Smart City Gnosys

Smart city article details

Title	Towards Lightweight Fire Detection At The Extreme Edge Based On Decision Trees
ID_Doc	58208
Authors	Bittencourt J.C.N.; Costa D.G.; Portugal P.; Vasques F.
Year	2024
Published	2024 IEEE 22nd Mediterranean Electrotechnical Conference, MELECON 2024
DOI	http://dx.doi.org/10.1109/MELECON56669.2024.10608598
Abstract	The prevalence of fires in modern urban landscapes continues to be reported as a significant challenge due to their potential negative consequences. This enduring threat heightened by climate changes necessitates the development of innovative approaches for early detection, incorporating a variety of sensor-based technologies across diverse scopes. Although effective, traditional detection methods based on Unmanned Aerial Vehicles (UAVs) or camera sensors often encounter cost, scalability, and deployment limitations, particularly for vast areas to be covered. On the other hand, scalar sensors for threshold-based fire detection are limited to the context and require proper calibration for different scenarios. This paper addresses these limitations by introducing a novel approach that leverages a sensor-based decision tree model for fire and smoke detection using micro Internet of Things (IoT) devices. The core of this approach lies in developing an efficient, lightweight model suited for extreme edge devices, where performance and energy efficiency are critical. We validated our model using a public dataset, demonstrating its potential effectiveness in real-world scenarios. By providing a sensor-based solution that is both scalable and cost-effective, the proposed solution emerges as a promising tool for augmenting fire safety measures in urban and wildland areas. © 2024 IEEE.
Author Keywords	Artificial intelligence; Embedded systems; Smart cities; Wireless sensor networks

Similar Articles

Id	Similarity	Authors	Title	Published
380	0.896	Mukherjee A.; Rakshit S.; Panja A.K.; De D.; Dey N.	A 6G-Enabled Edge-Assisted Internet Of Drone Things Ecosystem For Fire Detection	Lecture Notes in Networks and Systems, 975 (2024)
26576	0.887	Dhamodaran S.; Ahamad S.; Ramesh J.V.N.; Sathappan S.; Namdev A.; Kanse R.R.; Pramanik S.	Fire Detection System Utilizing An Aggregate Technique In Uav And Cloud Computing	Handbook of Research on Thrust Technologies? Effect on Image Processing (2023)
51920	0.87	Alqarafi L.; Aloufi N.; Alluqmani R.; Alrehaili S.; Alotaibi M.	Smokeaway: An Early Smoke Detection Iot Architecture For Outdoors	2023 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2023 (2023)
44489	0.869	Dhabliya D.; Dari S.S.; Sakhare N.N.; Dhablia A.K.; Pandey D.; George A.S.; Hameed A.S.; Dadheech P.	Real-World Implementation Of Cloud Computing New Technologies	Emerging Trends in Cloud Computing Analytics, Scalability, and Service Models (2024)
61276	0.868	Nadeem M.; Dilshad N.; Alghamdi N.S.; Dang L.M.; Song H.-K.; Nam J.; Moon H.	Visual Intelligence In Smart Cities: A Lightweight Deep Learning Model For Fire Detection In An Iot Environment	Smart Cities, 6, 5 (2023)
59290	0.866	Hussain T.; Dai H.; Gueaieb W.; Sicklinger M.; De Masi G.	Uav-Based Multi-Scale Features Fusion Attention For Fire Detection In Smart City Ecosystems	ISC2 2022 - 8th IEEE International Smart Cities Conference (2022)
8257	0.865	Talaat F.M.; ZainEldin H.	An Improved Fire Detection Approach Based On Yolo-V8 For Smart Cities	Neural Computing and Applications, 35, 28 (2023)
35731	0.864	Said O.; Hamza B.M.; Hibatallah M.; Ali E.M.; Abdellatif E.A.	Low-Cost Real-Time Fire Detection In Smart Cities: Logistic Regression Is All You Need	Proceedings of 2024 1st Edition of the Mediterranean Smart Cities Conference, MSCC 2024 (2024)
38658	0.862	Vaegae N.K.; Annepu V.; Bagadi K.; Dahan F.	Multisensor Fuzzy Logic Approach For Enhanced Fire Detection In Smart Cities	Journal of Optimization, 2024, 1 (2024)
17955	0.86	Qadir Z.; Le K.; Bao V.N.Q.; Tam V.W.Y.	Deep Learning-Based Intelligent Post-Bushfire Detection Using Uavs	IEEE Geoscience and Remote Sensing Letters, 21 (2024)