Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Security

Background:

• Wireless sensor networks (WSNs) are crucial for security applications like intruder detection.
• Deploying sensors randomly in a linear domain presents challenges in coverage and resource management.
• Limited-power sensors with adjustable ranges require optimized deployment strategies.

Purpose of the Study:

• To address the tradeoff barrier coverage (TBC) problem for linear WSNs.
• To minimize total power consumption, the number of active sensors, and the maximum sensing range simultaneously.
• To develop an efficient multiobjective optimization framework for improved barrier coverage.

Main Methods:

• A problem-specific multiobjective evolutionary algorithm (MOEA/D), termed PS-MOEA/D, was developed.
• A 2-tuple encoding scheme and a cover-shrink algorithm were introduced for solution generation.
• Problem-specific knowledge was incorporated into local search for enhanced collaboration between subproblems.
• Computational resource allocation strategies were integrated based on problem characteristics.

Main Results:

• The PS-MOEA/D algorithm demonstrated effectiveness in achieving a favorable tradeoff among the three objectives.
• Experimental results showed that PS-MOEA/D consistently outperformed four competing approaches across various metrics.
• The approach successfully generated feasible and relatively optimal solutions for the TBC problem.

Conclusions:

• The PS-MOEA/D approach offers a promising solution for optimizing tradeoff barrier coverage in wireless sensor networks.
• The proposed method enhances efficiency and reliability in intruder detection systems.
• The algorithm's ability to balance multiple objectives makes it suitable for practical WSN deployments.