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Development of an AIoT-Based Early Flash-Flood Warning System for Smart Rural Disaster Resilience
(2026-06-01)
Wiangnak, Visit
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Wiboonrat, Montri
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Duangsuwan, Sarun
This paper presents the development of an AIoT-based early flash-flood warning system to enhance disaster resilience in smart rural communities. The framework integrates multi-source hydrological sensors, AI-enabled edge–cloud computing, and a mobile alert application to provide real-time monitoring and short-term flood forecasting, and includes an intelligent hybrid model combines YOLOv10 for visual water-level detection from CCTV imagery with a long short-term memory (LSTM) network for hydrological time-series prediction. The system was deployed and evaluated at two sites in Thailand: the Ban Luang station in Chiang Mai and the Chumkho station in Chumphon. The experimental results show near-perfect detection performance by YOLOv10, with precision and mAP@0.5 exceeding 0.99 across varying water-level conditions. The LSTM model achieved high forecasting accuracy, with an R<sup>2</sup> of 0.987 at Ban Luang and 0.781 at Chumkho, reflecting site-specific hydrodynamic complexity. The results confirm that integrating AIoT-based visual sensing with data-driven forecasting significantly improves the reliability, responsiveness, and robustness of early flash-flood warning systems in rural environments.
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Experimental RSSI, SINR, and Throughput Analysis of Drone-Enabled UOC-RF Communication for Real-Time Underwater Video Streaming
(2026-03-01)
Duangsuwan, Sarun
Highlights: What are the main findings? Experimental evaluation of a drone-enabled hybrid UOC-RF communication for real-time underwater video streaming from offshore to onshore. What are the implications of the main findings? The evaluation of RSSI, SINR, and throughput was conducted over 5G networks at 700 MHz and 2600 MHz, with ROV video streaming at 10 to 60 frames per second (fps). Results confirm feasibility and provide practical design insights for integrating underwater drone-enabled hybrid communication into marine observation. This paper proposes a hybrid underwater drone communication system that combines underwater optical communication (UOC) and radio-frequency (RF) communication to support real-time video streaming in underwater environments. The system consists of a remotely operated vehicle (ROV) that transmits video to a surface gateway, which relays the video to onshore facilities through a 5G network. An outdoor experiment conducted in a maritime environment measured the received signal strength indicator (RSSI), signal-to-interference-plus-noise ratio (SINR), occupied bandwidth, and end-to-end (E2E) throughput at 700 MHz and 2600 MHz with video frame rates ranging from 10 to 60 fps. The results show that the 700 MHz frequency band provides higher RSSI and SINR, which support more reliable long-range communications, while the 2600 MHz frequency band provides lower RSSI and SINR but a larger bandwidth. The maximum E2E throughput achieved was 53.5 Mbps at 700 MHz and 58.64 Mbps at 2600 MHz. Increasing frame rates mainly affects throughput by reducing SINR. These results analyze the coverage–capacity trade-off and provide valuable insights for drone-assisted hybrid UOC-RF communication in underwater video streaming applications.
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Underwater Drone-Enabled Wireless Communication Systems for Smart Marine Communications: A Study of Enabling Technologies, Opportunities, and Challenges
(2025-11-01)
Duangsuwan, Sarun
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Klubsuwan, Katanyoo
Highlights: What are the main findings? This paper reviews underwater wireless communication methods, including acoustic, optical, and RF communication, in marine applications, and explores the potential of existing underwater drones. This paper examines the opportunities and challenges of hybrid wireless communication systems for underwater drones. What is the implication of the main finding? This paper considers the integration of underwater drones, IoUT, AI-driven data, VR, and DT for smart marine communications. Underwater drones such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) are revolutionizing underwater operations and are essential for advanced marine applications like environmental monitoring, deep-sea exploration, and marine surveillance. In this paper, we concentrate on the enabling technologies and wireless communication strategies for underwater drones. Specifically, we analyze acoustic, optical, and radio frequency (RF) approaches, along with their respective advantages and disadvantages. We investigate the potential of integrating underwater drone-enabled wireless communication systems for smart marine communications. The study highlights the benefits of combining acoustic, optical, and RF methods to improve connectivity and data reliability. A hybrid underwater communication system is ideal for underwater drones because it can reduce latency, increase data throughput, and improve adaptability under various underwater conditions, supporting smart marine communications. The future direction involves developing hybrid communication frameworks that incorporate the Internet of Underwater Things (IoUT), AI-driven data, virtual reality (VR), and digital twin (DT) technologies, enabling a next-generation smart marine ecosystem.
