DV-Hop Algorithm Based on Multi-Objective Salp Swarm Algorithm Optimization.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Accurate sensor node localization is crucial for wireless sensor networks (WSNs).
• The conventional DV-Hop algorithm offers range-free localization but suffers from limited accuracy.
• Existing DV-Hop improvements often fail to achieve optimal localization precision.

Purpose of the Study:

• To enhance the localization accuracy of the DV-Hop algorithm in WSNs.
• To introduce a novel DV-Hop algorithm integrating multi-objective salp swarm optimization (MSSO).
• To validate the proposed algorithm's superior performance over existing methods.

Main Methods:

• Refining hop counts and correcting average hop distance using minimum mean-square error and weighting.
• Transforming the single-objective optimization of DV-Hop into a multi-objective optimization problem.
• Employing an improved MSSO algorithm for node coordinate estimation in the third stage of DV-Hop.

Main Results:

• The proposed algorithm demonstrated significant reductions in localization errors across various network topologies and communication radii.
• Localization errors decreased by up to 56.79% compared to the standard DV-Hop algorithm.
• Performance improvements were consistently observed across different node counts, anchor node numbers, and regional distributions.

Conclusions:

• The multi-objective salp swarm optimization-based DV-Hop algorithm offers superior localization accuracy in WSNs.
• The proposed method outperforms standard DV-Hop and other enhanced variants like GWO-DV-Hop, SSA-DV-Hop, and ISSA-DV-Hop.
• This approach provides a robust solution for precise sensor node positioning in complex network environments.