Performance Analysis of Underwater Wireless Sensor Network by Deploying FTP, CBR, and VBR as Applications

Sathish, K.; Ravikumar, C. V.; Srinivasulu, Asadi; Gupta, Anand Kumar

doi:https://doi.org/10.1155/2022/7143707

Journal of Computer Networks and Communications

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Abstract Introduction Conclusion Abbreviations Data Availability Conflicts of Interest Authors’ Contributions References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 7143707 | https://doi.org/10.1155/2022/7143707

Performance Analysis of Underwater Wireless Sensor Network by Deploying FTP, CBR, and VBR as Applications

K. Sathish,¹C. V. Ravikumar,¹Asadi Srinivasulu,²and Anand Kumar Gupta²

Academic Editor: Giovanni Pau

Received26 Jul 2022

Accepted05 Sept 2022

Published23 Sept 2022

Abstract

Oceans cover more than 75% of the planet’s land surface, making it the most water-rich place on the Earth. We know very little about oceans because of the extraordinary activities that take place in the depths. Underwater wireless sensors are devices that are able to monitor and record the physical and environmental parameters of their surroundings, as well as transmit these data in a continuous manner to one of the source sensors. The network that is formed by the collection of these underwater wireless sensors is referred to as an underwater wireless sensor network (UWSN). The analysis of performance parameters is thought to be most effectively done with this particular technology. In this paper, we will investigate various performance parameters in a random waypoint mobility model by shifting the maximum speed of a node and altering the number of nodes in the model. These parameters include average transmission delay, average jitter, average pathloss, percentage of utilization, and energy consumed in transmit, receive, and idle modes. The QualNet 7.1 simulator is utilized in order to conduct analyses and performance studies.

1. Introduction

The vast majority of our planet is covered by water, but much of this has yet to be discovered [1–4]. This area’s exploration has recently seen a significant uptick in activity. Besides being rich in valuable resources, it has also played an important role in defence, transportation such as ships, and natural resources such as oil pipelines.The researcher’s most pressing concern in areas such as oceans, seas, and the lakes, contain enormous amounts of naturally occurring data [5, 6]. The developers have been able to gather and analyse a large amount of data to some extent by creating a variety of UWSN protocols for the underwater environment [7–10].The results of prior studies of UWSN, as described in the area background below, serve as a foundation for research in this area. Figure 1 shows that the general structure of underwater wireless communication consists of member nodes, cluster nodes, cluster heads, radio channel, and acoustic channel along with the underwater surface and an onshore link.

Wireless sensor networks (WSNs) are a type of ad-hoc wireless network that is used to offer a wireless communication set-up, i.e., as an application underwater wireless communication [11]. Ad-hoc On-demand Distance Vector (AODV), dynamic source routing protocol (DSR), Dynamic Manet on Demand Routing Protocol (DYMO), Location Aided Routing (LAR1), Bellman Ford, Optimized Link State Routing Protocol (OLSR), Fisheye, Source Tree Adaptive Routing Optimum Routing Approach (STAR-ORA), Zone Routing Protocol (ZRP), Source Tree Adaptive Routing-Least Overhead Routing Approach (STAR-LORA), and others are some of the routing protocols available for ad-hoc networks [12].

The mobility model depicts how nodes move and how their location, velocity, and acceleration change over time. It is critical to simulate and evaluate the protocol performance of a new network protocol when researching it. The mobility model and the communicating traffic pattern are two important parameters in protocol simulation. User movement patterns are described by mobility models. Models of traffic describe the state of mobile services [13–17]. Figure 2 represents the real scenario of underwater wireless communication with various nodes in different ranges of frequencies under sea level as well as above sea level.

When contemplating the use of underwater sensor networks, one must ensure that the potential difficulties posed by the surrounding subsurface environment are given the attention and consideration they deserve [18–20]. Major challenges posed by the host conditions include continuous node movement and three-dimensional topology. In addition, many of the applications that are used underwater, such as those for detection or rescue missions, have a tendency to be of an ad-hoc nature [20–26].

