Energy-Efficient Forest Fire Prediction Model Based on Two-Stage Adaptive Duty-Cycled Hybrid X-MAC Protocol

Area of Science:

• Wireless Sensor Networks
• Environmental Monitoring
• Energy Efficiency in IoT

Background:

• Forest fires pose significant environmental and economic risks.
• Existing wireless sensor network protocols often struggle with energy efficiency for continuous monitoring.
• The X-MAC protocol offers a foundation for asynchronous sensor networks but can be optimized for dynamic environments.

Purpose of the Study:

• To develop and evaluate an energy-efficient protocol for forest fire prediction using wireless sensor networks.
• To improve the performance of the X-MAC protocol in terms of throughput and energy consumption.
• To enable adaptive duty-cycling based on real-time environmental risk assessment.

Main Methods:

• Proposal of the adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol.
• Integration of environmental status acquisition from forest fire monitoring sensors.
• Dynamic adjustment of the duty-cycle sleep interval based on environmental data.
• Experimental verification of the ADX-MAC protocol's performance.

Main Results:

• The ADX-MAC protocol demonstrated a 19% improvement in throughput compared to the X-MAC protocol.
• The ADX-MAC protocol achieved 24% greater energy efficiency than the X-MAC protocol.
• The protocol successfully shortened the duty-cycle as forest fire probability increased, enabling faster detection cycles.

Conclusions:

• The proposed ADX-MAC protocol significantly enhances energy efficiency and throughput for forest fire prediction systems.
• Adaptive duty-cycling based on environmental risk is a viable strategy for optimizing wireless sensor network performance.
• ADX-MAC offers a promising solution for real-time, energy-conscious environmental monitoring applications.