_by":"auto","created_at":"2022-11-30 16:14:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23444,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance comparison of proposed SC\u003csup\u003e2\u003c/sup\u003eR\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2314480/v1/06bb9cc2481f46948bee432a.png"},{"id":29728563,"identity":"ff62c370-1c35-4ee9-a56c-bfd258cbfb1d","added_by":"auto","created_at":"2022-11-30 16:06:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23044,"visible":true,"origin":"","legend":"\u003cp\u003eAlive nodes comparison\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2314480/v1/9851bddc77c6f3a7b6dd7f14.png"},{"id":29728568,"identity":"9b97601a-4ad8-4a0b-809a-a71fb414a4f2","added_by":"auto","created_at":"2022-11-30 16:06:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24140,"visible":true,"origin":"","legend":"\u003cp\u003eDead nodes comparison\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2314480/v1/9501fcad5fa8fd5986f4d50d.png"},{"id":29729554,"identity":"8ac74eb4-07b5-4f03-a62d-3e7155fe5961","added_by":"auto","created_at":"2022-11-30 16:14:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":21348,"visible":true,"origin":"","legend":"\u003cp\u003eEnd-to-end delay comparison\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2314480/v1/a09d21e1fad828dd239a0f39.png"},{"id":29728564,"identity":"348d58fa-004f-4e48-832f-f83f5c7565e0","added_by":"auto","created_at":"2022-11-30 16:06:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":24086,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork’s remaining energy\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2314480/v1/497a50132d0d8cc8f71e5fc8.png"},{"id":30486900,"identity":"bb6c99ba-f601-42d3-b393-84c4dcb86249","added_by":"auto","created_at":"2022-12-18 20:14:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":780917,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2314480/v1/38207f70-b4fe-423e-abb9-7659048052c1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SC 2 R: Strategic cooperative cluster-based routing for Internet of Underwater Things","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eExploration of the undersea environment is essential since water covers a large portion of our globe. A sink node with both acoustic and radio modems and sensor nodes with acoustic modems make up the Underwater Wireless Sensor Network (UWSN) and when it is associated with Internet of Things (IoT), it becomes the IoUT [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. IoUT is a new category of IoT and is considered to be one of the recent technologies towards the building of a smart city. IoUT is defined as \u0026ldquo;the network of smart interconnected underwater objects\u0026rdquo;. For this UWSNs have surfaced as a promising system. Rivers, lakes, and abysses are all covered via these networks. Naval data gathering, military training, aquatic pollution monitoring, politic surveillance, submarine discovery, disaster avoidance, submarine ecosystem monitoring and other applications are only a few examples [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the basic architecture of IoUT. For IoUT, the basic architectural design is of UWSN. The UWSN is made up of sensor nodes that are linked together by acoustic connections and interact via protocols. The nodes collect data from the hostile underwater environment and send it to offshore stations, also known as sinks, that are installed at the water's surface. The underwater sensor nodes are placed meagerly from the layer of the onshore surface to seabed-layer for bringing the information from the submerged callous environmental conditions by utilizing an acoustic modem [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSensors in UWSN have the ability of detecting, and communicating with neighboring or to say other sensors. In UWSNs, the sensors are spread in a three-dimensional (3D) space in underwater climates [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Since the principal reason for UWSNs is to gather information at whatever point an incident happens, the sensor nodes should be placed to cover the entire region under observation. Subsequently, a path between a source to the expected sink should be set up for powerful and dependable information transmission. Communication is performed with their neighbors using acoustic modems, whereas sink nodes use combined radio and acoustic modems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Acoustic signals are used by UWSNs for communication because they are very less attenuated in water than conventional radio waves, which are substantially attenuated.\u003c/p\u003e \u003cp\u003eThere are different advantages of underwater sensor networks. First is real-time monitoring, something that is very important [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Standalone sensor nodes which are dipped underwater can only record information that is occurring around them once, but that is a traditional way in which real-time monitoring is not possible. By recovering these nodes which are sinking underwater and the stored data can be obtained from them, but using underwater sensor networks one can do real-time monitoring of the region or the oceanic column around which these underwater sensor networks are deployed. There are various issues with underwater acoustic communication [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Firstly, the speed of propagation of an acoustic transmission is several orders lower than that of a radio signal, leading to a longer end-to-end delay (E2ED).Due to repeated topological changes of sensor nodes caused by water currents, node densities in various sections of the network become unbalanced. Further, nodes in low-density regions degrade in a relatively short period. Acoustic communication has a low bandwidth, which translates to a low data rate and, as a result, a sluggish information transmission method. Diffraction, Reflection, and refraction processes have a significant impact on the physical characteristics of auditory waves. Changes in pressure, salinity, and temperature for example, affect the speed of acoustic transmissions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Since underwater sensors are power-driven by batteries, which are expensive to replenish or replace, energy is a limited resource. Water currents modify the network topology often, and based on the pace of change, the system must adapt to new topology control regularly. Inter Symbol Interference (ISI) is caused by delays and Doppler spreads, and the trustworthiness of data originality diminishes as a result.