Version 1
: Received: 7 August 2018 / Approved: 7 August 2018 / Online: 7 August 2018 (09:10:12 CEST)
Version 2
: Received: 2 September 2018 / Approved: 3 September 2018 / Online: 3 September 2018 (10:09:11 CEST)
Kang, J.-G.; Lim, D.-W.; Jung, J.-W. Energy-Efficient Forest Fire Prediction Model Based on Two-Stage Adaptive Duty-Cycled Hybrid X-MAC Protocol. Sensors2018, 18, 2960.
Kang, J.-G.; Lim, D.-W.; Jung, J.-W. Energy-Efficient Forest Fire Prediction Model Based on Two-Stage Adaptive Duty-Cycled Hybrid X-MAC Protocol. Sensors 2018, 18, 2960.
Kang, J.-G.; Lim, D.-W.; Jung, J.-W. Energy-Efficient Forest Fire Prediction Model Based on Two-Stage Adaptive Duty-Cycled Hybrid X-MAC Protocol. Sensors2018, 18, 2960.
Kang, J.-G.; Lim, D.-W.; Jung, J.-W. Energy-Efficient Forest Fire Prediction Model Based on Two-Stage Adaptive Duty-Cycled Hybrid X-MAC Protocol. Sensors 2018, 18, 2960.
Abstract
In this paper, we propose an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The X-MAC protocol acquires the additional environmental status collected by each forest fire monitoring sensor for a certain period. And, based on these values, the length of sleep interval of duty-cycle is changed to efficiently calculate the risk of occurrence of forest fire according to the mountain environment. The performance of the proposed ADX-MAC protocol was verified through experiments the proposed ADX-MAC protocol improves throughput by 19% and was more energy-efficient by 24% compared to X-MAC protocol. As the probability of forest fires increases, the length of the duty cycle is shortened, confirming that the forest fires are detected at a faster cycle.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
