The Role of UAV-IoT Networks in Future Wildfire Detection

Bushnaq, Osama M.; Chaaban, Anas; Al-Naffouri, Tareq Y.

doi:10.1109/jiot.2021.3077593

Cited by 108 publications

(52 citation statements)

References 35 publications

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“…Recently, UAVs are being made more economical and smaller in size, and the availability of autopilot software raises their popularity in the public and private sectors [2]. UAVs are used for a variety of purposes, including agriculture [3], remote sensing [4], forest fire detection [5], patrolling [6], and providing communication facilities in remote and disaster-prone areas [7,8]. However, in order to carry out tough tasks, UAVs must be able to communicate effectively.…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement of Optimized Link State Routing Protocol by Parameter Configuration for UANET

Tuli

Golam

Kim

et al. 2022

Drones

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The growing need for wireless communication has resulted in the widespread usage of unmanned aerial vehicles (UAVs) in a variety of applications. Designing a routing protocol for UAVs is paramount as well as challenging due to its dynamic attributes. The difficulty stems from features other than mobile ad hoc networks (MANET), such as aerial mobility in 3D space and frequently changing topology. This paper analyzes the performance of four topology-based routing protocols, dynamic source routing (DSR), ad hoc on-demand distance vector (AODV), geographic routing protocol (GRP), and optimized link state routing (OLSR), by using practical simulation software OPNET 14.5. Performance evaluation carries out various metrics such as throughput, delay, and data drop rate. Moreover, the performance of the OLSR routing protocol is enhanced and named “E-OLSR” by tuning parameters and reducing holding time. The optimized E-OLSR settings provide better performance than the conventional request for comments (RFC 3626) in the experiment, making it suitable for use in UAV ad hoc network (UANET) environments. Simulation results indicate the proposed E-OLSR outperforms the existing OLSR and achieves supremacy over other protocols mentioned in this paper.

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Section: Introductionmentioning

confidence: 99%

Performance Enhancement of Optimized Link State Routing Protocol by Parameter Configuration for UANET

Tuli

Golam

Kim

et al. 2022

Drones

View full text Add to dashboard Cite

show abstract

“…However, the location of the fire can not be directly determined. In addition, researches are also using deep learning method to detect the fire using unmanned aerial vehicle (UAV) images or videos (Yuan et al, 2017;Jiao et al, 2019;Kinaneva et al, 2019;Bushnaq et al, 2021;Guede-Fernández et al, 2021). For instance, Muhammad et al (2018) suggests a convolutional neural network using surveillance videos.…”

Section: Introductionmentioning

confidence: 99%

Active Fire Detection Using a Novel Convolutional Neural Network Based on Himawari-8 Satellite Images

Hong

Tang

Pan

et al. 2022

Front. Environ. Sci.

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Fire is an important ecosystem process and has played a complex role in terrestrial ecosystems and the atmosphere environment. Sometimes, wildfires are highly destructive natural disasters. To reduce their destructive impact, wildfires must be detected as soon as possible. However, accurate and timely monitoring of wildfires is a challenging task due to the traditional threshold methods easily be suffered to the false alarms caused by small forest clearings, and the omission error of large fires obscured by thick smoke. Deep learning has the characteristics of strong learning ability, strong adaptability and good portability. At present, few studies have addressed the wildfires detection problem in remote sensing images using deep learning method in a nearly real time way. Therefore, in this research we proposed an active fire detection system using a novel convolutional neural network (FireCNN). FireCNN uses multi-scale convolution and residual acceptance design, which can effectively extract the accurate characteristics of fire spots. The proposed method was tested on dataset which contained 1,823 fire spots and 3,646 non-fire spots. The experimental results demonstrate that the FireCNN is fully capable of wildfire detection, with the accuracy of 35.2% higher than the traditional threshold method. We also examined the influence of different structural designs on the performance of neural network models. The comparison results indicates the proposed method produced the best results.

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“…[5]. There are many UAV applications such as wildfire monitoring [6], [7], crowd monitoring [8] target tracking [9], goods delivery [10], medical assistance, search and rescue (SaR) [11], emergency cellular deployment [12], and intelligent transportation. However, UAVs can not perform optimally in a complex dynamic environment owing to the dependency on human control and limitation of radio frequency (RF) communication [1].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

Rezwan

Choi

2022

IEEE Access

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Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learningbased approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.

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The Role of UAV-IoT Networks in Future Wildfire Detection

Cited by 108 publications

References 35 publications

Performance Enhancement of Optimized Link State Routing Protocol by Parameter Configuration for UANET

Performance Enhancement of Optimized Link State Routing Protocol by Parameter Configuration for UANET

Active Fire Detection Using a Novel Convolutional Neural Network Based on Himawari-8 Satellite Images

Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges

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