\u003c/p\u003e \u003cp\u003eThe routing methods built for traditional sensor networks cannot operate adequately for UWSNs due to the aforementioned concerns. Furthermore, environmental factors such as fouling and corrosion alter the physical characteristics of sensor nodes, limiting the lifetime of the network of underwater sensor devices. Moreover, because of node mobility and also their unpredictable failures, the topology of the network keeps on changing. Also, the architecture being 3-dimensional (3D), being different from terrestrial wireless sensor networks (TWSNs) requires various issues to be dealt with differently.\u003c/p\u003e \u003cp\u003eEnd-to-end delay is a crucial aspect that needs to be addressed while considering the critical applications for which IoUT is designed [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A sprinkling of efforts is reported in this direction, however, it is anticipated that once the end-to-end delay is reduced, the data delivery will help in serving the human or marine resources from any catastrophe. Once, the number of packets being transmitted is known, it will set up the time taken to deliver those packets.\u003c/p\u003e \u003cp\u003eWith technological advances in underwater networks, sensors are becoming smaller, smarter, and flexible with less power consumption, an increase in the processing capabilities, and also, their capability to operate in underwater scenarios. Also, UWSN technology can be incorporated along with Internet Protocol-based systems for supporting the IoT and also machine-to-machine (M2M) frameworks for monitoring. Since the development of IoUT, the energy-limited sensor-based IoT devices have been facing challenges of limited network lifetime.3D deployment of nodes imposes further challenges [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Sensor nodes must have the capability of transmitting data to the sink. The nodes should be able to adjust their depths for finding a suitable relay if no accessible relay could be found using the current transmission range. Because the nodes are distributed at random and energy harvesting is not possible, the void hole problem develops, which is considered the most difficult challenge in routing protocol design. Further, it is also observed that the data transmission in acoustic medium follows the multi-hop transmission which eventually leads to energy hole problem [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It has been studied that cooperative routing has helped tremendously in bringing energy balancing to the network. Hence, if the cooperative nodes are used along with the CH nodes which will be placed above the surface of the water, may help in the early delivery of data and also can lead to early delivery of data to the sink. The routing protocol hence designed should be robust and self-adaptive, an important requirement for networks operating in ruthless underwater environments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe key contributions of the paper are listed below.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIt discusses the latest papers investigating the intermingling for Internet of Underwater Things applications.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIt proposes strategic cooperative cluster-based routing for time-critical applications for IoUT.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe proposed method deploy cooperative nodes which are kept higher in energy to the other nodes, these nodes collect data from the nearest CH in a single hop and hence, relay data to sink through the intermediate cooperative nodes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe CH selection is done based on the node energy and distance from the nearest cooperative node.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFinally, performance validation is performed against recently proposed routing algorithms in underwater WSN.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eImprovement in network lifetime and end-to-end delay is achieved with the proposed algorithm.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe rest of the paper is given as follows. Section 2 presents the literature work, in Section 3, the proposed work is explained, Section 4 discusses the results of the proposed work and finally, Section 5 concludes the paper.\u003c/p\u003e"},{"header":"2. Related Work","content":"\u003cp\u003eThere has been a great magnitude of research as far as an energy-efficient routing in underwater scenarios is concerned [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Designing a routing protocol is a critical task since it provides varied needs for acoustic communication to spot and maintain network routes. By capturing data and sending it to ground stations, UWSNs serve an important role in ocean research and monitoring. The sensor nodes when deployed underwater needs to sustain for a longer period to deliver optimized performance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It is due to the reason that circumstances in the underwater scenario are dynamic and needs to be given special care when it comes to routing [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Energy-efficient routing has been targeted by various researchers under the umbrella of UWSN [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In several ways, UWSN differs from standard land-based WSNs, in terms of bandwidth, long propagation delays, floating nodes, and power efficiency.