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

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.