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Drone-Enabled AI Edge Computing and 5G Communication Network for Real-Time Coastal Litter Detection
(2024-12-01)
Duangsuwan, Sarun
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Prapruetdee, Phoowadon
Coastal litter is a severe environmental issue impacting marine ecosystems and coastal communities in Thailand, with plastic pollution posing one of the most urgent challenges. Every month, millions of tons of plastic waste enter the ocean, where items such as bottles, cans, and other plastics can take hundreds of years to degrade, threatening marine life through ingestion, entanglement, and habitat destruction. To address this issue, we deploy drones equipped with high-resolution cameras and sensors to capture detailed coastal imagery for assessing litter distribution. This study presents the development of an AI-driven coastal litter detection system using edge computing and 5G communication networks. The AI edge server utilizes YOLOv8 and a recurrent neural network (RNN) to enable the drone to detect and classify various types of litter, such as bottles, cans, and plastics, in real-time. High-speed 5G communication supports seamless data transmission, allowing efficient monitoring. We evaluated drone performance under optimal flying heights above ground of 5 m, 7 m, and 10 m, analyzing accuracy, precision, recall, and F1-score. Results indicate that the system achieves optimal detection at an altitude of 5 m with a ground sampling distance (GSD) of 0.98 cm/pixel, yielding an F1-score of 98% for cans, 96% for plastics, and 95% for bottles. This approach facilitates real-time monitoring of coastal areas, contributing to marine ecosystem conservation and environmental sustainability.
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Exploring Ground Reflection Effects on Received Signal Strength Indicator and Path Loss in Far-Field Air-to-Air for Unmanned Aerial Vehicle-Enabled Wireless Communication
(2024-11-01)
Duangsuwan, Sarun
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Jamjareegulgarn, Punyawi
Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection.
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Accuracy Assessment of Drone Real-Time Open Burning Imagery Detection for Early Wildfire Surveillance
(2023-09-01)
Duangsuwan, Sarun
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Klubsuwan, Katanyoo
Open burning is the main factor contributing to the occurrence of wildfires in Thailand, which every year result in forest fires and air pollution. Open burning has become the natural disaster that threatens wildlands and forest resources the most. Traditional firefighting systems, which are based on ground crew inspection, have several limits and dangerous risks. Aerial imagery technologies have become one of the most important tools to prevent wildfires, especially drone real-time monitoring for wildfire surveillance. This paper presents an accuracy assessment of drone real-time open burning imagery detection (Dr-TOBID) to detect smoke and burning as a framework for a deep learning-based object detection method using a combination of the YOLOv5 detector and a lightweight version of the long short-term memory (LSTM) classifier. The Dr-TOBID framework was designed using OpenCV, YOLOv5, TensorFlow, LebelImg, and Pycharm and wirelessly connected via live stream on open broadcaster software (OBS). The datasets were separated by 80% for training and 20% for testing. The resulting assessment considered the conditions of the drone’s altitudes, ranges, and red-green-black (RGB) mode in daytime and nighttime. The accuracy, precision, recall, and F1-Score are shown for the evaluation metrics. The quantitative results show that the accuracy of Dr-TOBID successfully detected open burning monitoring, smoke, and burning characteristics, where the average F1-score was 80.6% for smoke detection in the daytime, 82.5% for burning detection in the daytime, 77.9% for smoke detection at nighttime, and 81.9% for burning detection at nighttime.
