Article 1
Alshbatat, A. I. N. (2018). Fire Extinguishing System for High-Rise Buildings and Rugged Mountainous Terrains Utilizing Quadrotor Unmanned Aerial Vehicle. International Journal of Image, Graphics and Signal Processing, 12(1), 23-29. doi:10.5815/ijigsp.2018.01.03
This article examines the feasibility of using drones to extinguish fires by incorporating six core components including: a drone, a release mechanism, a spring-operated gun, fire extinguishing balls, a collision avoidance system, and cameras. The author’s objective is to develop a compact, lightweight and economical drone which can help mitigate the impact of fires. The research experiments were performed across several real fire scenarios in various outdoor environments and conditions. The main limitation of the technology is that the drone can hold only a single extinguishing ball which greatly impacts the size of the fire it is able to extinguish. This article is useful as experimental results have shown that the drone is capable of extinguishing fire in its early stages. Therefore, if drones can autonomously extinguish small fires it is possible that the technology can be adapted to prevent bushfires through the use of drone swarms.
Article 2
Allison, S. R., Johnston, M. J., Craig, G., Jennings, S. (2016). Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring. Sensors, 16(8), 1310-1338.
This research article analyses several types of sensor technologies which are being used in the airborne detection and monitoring of wildfires. The authors provide information regarding the increasing affordability of sensor systems such as hyperspectral cameras, thermal cameras and image intensifiers and how these technologies can be used to combat wildfires. The research considers both the experimental data collected by sensors as well evaluating the operational constraints when the technologies are applied to a realistic context. The main limitation of the article is that many of the key elements have been cited from other sources and therefore there is not an original approach or methodology presented for detecting or monitoring wildfires. The article is useful as the research findings provide a peer reviewed source of information which details the many types of sensors available to detect fires. Furthermore, by detailing the types of sensors available and their limitations, the findings can be used to determine their applicability to modern drone technologies.
Article 3
Ejaz, W., Azam, M. A., Saadat, S., Iqbal, F., & Hanan, A. (2019). Unmanned Aerial Vehicles enabled IoT Platform for Disaster Management. Energies, 12(14), 1-18. doi:10.3390/en12142706
This article discusses the effectiveness of drones and a ground based IoT devices in effectively predicting and responding to wildfires. The main idea is that customised drones which have cellular network connectivity can enhance the effectiveness of disaster management systems. The research considers that in times of disaster that traffic on cellular networks typically increases and can become overwhelmed, therefore, drones must report notifications as quickly as possible after fire detection. A methodology is proposed which uses a push-based communication mechanism when a fire is predicted and a pull-based mechanism is used during disaster-free situations. The article is useful as it considers a key challenge in the prediction of bushfires – communications. The article is also beneficial as it considers several challenges relating to fully autonomous drones in the prevention of fires including; limited energy, spectrum resources, legal requirements and connectivity issues. In addition, the authors explain how machine learning and artificial intelligence can be applied to predict wildfires. As the article is from a reliable source and the information contained in this article covers several areas, it will greatly assist the research project.
Article 4
Hasan, K. M., Shah, N. S., & Shamim, A. M. (2018). Design and development of an aircraft type portable drone for surveillance and disaster management. International Journal of Intelligent Unmanned Systems, 6(3), 147-159. doi:10.1108/IJIUS-02-2018-0004
This article examines the capabilities of autonomous portable drone technology and its suitability for assisting with disaster management scenarios. The authors present the research methodology used in the design and development of the portable drone. This methodology consists of six steps including; survey of current drone technologies, design the system architecture, simulation and modelling of various drone components, development of the specific modules of the drone, integration of the modules, and real-life performance analysis. The main limitation of this research is that a single drone was used throughout the testing rather than a group of drones. Although the research considered both autonomous and non-autonomous testing, the integration and cohesion with a group of drones should be assessed. The article is useful as a portable drone could be used in preventative approaches to detecting bushfires. A portable drone could be a suitable and cost-effective alternative to drones which require fixed base stations. Furthermore, the use of a ballistic recovery system addresses the issue of how to land a drone in remote bushland areas.
