Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization

Area of Science:

• Computer Science
• Electrical Engineering
• Network Optimization

Background:

• Efficient network coverage and connectivity are crucial for wireless sensor networks (WSNs) in data-driven applications.
• Optimal sensor node placement is a key challenge impacting WSN performance and deployment costs.
• Existing methods face limitations in achieving high coverage, connectivity, and cost-efficiency.

Purpose of the Study:

• To develop an Improved Chaotic Grey Wolf Optimization (ICGWO) algorithm for enhancing WSN coverage and connectivity.
• To address challenges of high deployment costs, limited coverage, and insufficient connectivity in WSNs.
• To provide an optimized solution for sensor node placement in diverse WSN scenarios.

Main Methods:

• Development of a mathematical model for WSN coverage and connectivity optimization.
• Enhancement of the Grey Wolf Optimizer (GWO) using a chaotic map to create the ICGWO algorithm.
• Evaluation of ICGWO performance using CEC_22 benchmark functions and comparison with other optimization methods.

Main Results:

• The ICGWO algorithm demonstrated superior performance in finding optimal solutions and achieving faster convergence.
• Practical WSN deployment cases showed significant improvements in coverage rates, reaching up to 99.4940% with 50 nodes.
• Average coverage improvements ranged from 2.18% to 16.41% compared to state-of-the-art methods across different node counts.

Conclusions:

• The ICGWO algorithm is an effective and reliable solution for optimizing sensor node placement in WSNs.
• ICGWO significantly enhances network coverage and connectivity, addressing critical deployment challenges.
• The proposed method contributes to the advancement of WSN technology by maximizing network performance and efficiency.