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Enhancing Wi-Fi-based Fingerprint Technique for Indoor Positioning System
(2024-01-01)
Nimnaul, Tanapol
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Bureetes, Natchapong
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Siriwat, Siwat
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Wongwirat, Olarn
This paper addresses the enhancement of the Wi-Fi-based fingerprint technique for an indoor positioning system applied in an experimental area. The conventional Wi-Fi-based fingerprint technique utilizes a k-nearest neighbor (k-NN) algorithm for position estimation. The k-NN algorithm is a simple and intuitive classification algorithm based on distance metric, i.e., Euclidean distance (ED), but often demonstrates limited accuracy. To mitigate this constraint and enhance positioning precision, advanced machine learning algorithms in artificial neural networks (ANNs) have been introduced. Although ANN algorithms are considered highly reliable, they are complex and resource-intensive algorithms, resulting in less suitable for a small-scale area that requires simple indoor positioning applications. In contrast, the random forest (RF) algorithm offers comparable positioning accuracy while being more computationally efficient, making it a favorable choice for such scenarios. The work in this paper enhances the accuracy of the Wi-Fi-based fingerprint technique for indoor positioning systems by adopting the RF algorithm over the k-NN alternative for position estimation accuracy. The number of received signal strength (RSS) data selected from appropriate access points (APs) in the area chosen by a feature selection method is a pivotal factor influencing accuracy improvements. The experimental results express the direct correlation between increased RSS data and accuracy improvement for both algorithms. Significantly, the application of the feature selection method using the information gain ratio augments the positioning accuracy specifically for the RF algorithm.
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Improving Machine Learning-Based Wi-Fi Fingerprint Technique with Feature Selection and Grid Search Methods
(2024-01-01)
Nimnaul, Tanapol
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Wongwirat, Olarn
This paper presents the enhancement of the WiFi-based fingerprint technique for an indoor positioning system applied to a real experimental area. In typical Wi-Fi-based fingerprint techniques, classification algorithms such as k-nearest neighbor (k-NN), decision tree (DT), and random forest (RF) are used for position estimation. However, these algorithms do not perform well with high-dimensional and large datasets. They also face limitations related to overfitting and uninformative features in datasets. This paper overcomes these challenges by deploying feature selection based on filter methods. This process removes uninformative features from the datasets before feeding them to construct a training model. The grid search method is also employed to perform hyperparameter tuning, which is used to construct the outperforming models. The experimental setup involved collecting received signal strength indicators (RSSIs) from access points (APs) in the real indoor environment to create the radio map. The accuracy performance of the proposed methods was tested by employing the feature selection method on the radio map dataset and using the grid search method to find optimized hyperparameters for constructing the training models based on the k-NN, DT, and RF algorithms. The accuracy results were compared with those of non-feature selection and default hyperparameters used for the three algorithms. The computational results demonstrate that the RF algorithm outperforms the other two algorithms. Furthermore, performance is improved when using grid search and feature selection as proposed.
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Development of WLAN Topology Display System
(2023-01-01)
Wongwirat, Olarn
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Chotiphan, Supannada
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Hongtong, Tadchapon
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Suttijumnong, Nattawat
Currently, packet sniffer tools can capture packets and provide information for monitoring, analyzing, and troubleshooting networks, e.g., traffic, bandwidth, protocol, etc. However, these tools lack the capability, or feature, to display a network topology diagram on the screen, particularly for a WLAN (Wireless Local Area Network), which is different from expensive network monitoring software sold commercially in the market. Therefore, it is difficult for a network administrator to visualize which client is connected to which AP (Access point) in the service areas of WLAN, or hotspots, for monitoring. This paper presents the development of the WLAN topology display system to support the network administrator in monitoring and enhancing the network services in the future. The system acquires the packet data captured by the packet sniffer tool, i.e., Wireshark. Then, the packet information is analyzed by using the data from the MAC (Medium Access Control) header following the IEEE802.11 standard to find the types, connection modes, and MAC addresses. Finally, the mapping table associated with the connected devices, i.e., client stations and APs, is constructed and used to create the WLAN topology diagram to display on the screen. The prototype system is implemented and tested in the laboratory environment, and the WLAN topology diagram showing the connections between the clients and the APs can be displayed on screen accurately as required.
