An Optimization Coverage Strategy for Wireless Sensor Network Nodes Based on Path Loss and False Alarm Probability.

Area of Science:

• Wireless Sensor Networks (WSN)
• Signal Processing
• Optimization Algorithms

Background:

• Traditional WSN perception models inaccurately represent wireless signal transmission, leading to coverage gaps in complex environments.
• Existing models often overlook sensor failures, impacting signal detection and network longevity.

Purpose of the Study:

• To develop an advanced WSN perception model incorporating path loss and false alarm probability for improved coverage optimization.
• To enhance WSN network performance and lifespan through a novel node optimization strategy.

Main Methods:

• Derived a logarithmic-based path loss model for wireless signals.
• Utilized the Neyman-Pearson criterion to formulate a maximum detection probability model under unknown cost functions and prior probabilities, constraining the false alarm rate.
• Developed and solved an optimization model for WSN coverage using an intelligent optimization algorithm.

Main Results:

• The proposed model more accurately captures signal transmission and detection characteristics in WSNs.
• Achieved full coverage with 50 nodes, outperforming the exponential decay model (54 nodes) and the 0/1 model (less than 70% coverage with 60 nodes).

Conclusions:

• The developed WSN node optimization coverage strategy effectively improves network performance.
• The proposed perception model offers a viable solution for extending WSN network lifespan.