Rule-Based vs. Behavior-Based Self-Deployment for Mobile Wireless Sensor Networks

Area of Science:

• Robotics
• Computer Science
• Network Engineering

Background:

• Mobile wireless sensor networks (MWSN) utilize autonomous node movement for optimal deployment.
• Deployment goals include maximizing network coverage and ensuring homogeneous communication load distribution.
• Reactive algorithms guide node movement based on predefined rules or behaviors.

Purpose of the Study:

• To experimentally evaluate and compare rule-based versus behavior-based reactive deployment algorithms in MWSNs.
• To assess the performance of a backbone dispersion algorithm against a social potential fields algorithm.
• To validate simulation findings with real-world experiments on a small robot network.

Main Methods:

• Comparative analysis of rule-based and behavior-based deployment strategies.
• Simulation of large-scale MWSN deployments in environments with and without obstacles.
• Real-world validation using a physical robot network.

Main Results:

• Behavior-based deployment algorithms demonstrated superior network coverage.
• Behavior-based approaches achieved a more balanced communication load distribution.
• These benefits were particularly pronounced in simulations with a high density of nodes and in environments containing obstacles.

Conclusions:

• Behavior-based deployment strategies are more effective for MWSN node placement.
• These methods offer significant advantages in achieving coverage and load balance, especially in challenging network configurations.
• The findings support the adoption of behavior-based algorithms for robust MWSN deployment.