Reliable Data Transmission in Underwater Wireless Sensor Networks Using a Cluster-Based Routing Protocol Endorsed by Member Nodes

Abstract

Considering Underwater Wireless Sensor Networks (UWSNs) have limited power resources (low bandwidth, long propagation delays, and non-rechargeable batteries), it is critical that they develop solutions to reduce power usage. Clustering is one solution because it not only saves energy consumption but also improves scalability and data integrity. The design of UWSNs is vital to the development of clustering algorithms. The limited energy of sensor nodes, narrow transmission bandwidth, and unpredictable topology of mobile Underwater Acoustic Wireless Sensor Networks (UAWSNs) make it challenging to build an effective and dependable underwater communication network. Despite its success in data dependability, the acoustic underwater communication channel consumes the greatest energy at a node. Recharging and replacing a submerged node’s battery could be prohibitively expensive. We propose a network architecture called Member Nodes Supported Cluster-Based Routing Protocol (MNS-CBRP) to achieve consistent information transfer speeds by using the network’s member nodes. As a result, we use clusters, which are produced by dividing the network’s space into many minute circular sections. Following that, a Cluster Head (CH) node is chosen for each circle. Despite the fact that the source nodes are randomly spread, all of the cluster heads are linked to the circle’s focal point. It is the responsibility of the MNS-CBRP source nodes to communicate the discovered information to the CH. The discovered data will then be sent to the CH that follows it, and so on, until all data packets have been transferred to the surface sinks. We tested our techniques thoroughly using QualNet Simulator to determine their viability.

Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe

“…The destination sequence number is used on demand by both the AODV and the DSDV, but the AODV more frequently. If the transmitting node is unable to determine whether or not there is a path to the destination, AODV will begin looking for one [ 16 ].…”

Enhancing underwater target localization through proximity-driven recurrent neural networks

“…First and foremost, the dynamic character of underwater acoustic waves emitted by targets and sensed by hydrophone arrays can be represented using Recurrent Neural Networks, which have been found to be effective in simulating temporal relationships within sequential data. Second, by incorporating proximity-based techniques, localization accuracy can be considerably enhanced by taking into account the relative distances between hydrophone nodes and targets [ 17 , 18 ].…”

Enhancement of Precise Underwater Object Localization

“…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.…”

“…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.…”