An Adaptive LoRaWAN MAC Protocol for Event Detection Applications.

Area of Science:

• Environmental Science
• Computer Science
• Network Engineering

Background:

• The Internet of Things (IoT) is increasingly vital for monitoring environmental disasters like forest fires, which are exacerbated by the climate crisis.
• Effective management of environmental events requires robust, large-scale monitoring solutions.
• Existing IoT networks face challenges in timely data transmission for critical event detection.

Purpose of the Study:

• To propose and evaluate an efficient IoT-based technique for monitoring environmental events, specifically forest fires, over large geographical areas.
• To reduce transmission delay in Long-Range Wide Area Network (LoRaWAN) systems during critical event occurrences.
• To enhance the performance of IoT networks in environmental monitoring applications.

Main Methods:

• Development of a learning-automata-based hybrid Medium Access Control (MAC) model for LoRaWAN.
• Simulation-based evaluation of the proposed hybrid MAC model against traditional LoRaWAN schemes.
• Focus on reducing packet delay for event packets generated by a subset of network devices.

Main Results:

• The proposed hybrid MAC model demonstrates significantly improved performance in terms of packet delay.
• The model effectively handles event packet transmission from localized network segments.
• Achieved performance gains are notable when compared to conventional LoRaWAN protocols.

Conclusions:

• The learning-automata-based hybrid MAC model offers a superior solution for IoT-based environmental monitoring, particularly for time-sensitive events like forest fires.
• This approach enhances the reliability and efficiency of LoRaWAN for large-scale environmental sensing.
• The research contributes to the development of more effective disaster management systems through advanced IoT networking.