\u003c/p\u003e \u003cp\u003eBecause the underwater environment is so complicated, nodes' batteries cannot be changed recurrently. As a result, the energy constraint is the most important aspect in underwater routing systems [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These protocols demand that the nodes in the path consume as little energy as possible, which is critical for enhancing the lifetime of the network. Given the available energy of the sensor or the energy demand on the transmission network, energy-efficient routing methods pick the most optimal route for data forwarding from the source to the destination [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a result, effective routing protocols are required because they provide stable and dependable data transport from source to destination nodes. Several protocols are developed and investigated within the past and gift to boost UWSN performance. The authors considered the latest research of routing protocols in UWSNs and discovered that the primary consideration while designing the routing protocol is its energy efficiency, its lifetime, and also EED where time-critical applications are considered. The major task is to keep the network operational with whatever little energy in nodes is there [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Several protocols have been discussed considering all these factors.\u003c/p\u003e \u003cp\u003eMany underwater communication routing algorithms have been suggested over the last few decades, and they may be loosely split into various categories.\u003c/p\u003e \u003cp\u003eMCOBR [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]: To address the concerns of multi-path fading, low throughput, sinkhole, etc. the authors proposed the MCBOR algorithm. In this authors exploited the sense of butterflies for delivering data without any loss. The increase in PDR and reduction in transmission loss have been targeted.\u003c/p\u003e \u003cp\u003eIBSO-VFA [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]:In this author discussed the sparsely populated scenario which may lead to energy holes and making it difficult for target location. The author proposed an enhanced version of brain storm optimization which is integrated with the virtual force algorithm (IBSO-VFA) which helped in improvement of the UWSN coverage performance.\u003c/p\u003e \u003cp\u003eCSO [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]: In this, the author explored the concept of water current which makes the node move from their position of deployment. The CSO-based approach is adapted to identify articulation points in the network. The delay and threshold parameters are targeted in this work. However, due to an excessive number of hops among the nodes, it suffers from the hot-spot problem.\u003c/p\u003e \u003cp\u003eCUWSN [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]: The authors proposed a multi-hop-based communication for the reduction of energy expenditure. The authors\u0026rsquo; select cluster coordinator and CH and various parameters are examined as the performance check for the proposed work by the authors. However, as this protocol follows multi-hop communication, it faces the hot-spot problem. The authors have given no provision to deal with such a scenario.\u003c/p\u003e \u003cp\u003eMFOBR [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]: The authors worked on the fault resilience for the UWSN. These faults to the nodes could be caused by various creatures, disasters, etc. The authors proposed MFOBR which uses Autonomous Underwater Vehicles (AUVs) for dealing with link failure problems. The authors selected the best possible node for the collection of data and hence, forwarding to the nearest AUVs. However, due to multi-hop communication, it suffers from a hot-spot problem.\u003c/p\u003e \u003cp\u003eMLCEE [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]: This protocol works in three steps. The first step is division of network into layers, starting from sea bed to sea surface, making clusters within the layer, and finally data forwarding to the sink node. The layer next to the surface remains unclustered and the nodes present in it relay the data directly to sink. In this algorithm, CH selection was based on Bayesian probability.\u003c/p\u003e \u003cp\u003eJCRP [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]: There are two methods of CH selection which are probabilistic and non-probabilistic methods. JCRP used non-probabilistic method for CH selection which depends on the residual energy and network connectivity. There are multiple levels of clusters from sea to surface.\u003c/p\u003e \u003cp\u003eCEB2R [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]:Cluster-based energy-efficient routing works by dividing the depth of the water into seven layers. The protocol itself is divided into three phases. These are the formation of clusters, the development of routes, and the transmission of data. During the first phase, the node with the highest battery power and also the highest memory is chosen as the cluster head. Route selection is done based on weighted values of neighboring nodes.\u003c/p\u003e \u003cp\u003eRJLS [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]: In Robust joint localization and synchronization, the authors suggested a strategy based on time synchronization for forecasting underwater node position. To anticipate the position of nodes, they use the RWP model. Their method adjusts for the impacts of stratification while removing the bias produced by straight-line propagation. Stratification is induced by differences in pressure and temperature at different depths in the water. Their method, however, is not assessed in terms of energy efficiency.