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

Abstract

Abstract Forest fires cause severe ecological and economic damage, and their early detection is critical for effective disaster management. Conventional wireless sensor networks often fail to deliver timely alerts during emergencies due to congestion and unreliable channel conditions in forest environments. This paper proposes a Cognitive Radio Sensor Network (CRSN) utilizing IEEE 802.11af technology for Forest Fire Early Warning and Emergency Notification System to address these challenges. The system integrates temperature, smoke, and gas sensors with Cognitive radio sensor nodes to detect forest fire events and prioritize emergency alert transmissions. By dynamically sensing and utilizing idle licensed channels, the system bypasses congestion, and an Adaptive Priority Management for prioritizing classes for emergency notification, ensuring low-latency and reliable delivery of fire alerts. Simulation results demonstrate that the proposed system achieves lower bit error rates and reduced latency under varying environmental conditions, enhancing the reliability and effectiveness of forest fire emergency notifications.