Article 5
Harkiran, K., & Sandeep, S. (2019). Adaptive Neuro Fuzzy Inference System (ANFIS) based wildfire risk assessment. Journal of Experimental & Theoretical Artificial Intelligence, 31(4), 599-619. doi:10.1080/0952813X.2019.1591523
This article discusses the importance of early detection and prediction of wildfires using ground-based sensors. The authors propose a paradigm which considers the effectiveness of several IoT sensors deployed throughout a forest. The sensors are programmed with a Fuzzy Logic algorithm consisting of five key functions; temperature, smoke, light, humidity and distance which are used to determine the probability of fire. The research examines the following three-tier architecture for detecting forest fires; Data Perception layer which define the sensors used; Fog Computing layer which is used to quickly analyse and respond to data trends; and Cloud Computing layer which is used for long term data storage and analysis. There are two main limitations of this article; the research relates to the use of ground-based sensors not the use of sensors on drones and the statistical findings are based on experimental results only. The article is useful as it discusses how Fog Computing can be used to perform real-time analysis and data processing to overcome one of the key challenges of preventing wildfires – deriving accurate and timely alerts from large datasets. Furthermore, the article considers that the occurrence of wildfires is likely to increase due to a warming climate which highlights the need for early detection and potential prevention of wildfires.
Article 6
Laszlo, B., Agoston, R., Qiang, X. (2017). Conceptual Approach of Measuring the Professional and Economic Effectiveness of Drone Applications Supporting Forest fire Management. Procedia Engineering 211(1), 8-17. doi:10.1016/j.proeng.2017.12.132
This article examines the economic efficiencies drone technologies could provide when combating forest fires. The main idea is that the application of drone technology will save more forest value than the costs of using the drone. To support the idea, the research proposes a comparison analysis using three scenarios for determining the cost effectiveness of treating forest fires with the use of drones. These scenarios include; a direct analysis which calculates the value saved with and without the use of a drone; an indirect analysis which relies on cost estimations to be made to determine the value of forests and drone lifecycles; and a tactical analysis which considers the use of drones for early detection of fire. The main limitation of this article is that in order to determine the economic benefits use of drones can provide key variables must be estimated using expert judgement – including the value of a forest and the value of potential damage. This article is useful as it suggests that the financial costs associated with the application of drone technologies in the management of forest fires would outweigh the consequential losses if drones were not used. The article specifically considers the economic effectiveness of preventative drone applications, including hot spot detection.
Article 7
Pantelimon, G., Tepe, K., Carriveau, R., & Ahmed, S. (2019). Survey of Multi-agent Communication Strategies for Information Exchange and Mission Control of Drone Deployments. Journal of Intelligent & Robotic Systems, 95(3-4), 779-788. doi:10.1007/s10846-018-0812-x
This article studies the key aspects of communication strategies that are essential to drone swarm formations architectures as well as mission planning and associated communication hardware. The authors propose two categories of communication strategies; centralised – where drones communicate with a base station, and decentralised – where communications are achieved through drone-to-drone interaction. The research also analyses two communication strategies for drone formation control; leader-follower and virtual structure. The authors provide diagrams throughout the article which provides a visual aid to support complex theories. Useful summary tables are used within the article which detail the advantages and disadvantages of the various communication strategies. The research was limited to controlled simulations and field studies were not conducted. This article is useful as the research proposes that a hybrid communication structure would be an ideal solution as it would allow drones to act autonomously and provide capabilities for centralised operation.
Article 8
Pham, H. X., La, H. M., Feil-Seifer, D., & Deans, M. C. (2020). A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking. IEEE Transactions on Systems, Man & Cybernetics. Systems, 50(4), 1537–1548. doi:10.1109/TSMC.2018.2815988
This article proposes a framework for precisely tracking the spread of wildfires using drones and sensor technologies. The research offers a series of control algorithms to support the objective of autonomously directing a drone swarm for monitoring fire front propagation. The research is supported through detailed test results which were produced after running several simulation scenarios. The article is well written as it presents complex research and findings using both text and diagrams to convey the proposition. The main limitation of this article is that the location of the fire must be known in advance as the drones must be directed to that rendezvous point before the control framework can be applied. A secondary limitation is that the research was conducted in a lab environment where test conditions are optimal. This article is useful as it addresses key technological challenges including; maintaining a safe distance from the ground and avoiding in-flight collisions while capturing data and imagery of a moving wildfire. Furthermore, the article is relevant as it was recently published and has been well referenced.