The main contribution of the manuscript is highlighted as follows:(i)To implement routing protocol for underwater wireless sensors network (UWSN).(ii)To compare the STAR-LORA and fisheye routing protocol with standard routing protocols in literature, namely, AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, and ZRP.(iii)To analyse the energy efficiency of the routing protocol with the increase number of underwater wireless sensors nodes.(iv)To analyse the trade-off between average transmission delay, average jitter, utilisation rate, and energy in transmit and receive modes.(v)To recommend appropriate routing protocol based on the targeted performance metric for underwater wireless sensors network.

Sections 2 covers design and simulation parameters. Section 3 gives the methodology of network scenario. In Section 4, we present investigational results. Section 5 provides simulation results. Finally, we have concluded in Section 6.

2. Design and Simulation Parameters

Generally, the design of UWSN is considered in various simulation tools such as Network Simulator, OPNET, OMNET, MATLAB, QUALNET, etc., [27–33]. In this proposed network, USWN is designed in QualNet simulator with various design parameters of UWSN as user friendly. The below mentioned parameters are the performance parameters of UWSN network in different applications. UWSNs have many research issues that affect design and performance of overall network such as long propagation delay, high energy consumption, and high dynamic topology [34, 35].

2.1. Average Transmission Delay

The information travelled from the source to the destination in successful transmission is referred as an average transmission delay.

2.2. Percentage of Utilization

A communication channel’s throughput is the percentage of packets that are effectively transferred from the transmitting node to the receiving node.

2.3. Average Jitter

It refers to the difference in time that occurs between individual packets as a result of changes in route or network congestion. In order to a routing protocol to work more effectively, it should be lower. Congestion on a network, changes in its routing, or timing drift all can contribute to jitter by causing a delay in transmission between individual packets.

2.4. Average Path Loss

The term “path loss,” also known as “path attenuation,” refers to the gradual reduction in power density that any electromagnetic wave experiences as it travels through space.

2.5. Energy Consumption

Energy consumption is the amount of power expended by nodes in the transmission of data from their point of origin to their point of destination.

3. Methodology of Network Scenario

3.1. Existing Network

There are approachable existing networks that are available with CBR as a deployment application. In this proposed network, FTP and VBR are considered along with CBR and finally compared the parameters with all the three FTP, CBR and VBR applications [13, 22, 26, 36–38].

3.2. Proposed Network

The proposed scenario is designed in Qualnet 7.1 Simulator with an area of 1500 by 1500 sq.mts. There are 60 nodes considered in which 15 are node devices, 25 are ship devices, and 20 are sensor devices which are connected with respect to file transfer protocol (FTP), constant bit rate (CBR), and variable bitrate (VBR) applications. The simulation runs for a total of 500 seconds. The node mobility model is set to random waypoint mobility with a minimum speed of 1.5 m/sec and a maximum speed of 3 to 10 m/sec. The initial routing protocol is AODV and followed by DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA. We examine the graphs in simulator after running the test. Thus, we obtain the necessary performance metrics: average transmission delay, average Jitter, average pathloss, percentage of utilization, and energy consumed in transmit, receive and idle modes. Figure 3 and Figure 4 represent proposed scenario of underwater wireless communication with various nodes in both X–Y and 3D- Visualization. Figure 5 and Figure 6 represent proposed scenario of underwater wireless communication with various nodes in both X–Y and 3D-Visualization [39, 40].

Clustering algorithms can make the use of methods that are either centralised or decentralised in order to select the CHs that will be the most helpful. When using centralised approaches, all of the necessary criteria for selecting CHs are compiled at a single node (often the BS) where they are then subjected to comparison, analysis, and processing. The expense of centralised techniques can be prohibitive for large and/or dynamic networks, especially when CHs are reselected on a frequent basis. This is the case despite the fact that their findings have the potential to be universal due to the fact that they compare all nodes. The transmission of several management packets, which can place a significant strain on a network’s resources, is required by such systems. The overhead of distributed techniques is reduced, but the selected CHs that these systems produce are often unable to match all of the requirements of the network because of the limited network information that they have (i.e., only neighbour nodes).

3.3. Random Waypoint Mobility Model

This model is typically used to assess the effectiveness of routing protocols in UWSN due to its simplicity and wide availability. The remote node in this proposed model utilises any random location as destination and moves in a randomly selected straight line through a constand speed. The amount of time taken by the node to reach at its destination is termed as length and is determined by the pause period of the remote node. This is repeated by the node throughout the simulation process [15].