\u003c/p\u003e \u003cp\u003eDMR [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]: The Delay Minimization Routing technique decreases latency while also saving energy. Each part of the network is split into four equal sections, each having a minor sink node in the middle. The primary sink node, located in the center of the sea surface, is connected to these subordinate sinks. A node with a minor sink node in its transmission range delivers data straight to the sink in the DMR algorithm. The network has a hotspot problem as a result of multi-hop.\u003c/p\u003e \u003cp\u003eMS-GAOC [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]: The authors utilized multiple sinks in the network to address the hot-spot problem. The optimization method GA; was used by the authors for the selection of the CH node. The parameters used include energy, distance, and density of nodes. However, the use of four sinks increased the cost of the network tremendously.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e gives the comparison of various routing protocols based on different performance metrics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePERFORMANCE COMAPRISON OF ROUTING PROTOCOLS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtocol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetwork Lifetime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEED delay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnergy Efficiency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThroughput\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMCOBR\u003c/b\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIBSO-VFA\u003c/b\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSO\u003c/b\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCUWSN\u003c/b\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMFOBR\u003c/b\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMLCEE\u003c/b\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJCRP\u003c/b\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCEB2R\u003c/b\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRJLS\u003c/b\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDMR\u003c/b\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMS-GAOC\u003c/b\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGOOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePOOR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"3. Proposed Methodology","content":"\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e3.1 Assumptions for the proposed work\u003c/h2\u003e\n \u003cp\u003eSome network assumptions are considered for the proposed work. These assumptions need to be listed to understand the feasibility and circumstances of the implementation of the proposed work.\u003c/p\u003e\n \u003cp\u003e1. The nodes communicate in the acoustic medium wherein the low range and low data rate data transmission is performed. \u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e2. The sensor nodes deployed underwater are energy limited and also subjected to hardware constraints due to the challenging conditions of the water environment.\u003c/p\u003e \u003cspan\u003e\n \u003cp\u003e3. The data transmission is subjected to the multi-path effect, propagation delay, and also fading. However, in this work, the primary focus is on the network layer which deals with the routing. The network consideration related to the physical layer is out of scope for this work.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e4. Although, security is a major concern for IoUT as UWSN acoustic channel is open and is vulnerable to malicious activities. However, the network is assumed to be a secured one. As the focus is on energy-efficient routing.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e5. The cooperative nodes are placed on the surface of the water and are fixed in number. For this work, 10 cooperative nodes, and 90 nodes are deployed beneath the surface.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e6. The sink or data collecting node is energy unlimited. Hence, energy availability is not a concern with this node.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe proposed work is performed in 3D i.e., three-dimensional scenario as the surface nodes have been considered and also the nodes placed underneath the surface of the water. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. give the proposed routing scenario. The nodes are divided into clusters having a Cluster Head (CH). Each CH transmits the gathered information to the next CH which is lower in depth to itself and finally, the CHs close to the surface sent it to relay or cooperative nodes placed on the surface using the acoustic links. These cooperative nodes send the data eventually to the sink using the radio links. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e gives the symbols used for mathematical modeling of the architecture.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSymbols used\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSymbols\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeaning\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{tt}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttenuation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpreading coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({N}_{tur}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNoise caused due to turbulence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({N}_{wave}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNoise caused due to wave\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({N}_{ship}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShipping noise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({N}_{ther}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThermal noise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{tx}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransmission power\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{index}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirectivity index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e3.2 Energy model for underwater scenario\u003c/h2\u003e\n \u003cp\u003eThe attenuation of the signal in UWSNs for the distance p is given as below.