Adjusted Rand Index-Guided DPSO for Clustering and Data Routing in Wireless Sensor Networks.

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Wireless Sensor Networks (WSNs) require energy-efficient data routing and clustering to prolong operational longevity.
• Existing methods often face challenges in balancing energy consumption and network lifetime.

Purpose of the Study:

• To develop an intelligent and energy-aware framework for data routing and clustering in WSNs.
• To enhance the stable operational lifetime of WSNs by optimizing cluster configuration and data paths.

Main Methods:

• An adjusted Rand index (ARI)-guided discrete particle swarm optimization (ARI-DPSO) algorithm was employed.
• Dijkstra's algorithm was utilized for establishing energy-efficient data paths.
• Network lifetime served as the fitness function for ARI-DPSO to find optimal cluster configurations.

Main Results:

• The ARI-DPSO algorithm demonstrated improved swarm dynamics and diversity.
• The framework effectively maximized the stable operational lifetime of wireless sensor networks, indicated by the first node death.
• The ARI metric was used to prevent premature convergence by quantifying solution similarity and triggering dynamic perturbations.

Conclusions:

• The proposed ARI-DPSO framework offers a novel and effective approach to energy-balanced data routing and clustering in WSNs.
• This method significantly extends the network's operational lifetime compared to conventional approaches.
• The intelligent design enhances exploration capabilities, leading to globally optimal solutions for network longevity.