4. Investigational Results

In this section, the flow chart for the proposed UWSN network scenario is shown in Figure 7. In the flow chart, based upon the nodes, deployments are done randomly and the paths are created for nodes. Then after, it needs to generate the traffic for configuring the required parameters and simulate the UWSN testing enviornment.

Also, the investigational results such as average transmission delay, average jitter, avrage path loss, percentage of utilization, engery consumed in transmit, rereive, and idle modes are obtained and discussed in below Figures 8(a)–17(g) represents FTP application. Figures 18(a)–27(g) describes VBR application for various routing protocols such as AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP and STAR-LORA with FTP, CBR and VBR applicatoins. These results are taken from Qualnet simulator under different application layers.

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5. Simulation Results

Here are the results of finding performance parameters of proposed UWSN network in FTP, CBR, and VBR applications. See Figure 28.

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5.1. Performance Parameters

The following are performance parameters of UWSN network in FTP, CBR, and VBR applications.

5.1.1. Energy (Milli Watt Hour-mWh) Consumed in Transmit Mode by AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA Routing Protocols

Comparison of AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA routing protocols in transmit mode with FTP, CBR and VBR applications is as shown in Figure 29. In proposed UWSN, minimum transmits energy is consumed on the maximum data size of AODV routing protocol in CBR application by 84.6% of DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA routing protocol, respectively, as illustrated in Table 1. For UWSN, the goal is to use as little transmit energy as possible. Other routing protocols cannot compete against AODV’s speed and reliability.

5.1.2. Energy (Milli Watt Hour-mWh) Consumed in Receive Mode by AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA Routing Protocols

Figure 30 presents a comparison of the amount of receive energy consumed by the routing protocols AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA with FTP, CBR, and VBR applications. When compared to the other routing protocols shown in Table 1, such as DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA, the AODV routing protocol uses 78.5 percent in CBR application and receives less energy than those other protocols. When compared to other routing protocols, the AODV routing protocol consumes significantly and receives less energy than other routing protocols do when dealing with larger packet sizes. UWSN should consume the least amount of energy possible when receiving data. The AODV routing protocol outperforms other routing protocols in terms of performance.z

5.1.3. Energy (Milli Watt Hour-mWh) Consumed in Idle Mode by AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA Routing Protocols

Idle energy consumption by AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA routing protocols with FTP, CBR, and VBR applications is as shown in Figure 31. Increased packet size reduces the amount of idle energy that must be expended. More energy is saved when using the OLSR routing protocol by 75% instead of the other in CBR application in Table 1, even when sending larger packets. Energy consumption during idle time is undesirable for UWSN. In terms of performance, the OLSR routing protocol is superior to the other protocols in this category.

5.1.4. Average Transmission Delay (µsec) by AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA Routing Protocols

AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA are compared with one another in terms of their average delay in Figure 32. As can be seen in Table 1, the AODV routing protocol results in an average delay that is 89.4 percent shorter than that of other routing protocol in CBR application.

5.1.5. Percentage of Utilization by AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA Routing Protocols

Table 1 shows that the Fisheye routing protocol achieved 92.3 percent higher percentage of utilization in the CBR application than other routing protocols, and Figure 33 shows the percentage of utilization produced by AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA.

5.1.6. Average Pathloss (dB) by AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA Routing Protocols

The comparison of average Pathloss in current network is shown in Figure 34. DSR achieved 0.3% less average Pathloss in comparison with DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA routing protocols as depicted in Table 1.

5.1.7. Average Jitter (µsec) by AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA Routing Protocols

As shown in Figure 35, each routing protocol has an average latency that is depicted in terms of jitter. When compared to other routing protocols in the proposed network, STAR-LORA produces 86.4 percent less average jitter than the others shown in Table 1.

Table 1 shows the comparison of all routing protocols AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA when applied to UWSN networks with variable deployment applications such as FTP, CBR, and VBR.

