A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Wireless Sensor Networks (WSNs) face challenges with limited energy and complex topologies.
• Mobile agent nodes can mitigate these issues, but inefficient data gathering causes latency.
• Existing mobile agent path planning methods struggle with energy consumption and network latency.

Purpose of the Study:

• To develop an efficient mobile agent path scheduling method for Wireless Sensor Networks.
• To reduce energy consumption and data gathering latency in large-scale WSNs.
• To improve the overall performance of data collection in WSNs.

Main Methods:

• A hybrid optimization method, HM-ACOPSO, combining Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).
• WSN field is divided into clusters, with mobile agent traversing cluster heads (CHs) in an ACO-ordered sequence.
• PSO is used to select anchor nodes within communication range based on the traversal sequence, with dynamic communication range adjustment and merging of duplicated covering areas.

Main Results:

• The HM-ACOPSO method demonstrates superior performance compared to existing methods.
• Significant reductions in energy consumption were observed.
• Improved data gathering efficiency and reduced network latency were achieved.

Conclusions:

• The HM-ACOPSO approach offers an effective solution for mobile agent path scheduling in WSNs.
• This method enhances energy efficiency and data collection performance.
• The hybrid optimization strategy provides a promising direction for future WSN research.