Distributed Energy Optimization Protocol using Crow Search Algorithm in Underwater Acoustic Sensor Network for Energy Enhancement Comparing with Depth Based Routing Algorithm

By combining a distributed energy optimization protocol and the Crow Search Algorithm, An underwater acoustic sensor network's sensor nodes can be made to use less energy.
$(DEO_CSA)\mathbf{in}$
contrast to the DBR protocol for depth-based routing. The Underwater Acoustic Sensor Network (UWASN) uses a 3D geographic zone for cooperative sampling to gather data and uses the crow search algorithm to distribute the data among the nodes.20 samples from each group were collected with a pre-test power of 80%, an error of 0.05, a confidence level of 95%, and 0.05 was chosen as the cutoff point for training the data sets. By changing the node distance, the proposed algorithm routing metrics are examined in a virtual underwater environment using the Aquasim patch and NS2 simulator. When compared to DBR's energy (1mJ) with delay, the proposed DEOCSA performs best for dynamically changing environmental and geographical topological conditions (850ms) The statistical research demonstrates that the least significant value (P0.05) for energy optimization is energy
$(\mathbf{P}=0.05)$
. The simulation results show that by using the recommended Crow Search algorithm rather than Depth Based Routing Algorithm, the sensor network's energy efficiency is increased by shortening the time spent choosing the best nodes.