6. Conclusion

The monitoring of underwater resources, the investigation of parameters, and the planning of military action are all aspects that are developing alongside exploration of an underwater environment. Due to the fact that UWSN is only capable of performing certain tasks, the extent of battery power is the primary focus of the network. Under this paper, we investigate the differences in performance that exist between the routing protocols AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA when applied to UWSNs with variable deployment applications such as FTP, CBR, and VBR. As performance metrics, we measure things such as the average transmission delay, average jitter, average pathloss, percentage of utilization, and energy consumed in transmit, receive, and idle modes.

According to the findings of our simulations, the AODV routing protocol generates the least amount of total energy when compared to the DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA routing protocols. In addition, the Fisheye routing protocol achieves 92 percent better percentage of utilization than the AODV, DSR, DYMO, LAR1, Bellman Ford, OLSR, Fisheye, STAR-ORA, ZRP, and STAR-LORA routing protocols, and the DSR produces 0.3 percent better average path loss. Finally, STAR-LORA produces 86.4 percent less average jitter than the other routing protocols.

Abbreviations

AODV:	Ad-hoc On-demand Distance Vector
ZRP:	Zone Routing Protocol
DSR:	Dynamic source routing protocol
FSR:	Fisheye State Routing
DYMO:	Dynamic MANET on Demand Routing Protocol
LAR:	Location Aided Routing
OLSR:	Optimized Link State Routing Protocol
STAR:	Source Tree Adaptive Routing
STAR-ORA:	Source Tree Adaptive Routing Optimum routing approach
STAR-LORA:	Source Tree Adaptive Routing Least overhead routing approach.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request ([email protected]).

Conflicts of Interest

The authors declare that they have no conflicts of interest to report regarding the present study.

Authors’ Contributions

K. Sathish conceptualized the study, performed data curation and formal analysis, proposed methodology, provided software, and wrote the original draft. Dr. Asadi Srinivasulu supervised the study, reviewed and edited the manuscript, was responsible for project administration, and visualized the study. Dr. Ravikumar CV visualized the study, investigated the study, performed formal analysis, and provided software. Mr. Anand Kumar Gupta performed data curation, investigated the study, was responsible for resources, provided software, wrote the original draft, and proposed methodology.

