Duangsuwan, Sarun

Preferred name

Duangsuwan, Sarun

Alternative Name

Duangsuwan, S.

Main Affiliation

KMITL Prince of Chumphon Campus

sarun.du@kmitl.ac.th

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Comparison of path loss prediction models for UAV and IoT air-to-ground communication system in rural precision farming environment
(2021-02-01)
Duangsuwan, Sarun
;
Maw, Myo Myint
The comparison of path loss model for the unmanned aerial vehicle (UAV) and Internet of Things (IoT) air-to-ground communication system was proposed for rural precision farming. Due to the uncertainty of propagation channel in rural precision farming environment, the comparison of path loss prediction was investigated by the conventional particle swarm optimization (PSO) algorithms: PSO (exponential or Exp), PSO (polynomial or Poly) and the machine learning algorithms: k-nearest neighbor (k-NN), and random forest, are exploited to accurate the path loss models on the basic of the measured dataset. Meanwhile, the empirical model in the rural precision farming was considered. By using the machine learning-based algorithms, the coefficient of determination (R-squared: R<sup>2</sup>) and root mean squared error (RMSE) were evaluated as highly accuracy and precision more than the conventional PSO algorithms. According to the results, the random forest method was able to perform more than other methods. It has the smallest prediction errors.
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Quantitative Measurement of Path Loss Model Adaptation Using the Least Squares Method in an Urban DVB-T2 System
(2018-01-01)
Keawbunsong, Pitak
;
Duangsuwan, Sarun
;
Supanakoon, Pichaya
;
Promwong, Sathaporn
The aim of this paper was to propose quantitative measurement of path loss model adaptation in urban radio propagation for a second-generation, terrestrial digital video broadcasting standard (DVB-T2) system. The measurement data was analyzed using data processing based on the least squares (LS) method to verify the probabilistic quantitation of realistic data measurement such as mean error (ME), root mean square error (RMSE), and standard deviation of error (SD), as well as relative error (RE). To distinguish the experimental evaluation, the researchers compared between the conventional Hata path loss model and the proposed model. The result showed that path loss based on the proposed model was more accurate in predicting the quantitative measurement of propagation data properly.
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Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios
(2021-01-01)
Duangsuwan, Sarun
;
Juengkittikul, Phakamon
;
Myint Maw, Myo
The purpose of this paper was to predict the path loss characterization of the ground-to-air (G2A) communication channel between the ground sensor (GS) and unmanned aerial vehicle (UAV) using machine learning (ML) models in smart farming (SF) scenarios. Two ML algorithms such as support vector regression (SVR) and artificial neural network (ANN) were studied to analyze the measured data in different scenarios with Napier and Ruzi grass farms as the measurement locations. The proposed empirical GS-to-UAV two-ray (GUT-R) model and the ML models were compared to characterize path loss prediction models. The performances of the path loss prediction models were evaluated using the statistical error indicators in different measurement locations and UAV trajectories. To obtain the statistical error indicators, the accuracy path loss results of UAV trajectory at 2 m altitudes showed the SVR model (MAE = 1.252 dB, RMSE = 3.067 dB, and R2 = 0.972) and the ANN model (MAE = 1.150 dB, RMSE = 2.502 dB, and R2 = 0.981) for the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 1.202 dB, RMSE = 2.962 dB, and R2 = 0.965) and the ANN model (MAE = 1.146 dB, RMSE = 2.507 dB, and R2 = 0.983) were presented. For UAV trajectory at 5 m altitudes, the SVR model (MAE = 2.125 dB, RMSE = 4.782 dB, and R2 = 0.933) and the ANN model (MAE = 2.025 dB, RMSE = 4.439 dB, and R2 = 0.950) were resulted in the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 2.112 dB, RMSE = 4.682 dB, and R2 = 0.935) and the ANN model (MAE = 2.016 dB, RMSE = 4.407 dB, and R2 = 0.954) were displayed. The proposed ML models using SVR and ANN can optimally predict the path loss characterization in SF scenarios, where the accuracy was 95% for the SVR and 97% for the ANN.
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Empirical Path Loss Channel Characterization Based on Air-to-Air Ground Reflection Channel Modeling for UAV-Enabled Wireless Communications
(2021-01-01)
Supramongkonset, Jatuporn
;
Duangsuwan, Sarun
;
Maw, Myo Myint
;
Promwong, Sathaporn
