IEEE 802.11af-enabled scalable cognitive radio sensor networks with adaptive priority management for early forest...

Area of Science:

• Environmental Science
• Computer Engineering
• Telecommunications

Background:

• Forest fires pose significant ecological and economic threats, necessitating rapid detection.
• Conventional wireless sensor networks struggle with timely alerts in forest environments due to congestion and unreliable channels.

Purpose of the Study:

• To propose a Cognitive Radio Sensor Network (CRSN) for an effective Forest Fire Early Warning and Emergency Notification System.
• To address the limitations of traditional networks in delivering urgent alerts during forest fire events.

Main Methods:

• Integration of temperature, smoke, and gas sensors with cognitive radio sensor nodes.
• Utilization of IEEE 802.11af technology for dynamic sensing and use of idle licensed channels.
• Implementation of Adaptive Priority Management for prioritizing emergency alert classes.

Main Results:

• The proposed CRSN system demonstrates lower bit error rates compared to conventional methods.
• Reduced latency in emergency alert transmissions was observed under various environmental conditions.
• Enhanced reliability and effectiveness in forest fire emergency notifications were achieved.

Conclusions:

• The CRSN system effectively bypasses network congestion by utilizing dynamic spectrum access.
• The proposed system ensures low-latency and reliable delivery of critical fire alerts.
• This technology significantly improves forest fire early warning and emergency notification capabilities.