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Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment.

Yu Gao1, Jin Wang2,3,4, Wenbing Wu5

  • 1College of Information Engineering, Yangzhou University, Yangzhou 225000, China. gaoyuyz@163.com.

Sensors (Basel, Switzerland)
|April 20, 2019
PubMed
Summary

This study introduces a travel route planning schema for mobile data collection in wireless sensor networks (WSNs). The approach optimizes routes for mobile collectors to maximize sensor coverage and minimize energy consumption.

Keywords:
ant colony optimizationmobile devicesparticle swarm optimizationtravel route planningwireless sensor networks

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Wireless Sensor Networks (WSNs) are crucial for environmental monitoring, especially in challenging terrains.
  • Intelligent vehicles offer potential for mobile data collection in WSNs.
  • Efficient route planning for mobile data collectors in WSNs remains a significant challenge.

Purpose of the Study:

  • To develop a travel route planning schema with a mobile collector (TRP-MC) for efficient data gathering in WSNs.
  • To maximize sensor coverage while minimizing energy consumption through optimized routes.
  • To enhance WSN performance using intelligent vehicle-based data collection.

Main Methods:

  • Defining sojourn points (SPs) for mobile data collection.
  • Determining the optimal number of SPs based on maximal coverage rate.
  • Utilizing Particle Swarm Optimization (PSO) for SP positioning.
  • Employing Ant Colony Optimization (ACO) for shortest loop scheduling.

Main Results:

  • The TRP-MC schema effectively plans short routes covering maximum sensors.
  • Optimized SP placement and scheduling significantly improve data gathering efficiency.
  • Simulations demonstrate superior performance compared to LEACH, MWR, and SHDGP algorithms.

Conclusions:

  • The proposed TRP-MC schema offers an efficient solution for mobile data collection in WSNs.
  • Integrating intelligent vehicles with optimized routing enhances WSN capabilities.
  • The study highlights the effectiveness of PSO and ACO in solving complex WSN routing problems.