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Performance Analysis of Unmanned Aerial Vehicle Assisted Wireless IoT Sensors Based on Air-to-Ground Communication Model for Smart Farming
(2023-01-01)
Duangsuwan, Sarun
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Promwong, Sathaporn
We used an unmanned aerial vehicle (UAV) and IoT as a new platform for soil moisture monitoring based on the air-to-ground (A2G) communication model. We investigated an energyefficient UAV trajectory by considering the power outage probability and transmission rate for UAV-assisted wireless IoT sensor connectivity. We considered the closed-form power outage probability for IoT sensors located within the coverage zone of a UAV drone small cell. We conducted experiments in the Napier and Ruzi grass farms with IoT sensors for detecting soil moisture along a drip line irrigation system located in the fields. The power outage probability is given for different UAV heights and transmission rates, which contributes to reliable communication with IoT sensors.
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3D AQI Mapping Data Assessment of Low-Altitude Drone Real-Time Air Pollution Monitoring
(2022-08-01)
Duangsuwan, Sarun
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Prapruetdee, Phoowadon
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Subongkod, Mallika
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Klubsuwan, Katanyoo
Air pollution primarily originates from substances that are directly emitted from natural or anthropogenic processes, such as carbon monoxide (CO) gas emitted in vehicle exhaust or sulfur dioxide (SO<inf>2</inf>) released from factories. However, a major air pollution problem is particulate matter (PM), which is an adverse effect of wildfires and open burning. Application tools for air pollution monitoring in risk areas using real-time monitoring with drones have emerged. A new air quality index (AQI) for monitoring and display, such as three-dimensional (3D) mapping based on data assessment, is essential for timely environmental surveying. The objective of this paper is to present a 3D AQI mapping data assessment using a hybrid model based on a machine-learning method for drone real-time air pollution monitoring (Dr-TAPM). Dr-TAPM was designed by equipping drones with multi-environmental sensors for carbon monoxide (CO), ozone (O<inf>3</inf>), nitrogen dioxide (NO<inf>2</inf>), particulate matter (PM<inf>2.5,10</inf>), and sulfur dioxide (SO<inf>2</inf>), with data pre- and post-processing with the hybrid model. The hybrid model for data assessment was proposed using backpropagation neural network (BPNN) and convolutional neural network (CNN) algorithms. Experimentally, we considered a case study detecting smoke emissions from an open burning scenario. As a result, PM<inf>2.5,10</inf> and CO were detected as air pollutants from open burning. 3D AQI map locations were shown and the validation learning rates were apparent, as the accuracy of predicted AQI data assessment was 98%.
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A Study of A2A Channel Modeling for Small UAV-Enabled Wireless Communication
(2022-01-01)
Supramongkonset, Jatuporn
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Duangsuwan, Sarun
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Promwong, Sathaporn
The objective of this work is to study the ground reflection modeling case study of air-to-air (A2A) channel modeling for an unmanned aerial vehicle (UAV)-enabled wireless communications. We consider the Internet of Things (IoT) WiFi modules at 2.4 GHz standard enabled with the transmitter Tx- UAV and the receiver Rx-UAV. The received signal strength indicator (RSSI) and path loss characteristics are addressed by using the free space path loss (FSPL) model, air-to-air two- ray (A2AT-R) model, and the modified Log-distance path loss model. The measurement results indicated that the limit of power constraint of WiFi modules was a level at 10 m Rx-UAV altitude, and the received sensitivity was limited at -95 dBm. It is shown that the impact of ground reflection path loss was characterized at 1-3 m Rx-Uavaltitudes which are accordingly related to the A2AT-R model and the relative resulting with the modified Log- distance model above 4 m Rx-UAV altitudes.
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A WiFi Link Budget Analysis of Drone-based Communication and IoT Ground Sensors
(2021-04-01)
Supramongkonset, Jatupotn
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Duangsuwan, Sarun
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Promwong, Sathapotn
An application of drone-based wireless networking in agricultural scenarios needs to analyze the radio link budget because of the uncertain propagation from the surrounding. This paper presents a link budget analysis between drone as a drone small cell (DSC) and Internet of Things (IoT) as a ground sensor. The measurement results show the received signal strength indicator (RSSI), path loss, and delay spread at 2.4 GHz frequency when considering the numbers of ground sensors to 15 points and use a single drone enabled with WiFi portable link. It can be found that drone DSC can compensate for the limited power of ground sensors and link budget analysis can optimally evaluate the communication channel in this scenario.

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