An Intelligent Bio-Inspired Autonomous Surveillance System Using Underwater Sensor Networks.

Area of Science:

• Underwater sensor networks
• Bio-inspired computing
• Network protocols

Background:

• Energy efficiency is critical for underwater sensor networks (USNs).
• Underwater environments present unique challenges to network lifespan and data communication.
• Novel protocols are required due to slow signal propagation, unlike terrestrial networks.

Purpose of the Study:

• To propose an intelligent bio-inspired autonomous surveillance system for USNs.
• To enhance energy efficiency and extend the network lifespan of USNs.
• To introduce an efficient data communication method using a tunicate swarm algorithm for cluster head election.

Main Methods:

• Utilized the tunicate swarm algorithm (TSA) for intelligent cluster head (CH) election.
• Considered energy, distance, and density as key parameters for CH selection.
• Implemented a rotating CH mechanism based on the fitness values of sensor nodes (SNs) within layers and clusters.

Main Results:

• The proposed protocol demonstrated improved network lifespan compared to EE-LHCR, EE-DBR, and DBR.
• The TSA-based CH election effectively achieved energy balance among SNs.
• The system achieved a longer network lifespan due to optimized energy distribution.

Conclusions:

• The proposed intelligent bio-inspired system significantly enhances energy efficiency in USNs.
• The TSA-based approach for CH election leads to a more balanced energy distribution and extended network operational time.
• This method offers a promising solution for improving the longevity and performance of underwater sensor networks.