The purpose of this work was to investigate the air-to-air channel model (A2A-CM) for unmanned aerial vehicle- (UAV-) enabled wireless communications. Specifically, a low-altitude small UAV needs to characterize the propagation mechanisms from ground reflection. In this paper, the empirical path loss channel characterizations of A2A ground reflection CM based on different scenarios were presented by comparing the wireless communication modules for UAVs. Two types of wireless communication modules both WiFi 2.4 GHz and LoRa 868 MHz frequency were deployed to study the path loss channel characterization between Tx-UAV and Rx-UAV. To investigate the path loss, three types of experimental channel models, such as CM1 grass floor, CM2 soil floor, and CM3 rubber floor, were considered under the ground reflection condition. The analytical A2A Two-Ray (A2AT-R) model and the modified Log-Distance model were simulated to compare the correlation with the measurement data. The measurement results in the CM3 rubber floor scenario showed the impact from the ground reflection at 1 m to 3 m Rx-UAV altitudes both 2.4 GHz and 868 MHz which was converged to the A2AT-R model and related to the modified Log-Distance model above 3 m. It clear that there is no ground reflection effect from the CM1 grass floor and CM2 soil floor. This work showed that the analytical A2AT-R model and the modified Log-Distance model can deploy to model the path loss of A2A-CM by using WiFi and LoRa wireless modules.
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Development of drone real-time air pollution monitoring for mobile smart sensing in areas with poor accessibility
(2020-01-01)
Duangsuwan, Sarun
;
Jamjareekulgarn, Punyawi
The topic of air pollution, especially in terms of particulate matter (PM), is a very serious problem in current society. This problem is caused by such factors as forest fires, construction, industrialization, and the ever-increasing number of motor vehicles. Thus, PM2.5 has become an important risk factor for citizens in Thailand as well as globally, not only in terms of the problems associated with health risks, but also the negative impact on the image of the country. Measuring pollution for air quality monitoring is a challenging task, especially when considering areas that have poor accessibility. The aim of this work is to develop a drone equipped with sensors to monitor and collect air quality data in real time from such areas of potential pollution. The proposed drone is called the drone for real-time air pollution monitoring (Dr-TAPM) and is equipped with the ability to measure the concentration of carbon monoxide (CO), ozone (O<inf>3</inf>), nitrogen dioxide (NO<inf>2</inf>), PM, and sulfur dioxide (SO<inf>2</inf>). Additionally, the collected data is transmitted to a cloud server every second over a wireless internet connection. In this study, the measurement was conducted in the experiment area, which is considered to be in the pollutant model scenario. The experimental results are shown as graphs of quantitative pollutant levels and air quality index (AQI) values obtained from realtime monitoring on a mobile application.
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Drone-Enabled AI Edge Computing and 5G Communication Network for Real-Time Coastal Litter Detection
(2024-12-01)
Duangsuwan, Sarun
;
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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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
;
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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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
;
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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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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High performance detection using three different weight blind algorithms in ZF receiver for uplink multi-user massive MIMO in 5G wireless communications
(2018-09-01)
Duangsuwan, Sarun
;
Teekapakvisit, Chakree
A novel of an uplink multi-user massive Multi-Input Multi-Output (MIMO) in 5G wireless communication that is an increasing efficiency of massive MIMO detection, in order to support a very high speed Giga-Wireless (GiWi). One of the most focused on the Zero-Forcing (ZF) receiver, but due to it has no optimal. In this paper, we present blind algorithms to optimize the performance of ZF because of their low complexity. The three different weight blind algorithms are various proposed by using the Conventional Constant Modulus (CCM), Supervised Constant Modulus (SCM), and the Variable Step Size Constant Modulus (VSSCM). The performance results show as a channel response, Mean Square Error (MSE), and Bit Error Rate (BER) is discussed. It can be shown that the proposed blind algorithms can optimal efficiency of ZF receiver under an assumption of no required CSI.

Duangsuwan, Sarun

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