References

S. Fang, “An integrated system for regional environmental monitoring and management based on internet of things,” IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1596–1605, 2014.
View at: Google Scholar
J. G. Proakis, E. M. Sozer, J. A. Rice, and M. Stojanovic, “Shallow water acoustic networks,” IEEE Communications Magazine, vol. 39, no. 11, pp. 114–119, 2001.
View at: Google Scholar
J. Heidemann, W. Ye, J. Wills, A. Syed, and Y. Li, “Research challenges and applications for underwater sensor networking,” Wireless Communications and Networking Conference, vol. 1, pp. 228–235, 2006.
View at: Google Scholar
S. Rani, S. H. Ahmed, J. Malhotra, and R. Talwar, “Energy efficient chain based routing protocol for underwater wireless sensor networks,” Journal of Network and Computer Applications, vol. 92, pp. 42–50, 2017.
View at: Publisher Site | Google Scholar
N. Subramani, P. Mohan, Y. Alotaibi, S. Alghamdi, and O. I. Khalaf, “An efficient metaheuristic-based clustering with routing protocol for underwater wireless sensor networks,” Sensors, vol. 22, no. 2, p. 415, 2022.
View at: Google Scholar
P. Mohan, N. Subramani, Y. Alotaibi, S. Alghamdi, O. I. Khalaf, and S. Ulaganathan, “Improved metaheuristics-based clustering with multihop routing protocol for underwater wireless sensor networks,” Sensors, vol. 22, no. 4, Article ID 1618, 2022.
View at: Google Scholar
M. Ayaz, I. Baig, A. Abdullah, and I. Faye, “A survey on routing techniques in underwater wireless sensor networks,” Journal of Network and Computer Applications, vol. 34, no. 6, pp. 1908–1927, 2011.
View at: Publisher Site | Google Scholar
N. Meratnia, “CLAM—collaborative embedded networks for submarine surveillance: an overview,” in Proceedings of the OCEANS, 2011 IEEE-Spain, pp. 1–4, IEEE, Santander, Spain, June 2011.
View at: Google Scholar
E. M. Sozer, M. Stojanovic, and J. G. Proakis, “Underwater acoustic networks,” IEEE Journal of Oceanic Engineering, vol. 25, no. 1, pp. 72–83, 2000.
View at: Publisher Site | Google Scholar
J. H. Cui, J. Kong, M. Gerla, and S. Zhou, “The challenges of building mobile underwater wireless networks for aquatic applications,” IEEE Network, vol. 20, no. 3, pp. 12–18, 2006.
View at: Google Scholar
K. Wang, H. Gao, X. Xu, J. Jiang, and D. Yue, “An energy-efficient reliable data transmission scheme for complex environmental monitoring in underwater acoustic sensor networks,” IEEE Sensors Journal, vol. 16, no. 11, pp. 4051–4062, 2016.
View at: Publisher Site | Google Scholar
G. Han, J. Jiang, L. Shu, and M. Guizani, “An attack-resistant trust model based on multidimensional trust metrics in underwater acoustic sensor network,” IEEE Transactions on Mobile Computing, vol. 14, no. 12, pp. 2447–2459, 2015.
View at: Publisher Site | Google Scholar
R. Agarwal, S. Kumar, and R. M. Hegde, “Algorithms for crowd surveillance using passive acoustic sensors over a multimodal sensor network,” IEEE Sensors Journal, vol. 15, no. 3, pp. 1920–1930, 2015.
View at: Publisher Site | Google Scholar
G. Han, J. Jiang, L. Shu, Y. Xu, and F. Wang, “Localization algorithms of underwater wireless sensor networks: a survey,” Sensors, vol. 12, no. 2, pp. 2026–2061, 2012.
View at: Publisher Site | Google Scholar
S. Lee and D. Kim, “Underwater hybrid routing protocol for UWSNs,” in Proceedings of the Fifth International Conference on Ubiquitous and Future Networks (ICUFN), IEEE, Da Nang, Vietnam, 2013.
View at: Google Scholar
F. Yuan, Y. Zhan, and Y. Wang, “Data density correlation degree clustering method for data aggregation in WSN,” IEEE Sensors Journal, vol. 14, no. 4, pp. 1089–1098, 2014.
View at: Publisher Site | Google Scholar
C. V. Ravikumar, “Design of MC-CDMA receiver using RBF network to mitigate MAI and non linear distortion,” Neural Computing & Applications, vol. 31, no. 2, 2019.
View at: Google Scholar
S. Rani, R. Talwar, J. Malhotra, S. H. Ahmed, M. Sarkar, and H. Song, “A novel scheme for an energy efficient Internet of Things based on wireless sensor networks,” Sensors, vol. 15, no. 11, pp. 28603–28626, 2015.
View at: Publisher Site | Google Scholar
C. Perkins, E. Belding-Royer, and S. Das, Ad hoc on-Demand Distance Vector (AODV) Routing, 2003, https://en.wikipedia.org/wiki/Ad_hoc_On-Demand_Distance_Vector_Routing.
S. H. Manjula, C. N. Abhilash, K. Shaila, K. R. Venugopal, and L. M. Patnaik, “Performance of AODV routing protocol using group and entity mobility models in wireless sensor networks,” International Multi Conference of Engineers and Computer Scientist, vol. 2, pp. 1212–1217, 2008.
View at: Google Scholar
G. S. Teja and P. Samundiswary, “Performance analysis of DYMO protocol for IEEE 802.15. 4 based WSNs with mobile nodes,” in Proceedings of the Computer Communication and Informatics (ICCCI), IEEE, Coimbatore, India, 2014.
View at: Google Scholar
