Clustering-based dragonfly optimization algorithm for underwater wireless sensor networks

Abstract

Communication and monitoring of the undersea environment have numerous applications, including marine environment studies, offshore research, and mineral extraction. However, factors like as the presence of an underwater current, a limited bandwidth, high water pressure, propagation lag, and error probability make these applications more difficult to implement. Underwater communication might be challenging due to the surroundings. As a clustering approach for sensor nodes in underwater wireless sensor networks (UWSNs), we present a clustering-based dragonfly optimisation (CDFO) algorithm with decentralised forwarding over wireless networks. The CDFO-UWSN approach is used to determine how many clusters should be used for routing. Analogous algorithms have been found to be connected to the proposed method, including the Ant Colony Optimizer (ACO), Adaptive Node Clustering for UWSN (ANC-UWSN), Grey Wolf Optimizer (GWO), and Moth Flame Optimizer (MFO). During the simulation, a performance matrix will be used to account for a variety of parameters such as grid size, transmission range, and node density. The results show that CDFO-UWSN outperforms all other algorithms tested. By establishing more clusters than competing systems, it improves overall routing and boosts network lifetime. This enables a network to run for an extended amount of time. The proposed CDFO-UWSN algorithm outperforms the GWO, ACO, MFO, and DFO algorithms in terms of number of nodes, with a percentage improvement of 78.13 % for 1500*1500 m grid size, and a percentage improvement of 64.71 % in terms of transmission range for 1500*1500 m grid size.