Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
Wildfires are complex natural disasters that significantly impact ecosystems and human communities. The early detection and prediction of forest fire risk are necessary for effective forest management and resource protection. This paper proposes an innovative early detection system based on a wirele...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Fire |
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| Online Access: | https://www.mdpi.com/2571-6255/8/7/245 |
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| _version_ | 1849303494198034432 |
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| author | Safiah Almarri Hur Al Safwan Shahd Al Qisoom Soufien Gdaim Abdelkrim Zitouni |
| author_facet | Safiah Almarri Hur Al Safwan Shahd Al Qisoom Soufien Gdaim Abdelkrim Zitouni |
| author_sort | Safiah Almarri |
| collection | DOAJ |
| description | Wildfires are complex natural disasters that significantly impact ecosystems and human communities. The early detection and prediction of forest fire risk are necessary for effective forest management and resource protection. This paper proposes an innovative early detection system based on a wireless sensor network (WSN) composed of interconnected Arduino nodes arranged in a hybrid circular/star topology. This configuration reduces the number of required nodes by 53–55% compared to conventional Mesh 2D topologies while enhancing data collection efficiency. Each node integrates temperature/humidity sensors and uses ZigBee communication for the real-time monitoring of wildfire risk conditions. This optimized topology ensures 41–81% lower latency and 50–60% fewer hops than conventional Mesh 2D topologies. The system also integrates artificial intelligence (AI) algorithms (multiclass logistic regression) to process sensor data and predict fire risk levels with 99.97% accuracy, enabling proactive wildfire mitigation. Simulations for a 300 m radius area show the non-dense hybrid topology is the most energy-efficient, outperforming dense and Mesh 2D topologies. Additionally, the dense topology achieves the lowest packet loss rate (PLR), reducing losses by up to 80.4% compared to Mesh 2D. Adaptive routing, dynamic round-robin arbitration, vertical tier jumps, and GSM connectivity ensure reliable communication in remote areas, providing a cost-effective solution for wildfire mitigation and broader environmental monitoring. |
| format | Article |
| id | doaj-art-6a7b6cb0b6f94bad8434b40e99d5610d |
| institution | Kabale University |
| issn | 2571-6255 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fire |
| spelling | doaj-art-6a7b6cb0b6f94bad8434b40e99d5610d2025-08-20T03:58:26ZengMDPI AGFire2571-62552025-06-018724510.3390/fire8070245Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote AreasSafiah Almarri0Hur Al Safwan1Shahd Al Qisoom2Soufien Gdaim3Abdelkrim Zitouni4Department of Physics, College of Science & Humanities Jubail, Imam Abdulrahman Bin Faisal University, Dammam 31961, Saudi ArabiaDepartment of Physics, College of Science & Humanities Jubail, Imam Abdulrahman Bin Faisal University, Dammam 31961, Saudi ArabiaDepartment of Physics, College of Science & Humanities Jubail, Imam Abdulrahman Bin Faisal University, Dammam 31961, Saudi ArabiaPRINCE Research Laboratory, ISITCom, Sousse University, Sousse 4011, TunisiaDepartment of Physics, College of Science & Humanities Jubail, Imam Abdulrahman Bin Faisal University, Dammam 31961, Saudi ArabiaWildfires are complex natural disasters that significantly impact ecosystems and human communities. The early detection and prediction of forest fire risk are necessary for effective forest management and resource protection. This paper proposes an innovative early detection system based on a wireless sensor network (WSN) composed of interconnected Arduino nodes arranged in a hybrid circular/star topology. This configuration reduces the number of required nodes by 53–55% compared to conventional Mesh 2D topologies while enhancing data collection efficiency. Each node integrates temperature/humidity sensors and uses ZigBee communication for the real-time monitoring of wildfire risk conditions. This optimized topology ensures 41–81% lower latency and 50–60% fewer hops than conventional Mesh 2D topologies. The system also integrates artificial intelligence (AI) algorithms (multiclass logistic regression) to process sensor data and predict fire risk levels with 99.97% accuracy, enabling proactive wildfire mitigation. Simulations for a 300 m radius area show the non-dense hybrid topology is the most energy-efficient, outperforming dense and Mesh 2D topologies. Additionally, the dense topology achieves the lowest packet loss rate (PLR), reducing losses by up to 80.4% compared to Mesh 2D. Adaptive routing, dynamic round-robin arbitration, vertical tier jumps, and GSM connectivity ensure reliable communication in remote areas, providing a cost-effective solution for wildfire mitigation and broader environmental monitoring.https://www.mdpi.com/2571-6255/8/7/245forest firehybrid WSNenergy efficiencyadaptive routingartificial intelligence |
| spellingShingle | Safiah Almarri Hur Al Safwan Shahd Al Qisoom Soufien Gdaim Abdelkrim Zitouni Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas Fire forest fire hybrid WSN energy efficiency adaptive routing artificial intelligence |
| title | Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas |
| title_full | Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas |
| title_fullStr | Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas |
| title_full_unstemmed | Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas |
| title_short | Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas |
| title_sort | optimized wireless sensor network architecture for ai based wildfire detection in remote areas |
| topic | forest fire hybrid WSN energy efficiency adaptive routing artificial intelligence |
| url | https://www.mdpi.com/2571-6255/8/7/245 |
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