Real-time forest fire detection with wireless sensor networks

By Liyang Yu, Neng-Ming Wang, Xiaoqiao Meng. Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005., 2005

521 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e0c2ece2a8dd71a6e6ef97262c42fbe27228fc04

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Citations in the corpus

A Review on Forest Fire Detection Techniques (2014, methodology): “[29] present a real-time forest fire detection system by using neural wireless sensor networks.”
A framework for use of wireless sensor networks in forest fire detection and monitoring (2012, methodology): “Yu et al. (2005) present a method which applies neural network techniques for in-network data processing in environmental sensing applications of wireless sensor networks.”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, background): “include event detection and query processing [131]–[133], and QoS, data integrity, and fault detection [134]–[136].”
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications (2014, methodology): “NEURAL NETWORK [79] [23], [99], [100] [26] [29], [30] [35], [36]”
Data-Dependent Convergence for Consensus Stochastic Optimization (2016, background): “In some sensor network applications such as global calibration, this assumption may also hold, but for spatially varying phenomena the task may be different and our model may no longer be as relevant [17].”
A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends (2009, background): “WSN measure temperature and humidity, and detect smoke [68,69].”