An energy efficient hierarchical routing approach for UWSNs using biology inspired intelligent optimization

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Underwater wireless sensor networks (UWSNs) face challenges with uneven energy consumption and suboptimal cluster head selection.
• Existing routing algorithms often struggle to balance energy efficiency and network longevity.

Purpose of the Study:

• To propose an improved Gray Wolf Optimization algorithm (CTRGWO-CRP) for UWSNs.
• To address energy consumption imbalance and optimize cluster head selection in UWSNs.

Main Methods:

• Implemented a cloning strategy, t-distribution perturbation mutation, and opposition-based learning within the Gray Wolf Optimization framework.
• Designed a dynamic weighted fitness function incorporating network energy and communication distance.
• Utilized an elite retention strategy for cluster head selection and a multi-hop relay mechanism for data transmission.

Main Results:

• The CTRGWO-CRP algorithm significantly extends network lifetime by at least 23.5% compared to LEACH, DMaOWOA, and GSHFA-HCP.
• Demonstrated improved energy balance and transmission efficiency through multi-objective optimization.
• Validated the effectiveness of the multi-strategy fusion in routing optimization.

Conclusions:

• The proposed CTRGWO-CRP algorithm offers a substantial improvement in UWSN performance.
• The fusion of multiple strategies effectively optimizes routing and extends network lifetime.