An Effective Forest Fire Detection Framework Using Heterogeneous Wireless Multimedia Sensor Networks

Abstract

With improvements in the area of Internet of Things (IoT), surveillance systems have recently become more accessible. At the same time, optimizing the energy requirements of smart sensors, especially for data transmission, has always been very important and the energy efficiency of IoT systems has been the subject of numerous studies. For environmental monitoring scenarios, it is possible to extract more accurate information using smart multimedia sensors. However, multimedia data transmission is an expensive operation. In this study, a novel hierarchical approach is presented for the detection of forest fires. The proposed framework introduces a new approach in which multimedia and scalar sensors are used hierarchically to minimize the transmission of visual data. A lightweight deep learning model is also developed for devices at the edge of the network to improve detection accuracy and reduce the traffic between the edge devices and the sink. The framework is evaluated using a real testbed, network simulations, and 10-fold cross-validation in terms of energy efficiency and detection accuracy. Based on the results of our experiments, the validation accuracy of the proposed system is 98.28%, and the energy saving is 29.94%. The proposed deep learning model’s validation accuracy is very close to the accuracy of the best performing architectures when the existing studies and lightweight architectures are considered. In terms of suitability for edge computing, the proposed approach is superior to the existing ones with reduced computational requirements and model size.

Network of Drones for Early Warning of Forest Fire and Dynamic Fire Quenching Plan Generation

“…It is observed that power aware computing algorithms were used but energy consumption had increased [24]. A framework that utilized smart multimedia sensors to extract more accurate information while minimizing the transmission of visual data was discussed and the framework employed a hierarchical structure that combined multimedia sensors with scalar sensors [25].…”

Forest Fire Monitoring Method Based on UAV Visual and Infrared Image Fusion

“…With the constant maturity in its technology, it has been applied in many fields, such as weather monitoring, disaster monitoring, power line inspection, and disaster rescue. In particular, it is easy to operate, highly mobile, low cost, easy to maintain, lightweight, small volume, and has real-time inspection capabilities, etc., in forest fire prevention and forest patrol, and other operations can also show its advantages [9][10][11][12]. Using the images taken by UAVs as data, combined with computer technology such as image processing to identify forest fires, has been one of the most commonly used tools in forest fire monitoring.…”

Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

“…In a sensor network, each sensor node can collect measurements, process the data, and exchange information with its neighboring nodes under a given communication topology, making the sensor network suitable to deal with systems or processes in large scales or spatially distributed [1], [2]. Some successful practical applications can be found in forest fire detection [3], air pollution surveillance [4], [5], and industrial valve monitoring [6], etc.…”