Quantum swarm-optimized DV-Hop algorithm for accurate localization of weak nodes in wireless sensor networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Accurate sensor node localization is critical in wireless sensor networks (WSNs).
• Traditional Distance Vector Hop (DV-Hop) methods struggle with dynamic network topologies and uneven node distribution.
• Existing DV-Hop algorithms exhibit slow convergence and can get stuck in local optima, hindering precise positioning.

Purpose of the Study:

• To enhance the accuracy and resilience of WSN localization in dynamic environments.
• To address the limitations of the traditional DV-Hop algorithm, specifically its convergence speed and tendency towards local optima.
• To introduce novel optimization techniques for improved node positioning in WSNs.

Main Methods:

• Proposed two improved DV-Hop methodologies: Quantum Golden Jackal Optimization (QGJO) and Quantum Bullhead Shark Optimization (QBSO).
• Integrated quantum inspiration functions into GJO and BSO to prevent premature convergence and maintain algorithmic authenticity.
• Incorporated a swarm intelligence communication function and a position update function to refine node estimations.

Main Results:

• QGJO-DV-HOP demonstrated a mean positioning error of 16.79% with a standard deviation of 2.59%.
• QBSO-DV-HOP resulted in a mean positioning error of 26.23% with a standard deviation of 4.38%.
• Simulations varied network coverage, node count, beacon proportions, and topology shifts, validating algorithm performance.

Conclusions:

• QGJO-DV-HOP significantly reduces positioning errors in WSNs compared to QBSO-DV-HOP and traditional methods.
• The proposed quantum-inspired optimization techniques enhance localization accuracy and robustness in challenging WSN environments.
• The integration of swarm intelligence and refined position updates contributes to more reliable WSN node localization.