Satellite constellation design for forest fire monitoring via a stochastic programing approach

2017 ◽  
Vol 64 (8) ◽  
pp. 642-661 ◽  
Author(s):  
Aaron B. Hoskins ◽  
Hugh R. Medal ◽  
Eghbal Rashidi
Keyword(s):  
Forest Fire ◽  
Fire Monitoring ◽  
Satellite Constellation ◽  
Constellation Design

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

Infra-red line camera data-driven edge detector in UAV forest fire monitoring

2021 ◽  
Vol 111 ◽  
pp. 106574
Author(s):  
Francesco De Vivo ◽  
Manuela Battipede ◽  
Eric Johnson
Keyword(s):  
Forest Fire ◽  
Data Driven ◽  
Edge Detector ◽  
Fire Monitoring ◽  
Infra Red ◽  
Red Line

A real-time deep learning forest fire monitoring algorithm based on an improved Pruned + KD model

2021 ◽  
Author(s):  
Shengying Wang ◽  
Jing Zhao ◽  
Na Ta ◽  
Xiaoye Zhao ◽  
Mingxia Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Forest Fire ◽  
Fire Monitoring ◽  
Monitoring Algorithm

Satellite constellation design with genetic algorithms based on system performance

2016 ◽  
Vol 27 (2) ◽  
pp. 379-385 ◽  
Author(s):  
Xueying Wang ◽  
Jun Li ◽  
Tiebing Wang ◽  
Wei An ◽  
Weidong Sheng
Keyword(s):  
Genetic Algorithms ◽  
System Performance ◽  
Satellite Constellation ◽  
Constellation Design

scholarly journals An Environmentally Aware Scheme of Wireless Sensor Networks for Forest Fire Monitoring and Detection

2018 ◽  
Vol 10 (10) ◽  
pp. 102 ◽  
Author(s):  
Yi-Han Xu ◽  
Qiu-Ya Sun ◽  
Yu-Tong Xiao
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Forest Fire ◽  
Forest Fires ◽  
Network Architecture ◽  
Resource Constraints ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Fire Monitoring

Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption.

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

2018 ◽  
Author(s):  
Jin-Gu Kang ◽  
Dong-Woo Lim ◽  
Jin-Woo Jung
Keyword(s):  
Prediction Model ◽  
Forest Fire ◽  
Duty Cycle ◽  
Forest Fires ◽  
Energy Efficient ◽  
Mac Protocol ◽  
Two Stage ◽  
Fire Monitoring ◽  
Environmental Status ◽  
Mountain Environment

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.

Satellite constellation design algorithm for remote sensing of diurnal cycles phenomena

2018 ◽  
Vol 62 (9) ◽  
pp. 2529-2550 ◽  
Author(s):  
Sung Wook Paek ◽  
Luzius G. Kronig ◽  
Anton B. Ivanov ◽  
Olivier L. de Weck
Keyword(s):  
Remote Sensing ◽  
Design Algorithm ◽  
Diurnal Cycles ◽  
Satellite Constellation ◽  
Constellation Design
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