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Performance Measurement of Energy Optimal Path Finding for Waste Collection Robot Using ACO Algorithm
(2022-01-01)
Tomitagawa, Koki
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Anuntachai, Anuntapat
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Chotipant, Supannada
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Wongwirat, Olarn
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Kuchii, Shigeru
Indoor waste collection that utilizes mobile robots can solve the labor cost and manpower shortage but has the problem of limited energy resources, making it difficult to operate for long periods of time. Therefore, it is important to reduce the energy consumption for efficient waste collection. The waste collection robot can be modeled as a Capacitated Vehicle Routing Problem (CVRP), where heuristics algorithms can be deployed to search for the most energy-efficient path. This paper proposes the Ant Colony Optimization (ACO) algorithm for finding the optimal path of the waste collection robot. Energy consumption of the robot depends not only on the travel path but also on the weight of the waste it carries. Therefore, the proposed ACO algorithm utilizes the path distance and waste weight as the visibility. The travel distance and energy consumption are also used to determine the updated pheromone. Whereas the conventional and adapted ACO algorithms use only either the path distance or the waste weight as the visibility, respectively. The simulation experiments are conducted to compare the travel distance and the energy consumption that the waste collection robot takes by using the conventional, adapted, and proposed ACO algorithms. In the simulation experiments, the number of nodes, the waste weight, and the carrying capacity are used as parameters to verify the performance under the determined environment. The simulation results express that the proposed ACO algorithm provides a better energy optimal path in terms of travel distance and energy consumption than the conventional and adapted ACO algorithms.
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Adapted ACO Algorithm for Energy-Efficient Path Finding of Waste Collection Robot
(2022-01-01)
Tomitagawa, Koki
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Anuntachai, Anuntapat
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Chotiphan, Supannada
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Wongwirat, Olarn
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Kuchii, Shigeru
Waste collection is a major concern of many companies with large areas of facility, e.g., buildings or factories, where there are many trash bins at various dumping points. Therefore, they require human labor to handle, which is a major cost of consideration. Currently, there are research works using robots for waste collection instead of humans. There is a challenge for waste collection robots in terms of energy consumption to pick up the waste at various dumping points efficiently. The factors related to the energy consumption of waste collection robots are directly related to the distance and waste weight that the robots have to collect and carry from the trash bins at various dump points along the paths. This paper presents the adapted ant colony optimization (ACO) algorithm to find the energy-efficient paths of the waste collection robots. The adapted ACO algorithm uses the waste weight in the trash bin as path heuristic information between two dumping points to determine the state transition probability for finding the most energy-efficient path. The experiment was conducted by the simulation to compare the result with the conventional ACO algorithm that uses distance as the path heuristic information. The simulation results expressed that the adapted ACO algorithm provided the most energy-efficient path under the number of nodes and waste weights specified better than the conventional ACO algorithm.
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Energy optimal path finding for waste collection robot using ant colony optimization algorithm
(2021-01-01)
Tomitagawa, Koki
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Chotiphan, Supannada
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Kuchii, Shigeru
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Anuntachai, Anuntapat
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Wongwirat, Olarn
Solid Waste Management (SWM) has always been an important consideration for any country, and among the operational steps of SWM, Solid Waste Collection (SWC) has become one of the most challenging ones. Currently, most of the vehicles used for waste collection require workers and have the problem of emitting CO2. Compared to waste collection by vehicles, waste collection using mobile robots has the advantage of not consuming personnel and not emitting CO2, which is harmful to the environment. However, while mobile robots can solve the shortage of manpower and environmental problems, they also have the problem of limited energy resources. In order for mobile robots to collect waste more efficiently, we designed the waste collection problem as a Capacitated Vehicle Routing Problem (CVRP) and optimized it using the Ant Colony Optimization (ACO) algorithm. The ACO algorithm proposed in this study focuses on the energy consumption of the mobile robot performing waste collection and searches for a route with less energy consumption by using the waste weight as the weighting factor. The preliminary performance verification of the proposed method is compared with the existing conventional ACO algorithm using the CVRP benchmark.
