Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe

Abstract

Underwater Wireless Sensor Networks (UWSNs) have recently established themselves as an extremely interesting area of research thanks to the mysterious qualities of the ocean. The UWSN consists of sensor nodes and vehicles working to collect data and complete tasks. The battery capacity of sensor nodes is quite limited, which means that the UWSN network needs to be as efficient as it can possibly be. It is difficult to connect with or update a communication that is taking place underwater due to the high latency in propagation, the dynamic nature of the network, and the likelihood of introducing errors. This makes it difficult to communicate with or update a communication. Cluster-based underwater wireless sensor networks (CB-UWSNs) are proposed in this article. These networks would be deployed via Superframe and Telnet applications. In addition, routing protocols, such as Ad hoc On-demand Distance Vector (AODV), Fisheye State Routing (FSR), Location-Aided Routing 1 (LAR1), Optimized Link State Routing Protocol (OLSR), and Source Tree Adaptive Routing—Least Overhead Routing Approach (STAR-LORA), were evaluated based on the criteria of their energy consumption in a range of various modes of operation with QualNet Simulator using Telnet and Superframe applications. STAR-LORA surpasses the AODV, LAR1, OLSR, and FSR routing protocols in the evaluation report’s simulations, with a Receive Energy of 0.1 mWh in a Telnet deployment and 0.021 mWh in a Superframe deployment. The Telnet and Superframe deployments consume 0.05 mWh transmit power, but the Superframe deployment only needs 0.009 mWh. As a result, the simulation results show that the STAR-LORA routing protocol outperforms the alternatives.

Acoustic Wave Reflection in Water Affects Underwater Wireless Sensor Networks

“…This study looks at the oblique incidence of homogenous waves from a fluid medium on different surfaces [43]. Water interactions with sediment, stainless steel interactions, ceramic interactions, air interactions with sediment, and air interactions with solids were all modeled using COMSOL Multiphysics 5.6.…”

Enhancement of Precise Underwater Object Localization

“…In the field of UWSNs, there has recently been a surge in the amount of interest in using distributed antenna systems to connect to wireless communication networks. In a WSN, individual antennas are dispersed and connected by UWANs, an external connection that connects sensor nodes via radio (Carroll et al., 2014; Sathish, Hamdi, Chinthaginjala, Pau, et al., 2023; Sathish, Hamdi, Chinthaginjala Venkata, Alibakhshikenari, et al., 2023; Sathish, Ravikumar, et al., 2023). Two or more of the internal sensor components of a sub‐merged or acoustically isolated by cluster and cluster head sensors followed by sink and base station, as shown in Figure 2.…”

“…This goal can be accomplished through the use of specialized software. Because it is impossible to save all of the data under these circumstances, the data must be processed as quickly as possible (Mahmood et al., 2013; Sathish, Hamdi, Chinthaginjala Venkata, Alibakhshikenari, et al., 2023; Sathish, Ravikumar, et al., 2023). Processing high‐velocity data at a higher rate is therefore required for data mining.…”

“…The physical parameters under which underwater acoustic channels operate are often considered to impose severe bandwidth constraints. Similarly, optical signals are attenuated and dispersed significantly in aquatic environments (J. Luo et al., 2018; Sathish, Hamdi, Chinthaginjala, Pau, et al., 2023; Sathish, Hamdi, Chinthaginjala Venkata, Alibakhshikenari, et al., 2023; Sathish, Ravikumar, et al., 2023; J. Yang et al., 2015). As a result, neither of these techniques is appropriate for application in submerged environments.…”

“…Similarly, optical signals are attenuated and dispersed significantly in aquatic environments (J. Luo et al, 2018;Sathish, Hamdi, Chinthaginjala Venkata, Alibakhshikenari, et al, 2023;Sathish, Ravikumar, et al, 2023;J. Yang et al, 2015).…”