Article 9
Younes, O. S., Hajar, M., Hassan, A. M. (2020). Predictive modelling of wildfires: A new dataset and machine learning approach. Fire Safety Journal, 104(1), 130-146. doi:10.1016/j.firesaf.2019.01.006
This article examines the use of artificial intelligence technologies in the prediction of wildfires. The research determines that wildfire monitoring can be more effective when combined with artificial intelligence (AI) technologies and platforms including; big data, data analytics and machine learning. The authors present a research methodology for creating a dataset which can be used to predict wildfire occurrences which includes who key elements: data collection and data pre-processing. The data collection stage includes: vegetation index information, land surface temperature and thermal anomalies, whereas the data pre-processing stage converts, cleans, clips, interpolates, extrapolates and extracts meaningful information from the data. The information in the article is well presented through the use of several images, graphs, diagrams and tables. The main limitation of this article is that the data collected to conduct the experiments was obtained from satellite images rather than drone images. This article is useful as it determines that through the use of aerial imagery and AI, wildfires could be predicted and therefore prevented.
Article 10
Yuan, C., Zhang, Y., & Liu, Z. (2015). A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Canadian Journal of Forest Research, 45(7), 783–792. doi:10.1139/cjfr-2014-0347
This article provides an assessment of the current capabilities, challenges and potential solutions in regards to monitoring, detecting and fighting forest fires using drones. The authors present useful statistical data regarding the economic and ecological costs and impacts of forest fires as well as assessing the characteristics of drones which are currently being used to fight forest fires. The authors have presented the research findings through the use of tables and images which provides a useful way of comparing the different technologies and approaches. As this is a research article, its main limitation is that it does not offer a theory or methodology in regards to using drones to monitor or detect forest fires. The research article is useful as it describes how drone technologies are being used to monitor, detect and fight fires. The article also concludes that drones with long endurance times would be required in the early detection of forest fires.
Article 11
Yuan, C., Liu, Z., & Zhang, Y. (2017). Aerial Images-Based Forest Fire Detection for Firefighting Using Optical Remote Sensing Techniques and Unmanned Aerial Vehicles. Journal of Intelligent & Robotic Systems, 88(2-4), 635-654. doi:10.1007/s10846-016-0464-7
This article proposes a forest fire detection methodology that uses vision-based sensing features to process images captured from a camera mounted on in-flight drones. The methodology considers the colour and motion features of fires and combines these elements to create decision-making rules. The research also addresses the challenge of capturing images from a drone that is moving in uncertain environments. The main limitation of the research is that testing was conducted using video footage captured from an aircraft and within an indoor environment. This article is useful as it compares several existing technological methods of detecting forest fires including; ground monitoring, manned aircraft, satellites as well as drones. By using reliable vision-based sensors, the financial costs of operating a drone in the detection of fires could be reduced when compared to a drone fitted with several other types of sensors.
Article 12
Yuan, C., Liu, Z., & Zhang, Y. (2019). Learning-Based Smoke Detection for Unmanned Aerial Vehicles Applied to Forest Fire Surveillance. Journal of Intelligent & Robotic Systems, 93(1-2), 337-349. doi:10.1007/s10846-018-0803-y
This article examines how a smoke detection technologies mounted on drones can be used in the early detection of forest fires. The research suggests that flame-based fire detection approaches can be degraded when there is smoke cover. The authors propose a learning-based smoke detection algorithm which analyses the various colour features of smoke from images captured by a drone. The article presents successful experimental results from research performed using several moving images of fires. The research findings are presented in a logical way through the use of tables, diagrams and text. The main limitation of this research is that it does not analyse smoke detection at night. This article is useful as early smoke detection could be used in the prevention of bushfires either as an independent technology or when combined with other sensory systems.