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An Automated ICD-10 Code Assigning System using A Classification Method
(2021-01-01)
Singto, Chanida
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Wongwirat, Olarn
At present, some hospitals in Thailand have to manually analyze patient treatment data for assigning the disease diagnostic code, or ICD-10 (International Classification of Diseases and Related Health Problem 10th Revision) code. The ICD-10 codes are collected and submitted to the Ministry of Public Health to collect Thailand's disease incidence statistics and allocate a budget for the development of the country's health system. These hospitals have difficulty recruiting personnel with expertise in analyzed and assigned ICD-10 codes, causing a long working time and a problem with the accuracy of the analyzed and assigned ICD-10 codes, due to many patients daily. This paper presents the automated ICD-10 code assigning system developed for solving the problem of analyzing and assigning the ICD-10 code manually by a human expert in the hospitals. The system uses a classification method with a decision tree diagram as the model to classify the ICD-10 codes from patient treatment data, i.e., medicine and laboratory results. The system can be used as a tool to support a medical staff who is the expertise that analyzes and assigns the ICD-10 code in a more accurate and rapid manner. The evaluation of the classification result with the decision tree model is found to be 91.67 percent accurate in performance for the ICD-10 codes assigned.
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An experimental study of wi-fi access service using drones in container yard
(2020-10-13)
Meesriyong, Krongpon
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Wongwirat, Olarn
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Namuduri, Kamesh
Currently, there are some restrictions on employees to access the network in the area of the container yard. This is not only because the operating computers are installed at the positions that are quite remote from the working areas in the container yard but also there is no wireless access network, or Wi-Fi, provided. Installing a fixed tower to transmit a radio signal providing the Wi-Fi access service is also not applicable in the container yard since the layout of placing containers is often changed periodically. Furthermore, the containers are made by metal and often stacked over that causes blocking of the radio signal resulting in a dead zone occurred in several spots in the area. Therefore, the work in this paper presents an experimental study for assessing feasibility to provide the Wi-Fi access service by using drones in the container yard. A business analysis, site survey, and prototype design are performed in the study. Then, the received signal strength, coverage area, and support data rate are defined as parameters to be measured in accordance with the drone altitudes in the experiments. Finally, the results verification and analysis are conducted for affirming the feasibility to use drones for providing the Wi-Fi access service in the container yard in the future.
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A Result Verification of Decision Tree Model for Industrial Wireless Sensors Selection using Analytic Hierarchy Process
(2019-07-01)
Meesawad, Saksiri
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Thanasopon, Bundit
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Wongwirat, Olarn
Nowadays, an industrial wireless sensor (IWS) is interested and widely used in a Gas industry in Thailand. There are many vendors trying to improve quality of IWS products for gaining advantage in competitive market. Therefore, choosing the IWS becomes a challenge for users not only the brand name and price but also several factors needed to be considered, e.g., data rate, output power, operating voltage, transmitting current, receiving current, and operating temperature. Selecting the proper IWS is considered as a multi-objective decision problem that is complicated for an engineer and a project manager. The classification method using a Decision Tree)DT(model can be applied to solve such the problem, but the accuracy is depended on the number of historical data. For IWS in the Gas industry in Thailand, not only the number of training and testing data is limited but also there are only a few brands that are chosen regularly. In this paper, the method of applying the DT model for IWS classification and selection is presented. Then, the classification result of the DT model is verified by using an Analytic Hierarchy Process)AHP(for confirming whether it is accurate based on the limit number of historical data. The verification result can be preliminarily ensured that the DT model can be applied as a decision tool for choosing the appropriate IWS accurately.
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A comparison of decision tree based techniques for indoor positioning system
(2018-04-19)
Chanama, Lummanee
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Wongwirat, Olarn
Currently, an indoor positioning system based on a fingerprint technique for wireless networks under IEEE 802.11 standard uses a method to collect a received signal strengths (RSS) to create a radio map in an offline phase. Then, it detects the RSS in an online phase to compare and find the position. However, while detecting and gathering the RSS, there are some variations of the RSS that affect the accuracy of position estimation. Therefore, there are several methods used to estimate the position in order to improve the accuracy, but the one focused in this paper is a decision tree based classification. The decision tree based classification method is found that it can provide better improvement in accuracy than the others, e.g., K-Nearest Neighbor (K-NN), Bayesian, and Neural networks. However, the techniques used to construct the decision tree are varied depending on the algorithm used to implement. Therefore, this paper is a comparison of decision tree based techniques using typical decision tree (DT) and Gradient boosted tree algorithms for estimating the position indoor. In the study, the RSSs collected from access points in the experimental area are used as the training and testing data. The decision tree models are created by using typical DT and Gradient boosted algorithms based on the training data obtained. There are two factors to consider in the comparative study, i.e., the number of training data and the number of reference radio signals. The testing results from the experiment showed that the decision tree based on Gradient boosted algorithm yielded more accurate results than typical DT, where the amount of 19 reference radio signals and 50 samples of training data gave the best result.

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