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$10logAtt\\left(p,f\\right)={s}_{p}\\times logp+p\\times log\\alpha \\left(f\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn Eq. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the spreading and absorption loss is illustrated. The spreading constant \u0026lsquo;s\u003csub\u003ep\u003c/sub\u003e\u0026rsquo; denotes the propagation geometry. For different values of s\u003csub\u003ep\u003c/sub\u003e, the spreading has different shapes. For cylindrical, s\u003csub\u003ep\u003c/sub\u003e=1, for spherical, s\u003csub\u003ep\u003c/sub\u003e=2, for practical scenarios, the s\u003csub\u003ep\u003c/sub\u003e=1.5. The noise prevailing in underwater communication is given below in Eq. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ2\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$Nl{N}_{T}\\left(f\\right)={N}_{tur}\\left(f\\right)+{N}_{ship}\\left(f\\right)+{N}_{wave}\\left(f\\right)+{N}_{therm}\\left(f\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({N}_{tur}\\left(f\\right)\\)\u003c/span\u003e\u003c/span\u003e is turbulence noise,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({N}_{ship}\\left(f\\right)\\)\u003c/span\u003e\u003c/span\u003e is shipping noise, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({N}_{wave}\\left(f\\right)\\)\u003c/span\u003e\u003c/span\u003e is wave noise and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({N}_{therm}\\left(f\\right)\\)\u003c/span\u003e\u003c/span\u003e is thermal noise.\u003c/p\u003e\n \u003cp\u003eSignal to Noise Ratio(SNR) can be given as below in Eq. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ3\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$SNR\\left(freq., p\\right)={P}_{tx}\\left(f\\right)-{A}_{tt}\\left(p,f\\right)-{N}_{T}\\left(f\\right)+{D}_{index}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e3.3 Selection of Cluster Head\u003c/h2\u003e\n \u003cp\u003eThe Cooperative nodes/relay nodes and CH nodes are selected based on the following parameters.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e1. Energy of the nodes\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe nodes which are having the highest energy are considered CH. The function of the cluster head is to gather information from all the member nodes [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], remove the redundant information, and preprocess it, so the node with the highest energy is the best suitable candidate for cluster head selection. The parameter\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ P}_{1}\\)\u003c/span\u003e\u003c/span\u003e, gives the energy value of a node to be selected as CH as shown in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ4\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$${P}_{1}={\\sum }_{i=1}^{N}\\frac{{E}_{rs}\\left(i\\right)}{{E}_{in}\\left(i\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cspan\u003e\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e2. Distance of the node from the cooperative node\u003c/span\u003e\u003c/p\u003e\u003c/span\u003e\u003cp\u003eThe other parameter is the distance, as it is computed from the other nodes on the surface of the water. The cooperative nodes are located on the surface of the water and act as intermediary sinks for the cluster heads CHs. The more the distance from the cooperative node the more will be the energy expenditure by the CH. The parameter \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{2}\\)\u003c/span\u003e\u003c/span\u003e gives the distance value of a node to be selected as CH as shown in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"Equation\" id=\"Equ5\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$${P}_{2}={\\sum }_{i=1}^{N}\\frac{{D}_{ns}\\left(i\\right)}{{D}_{avg}\\left(i\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cspan\u003e\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e3. Node density\u003c/span\u003e\u003c/p\u003e\u003c/span\u003e\u003cp\u003eFinally, this factor is considered as the nodes with the highest node density, which will be preferred for the selection as CH. If the density around a node is higher, this means its connectivity with other nodes is good and has a large number of nodes in its vicinity. This also affects the energy consumed by the neighboring nodes in sending the packets to CH, because if a node with sparse deployment around itself is chosen as CH, then it leads to more energy expenditure by the member nodes of that cluster. The parameter \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{3}\\)\u003c/span\u003e\u003c/span\u003e gives the energy value of a node to be selected as CH as shown in Eq.\u0026nbsp;(6).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u0026nbsp;\u003c/div\u003e\u003ctable border=\"1\" id=\"Taba\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{3}={\\sum }_{i=1}^{N}N{D}_{ns}\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch3\u003e4. Cumulative factor \u003c/h3\u003e\u003cp\u003eAll the above-discussed parameters \u003cem\u003eP\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e for residual energy of the nodes, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e for a distance of probable cluster head candidate from the cooperative node, and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e is the node density in the vicinity of probable candidate combined for determining the selection index for CH as shown in Eq. (\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\u003cdiv class=\"Equation\" id=\"Equ6\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e$${C}_{f\\left(i\\right)}=\\frac{{P}_{1}\\left(i\\right)+{P}_{3}\\left(i\\right)}{{P}_{2}\\left(i\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003cp\u003eIt is noted that the nodes with the highest cumulative factor, are selected as Relay node/ cooperative nodes and the second-highest are termed as CH.