M. K. Park and V. Rodoplu, “UWAN-MAC: an energy-efficient MAC protocol for underwater acoustic wireless sensor networks,” IEEE Journal of Oceanic Engineering, vol. 32, no. 3, pp. 710–720, 2007.
View at: Publisher Site | Google Scholar
M. C. Domingo and R. Prior, “Energy analysis of routing protocols for underwater wireless sensor networks,” Computer Communications, vol. 31, no. 6, pp. 1227–1238, 2008.
View at: Publisher Site | Google Scholar
C. V. Ravikumar, “MC-CDMA receiver design using recurrent neural network for eliminating MAI and non linear distortion,” International Journal of Communication Systems, vol. 10, no. 16, 2017.
View at: Google Scholar
Z. Alkindi, N. Alzeidi, and B. A. A. Touzene, “Performance evolution of grid based routing protocol for underwater wireless sensor networks under different mobile models,” International Journal of Wireless & Mobile Networks, vol. 10, no. 1, pp. 13–25, 2018.
View at: Google Scholar
M. S. A. Patil and M. P. Mishra, “Improved mobicast routing protocol to minimize energy consumption for underwater wireless sensor networks,” International Journal of Research Science. Engineering., vol. 3, no. 2, pp. 197–204, 2017.
View at: Google Scholar
A. Khan, I. Ahmedy, M. Anisi et al., “A localization-free interference and energy holes minimization routing for underwater wireless sensor networks,” Sensors, vol. 18, no. 2, p. 165, 2018.
View at: Publisher Site | Google Scholar
L. E. Emokpae, S. DiBenedetto, B. Potteiger, and M. Younis, “UREAL: underwater reflection enabled acoustic-based localization,” IEEE Sensors Journal, vol. 14, no. 11, pp. 3915–3925, 2014.
View at: Publisher Site | Google Scholar
Q. Liang, B. Zhang, C. Zhao, and Y. Pi, “TDoA for passive localization: underwater versus terrestrial environment,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 10, pp. 2100–2108, 2013.
View at: Publisher Site | Google Scholar
Z. Yu, C. Xiao, and G. Zhou, “Multi-objectivization-based localization of underwater sensors using magnetometers,” IEEE Sensors Journal, vol. 14, no. 4, pp. 1099–1106, 2014.
View at: Publisher Site | Google Scholar
R. Diamant and L. Lampe, “Underwater localization with time-synchronization and propagation speed uncertainties,” IEEE Transactions on Mobile Computing, vol. 12, no. 7, pp. 1257–1269, 2013.
View at: Publisher Site | Google Scholar
P. V. Rajaram and M. Prakash, “Intelligent deep learning based bidirectional long short term memory model for automated reply of e-mail client prototype,” Pattern Recognition Letters, vol. 152, pp. 340–347, 2021.
View at: Publisher Site | Google Scholar
S. Mishra and M. Prakash, “Digital mammogram inferencing system using intuitionistic fuzzy theory,” Computer Systems Science and Engineering, vol. 41, no. 3, pp. 1099–1115, 2022.
View at: Publisher Site | Google Scholar
X. Ba, L. Jin, Z. Li, J. Du, and S. Li, “Multiservice-based traffic scheduling for 5G access traffic steering, switching and splitting,” Sensors, vol. 22, no. 9, Article ID 3285, 2022.
View at: Google Scholar
M. Alsulami, R. Elfouly, and R. Ammar, “A re- liable underwater computing system,” in Proceedings of the 2021 4th IEEE International Conference on Industrial Cyber- Physical Systems (ICPS), IEEE, Victoria, BC, Canada, 2021.
View at: Google Scholar
K. Bhattacharjya, S. Alam, and D. De, “Performance analysis of DYMO, ZRP and AODV routing protocols in a multi hop grid based underwater wireless sensor network,” in Proceedings of the 2nd International Conference on Computational Intelligence, Communications and Business Analytics (CICBA), Springer Nature, Singapore, (2018a).
View at: Google Scholar
K. Bhattacharjya, S. Alam, and D. De, “TTCBT: two tier complete binary tree based wireless sensor network for FSR and LANMAR routing protocols,” Microsystem Technologies, vol. 27, no. 2, pp. 443–453, 2018b.
View at: Google Scholar
P. K. Varshney, G. S. Agrawal, and S. K. Sharma, “Relative performance analysis of proactive routing protocols in wireless ad hoc networks using varying node density,” Invertis Journal of Science & Technology, vol. 9, no. 3, pp. 161–169, 2016.
View at: Publisher Site | Google Scholar
K. Bhattacharjya, S. Alam, and D. De, “CUWSN: energy efficient routing protocol selection for cluster based underwater wireless sensor network,” Microsystem Technologies, vol. 28, 2019.
View at: Google Scholar
S. Alam and D. De, “Cloud smoke sensing model for AODV, RIP and STAR routing protocols using wireless sensor network in industrial township area,” in Proceedings of the Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), IEEE, Kolkata, India, 2016.
View at: Google Scholar

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Copyright © 2022 K. Sathish et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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