A Novel Centralized Range-Free Static Node Localization Algorithm with Memetic Algorithm and Lévy Flight

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Node localization is a critical NP-hard problem in wireless sensor networks (WSNs).
• Swarm intelligent algorithms (SIAs) are increasingly applied to address WSN localization challenges.
• Existing methods often struggle with precision due to distance estimation errors.

Purpose of the Study:

• To develop a highly precise node localization algorithm for WSNs.
• To introduce a novel algorithm, LMQPDV-hop, combining improved DV-Hop and a quantum-behaved particle swarm optimization (QPSO).
• To enhance global search capabilities and convergence speed for accurate node positioning.

Main Methods:

• An improved DV-Hop mechanism was used for distance estimation, modifying average hop distance with a defined weight.
• A quantum-behaved particle swarm optimization (QPSO) algorithm, LMQPSO, was developed for coordinate determination.
• Memetic algorithm (MA) and Lévy flight were integrated into QPSO to improve global search; a fast local search rule was added for convergence.

Main Results:

• The LMQPDV-hop algorithm demonstrated significant improvements in position precision.
• Simulations across various WSN deployment scenarios validated the algorithm's effectiveness.
• The enhanced QPSO (LMQPSO) successfully improved global searching and convergence speed.

Conclusions:

• LMQPDV-hop effectively enhances node localization precision in wireless sensor networks.
• The integration of MA and Lévy flight into QPSO offers superior global search and faster convergence.
• The modified DV-Hop mechanism reduces distance errors, contributing to overall accuracy.