\u003c/p\u003e\u003cspan\u003e\u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003e4. End-to-End Delay (EED)\u003c/span\u003e\u003c/p\u003e\u003c/span\u003e\u003cp\u003eIt is the time taken to deliver a data packet from a source node to the sink node, a very important parameter for time-critical applications is required for designing applications where time is a major consideration.EED is given by Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\u003cdiv class=\"Equation\" id=\"Equ7\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e$$EED= \\frac{{\\sum }_{n=1}^{{P}_{r}}({T}_{rp}-{T}_{sp})}{{P}_{r}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere T\u003csub\u003erp\u003c/sub\u003e is the time when the sink node receives a data packet and T\u003csub\u003esp\u003c/sub\u003e is the time when the source node sends the packet. P\u003csub\u003er\u003c/sub\u003e is the number of packets received successfully by the sink node.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e3.4 Steady-state phase\u003c/h2\u003e\n \u003cp\u003eIn this phase, the discussion for the data transmission from the sensor nodes to the sink is done. Communication occurs in two mediums; acoustic and radio wireless medium. The intra-cluster communication occurs in the acoustic medium. However, the data from the selected CH node is forwarded to the relay or the cooperative node is done by considering the various parameters discussed above. In the end, multi-hop communication is adapted for the transmission of data.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Simulation And Results","content":"\u003cp\u003eIn this work, the simulations are performed in MATLAB Software version R2019a. The parameters and their values considered in this work are mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The performance comparison of the proposed protocol is done with some state-of-the-art protocols such as CSO, MFPBR, and DMR. The same number of nodes are utilized during all simulations for all four schemes (CSO, MFOBR, DMR, and proposed). In the 3D network area, 100 sensor nodes are deployed. For every node, the initial energy is 0.5 joules. And for cooperative nodes, the initial energy is 1 joule. There is some performance evaluating parameters that are discussed as follows.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimulation parameters for proposed work\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of sinks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100, 100, 100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderwater sensors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderwater sensor nodes\u0026rsquo; energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 Joules\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of surface nodes acting as a cooperative node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy of the cooperative node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 Joule\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePacket size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 byte\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Reliability index\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis index determines the stability of the network in the context of all nodes working. In other words, this parameter is computed in terms of rounds completed before any one node of the network is found to be dead. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the stability period of the proposed protocol is 2275 rounds whereas the protocols DMR, MFOBR, and CSO acquire the stability period of 1778, 1160, and 813 rounds, respectively. The reason for the improvement for the proposed work is the use of energy-efficient CH and Relay node selection which explores residual energy, distance, and other crucial parameters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Network survival period\u003c/h2\u003e \u003cp\u003eThis parameter helps in the evaluation of the network in terms of the operational period of all sensor nodes. Hence, the moment the last node is dead, the rounds completed by then are said to be the network survival period. This is an important factor because it keeps the network fully connected. Also, it points to the sustainability of the network. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the network survival period of the proposed protocol is 4238 rounds whereas the protocols DMR, MFOBR, and CSO acquire the stability period of 3511, 2125, and 1617 rounds, respectively. The reason for acquiring enhanced network lifetime is the concept of relay node incorporated in the network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 End to End Delay\u003c/h2\u003e \u003cp\u003eThe delay involved in transmitting data packets is highly not affordable in critical applications. In this work, the End-to-end delay involved in the data transmission by the proposed work is calculated in comparison to the state-of-the-artwork. In the underwater scenario, this parameter also holds great significance as it determines in what unit time, the number of packets are being sent. