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Machine Learning Based Energy Efficient High Performance Routing Protocol for Underwater Communication
N. Krishnapriya and N. Kumareshan

Acoustic data communication is a powerful technology for underwater communication implementation. Underwater wireless information transmission holds immense significance for the defense, industrial and the scientific domain. In the last couple of years, a rising focus has been aimed towards acoustic wireless communication with respect to terrestrial, space and optical links since it has the potential of rendering increased data rates with reduced power and huge needs. Energy efficient data transmission has an important part to play in this practical environment. Different research approaches have been presented before to guarantee the energy efficiency data transmission in the underwater sensor network. In the earlier works, Q learning technique is found to have resulted in the time complexity and computation overhead being increased. The solution to this problem is found in the proposed research work that presents the technique known as Machine Learning based Energy Efficient High Performance Routing Protocol (MLEE-HPR). In this research work, the first step involves the transmission of acoustic data packets to underwater acoustic sensor nodes for the node information collection. This collected information will go through processing and multi-objective decision rules are derived by applying the fuzzy pareto optimality approach which will be used as input to the Modified Fuzzy SVM method for weight assignment. On the basis of the weight allocated and the network parameters obtained, the choice of the optimal route path is achieved by applying Modified Squirrel Search algorithm. The experiments on the technical research are implemented which confirms that the suggested approach works in the MATLAB simulation platform yields superior performance compared to the available research approaches.

Keywords: Acoustic data communication, Underwater Sensor Networks, Fuzzy Pareto optimality approach, Modified Fuzzy SVM method, Modified Squirrel Search algorithm

Full Text (IP)
DOI: 10.32908/ahswn.v54.9589