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the proposed protocol sends 88289 data packets whereas the other protocols DMR, MFOBR, and CSO send 70722, 40865, and 36754 packets, respectively. This transmission of packets is considered in different intervals. As it is clear from Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the proposed protocols at any given period, i.e., if it is 2000 rounds or seconds, the data packets transmitted are more than any other protocol. It is due to the use of radio wireless medium by a number of relay nodes which are present on the water surface and also because multihop transmission among sensor nodes is avoided by optimal CH selection and only these CHs communicate with the relay nodes for the data transmission to the sink whereas the medium of data transmission is acoustic in the other.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Status of network energy\u003c/h2\u003e \u003cp\u003eThis parameter determines the status of energy of the whole network. In every round, the total energy is the energy consumed in forwarding the packet, receiving the packet and its overhearing. This parameter helps in understanding the rate at which the energy of the network decreases. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the proposed protocol covers more rounds as compared to other protocols with the available stock of energy. It is due to the fact that routing is not performed n multiple hops but through cluster heads and also because relay nodes deployed on the water surface communicate using radio medium which is faster than acoustic communication.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this paper, the strategic cooperative routing is performed by placing the cooperative nodes on the surface of the water, whereas the other nodes are deployed on underwater shores. The cooperative nodes collect data from the selected CH and forward it to the sink. The CH selection is performed by considering energy and distance factors. For designing energy-efficient underwater sensor networks, it is required that resource constraints of the underwater nodes and environment be taken into consideration. The operating conditions are harsher than in terrestrial networks. The applications of UWSNs are also expanding into areas that represent harsher and generally more dangerous environments. The simulation results demonstrate the effectiveness of the proposed work as the proposed routing increases the reliability index by 28% and 96% and the network survival period of the network by 20% and 99% as compared to DMR and MFOBR protocols. In the future, the heterogeneity levels can be enhanced to bring the energy balancing in the network. Further, the physical characteristics of the acoustic medium for the data transmission can be investigated for better throughput and less SNR.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSwati Gupta and Niraj Pratap Singh wrote the manuscript text. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSwati Gupta prepared the figures and tables.\u003c/p\u003e\n\u003cp\u003eBoth authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot Applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eS. 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A robust method for underwater wireless sensor joint localization and synchronization. \u003cem\u003eOcean Engineering\u003c/em\u003e, \u003cem\u003e137\u003c/em\u003e, 276-286.\u003c/li\u003e\n\u003cli\u003eUllah, U., Khan, A., Altowaijri, S. M., Ali, I., Rahman, A. U., Kumar V, V., ... \u0026amp; Mahmood, H., \u0026ldquo;Cooperative and delay minimization routing schemes for dense underwater wireless sensor networks.\u0026rdquo; \u003cem\u003eSymmetry\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 195.2019\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"32\" type=\"1\"\u003e\n\u003cli\u003eGupta, S., \u0026amp; Singh, N. P., \u0026ldquo;Residual Energy and Throughput Enhancement in Underwater Sensor Network Routing Using Backward Forwarding\u0026rdquo; in \u003cem\u003eComputer Communication, Networking, and IoT\u003c/em\u003e Springer, Singapore,pp. 527-535.,2021\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"IoUT, time-critical, underwater sensor network, cooperative routing","lastPublishedDoi":"10.21203/rs.3.rs-2314480/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2314480/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOceanographic data gathering, pollution monitoring, offshore exploration, catastrophe avoidance, aided navigation, and tactical surveillance are all expected to benefit from the Internet of Underwater Things (IoUT). However, the viability of various applications in underwater scenarios is possible only if the routing among the sensor nodes employed is strategically optimized. This paper proposes Strategic Cooperative Routing for IoUT (SC\u003csup\u003e2\u003c/sup\u003eR) that employs a cooperative node for data collection from each Cluster-Head (CH). CH selection is done through the energy and distance parameters. The cooperative nodes are positioned on water surface, other nodes being placed in the underwater terrain. This cooperative routing helps in the data collection for the time-critical scenario as it avoids multi-hop communication among the sensor nodes underwater. Due to decreased number of hops of communication, the delay in data transmission is reduced. The simulation results illustrate the efficacy of the proposed routing technique in comparison to competitive algorithms. The proposed protocol outperforms state of art routing protocols in terms of Network Lifetime and End to End Delay (EED).\u003c/p\u003e","manuscriptTitle":"SC 2 R: Strategic cooperative cluster-based routing for Internet of Underwater Things","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-30 16:06:02","doi":"10.21203/rs.3.rs-2314480/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80d8bf1e-a1f0-4945-80f7-fd12a381a541","owner":[],"postedDate":"November 30th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-12-18T20:14:18+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-30 16:06:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2314480","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2314480","identity":"rs-2314480","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}