Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975874
Grant Eldrick M. Uy, Alan Carlisle Y. Choachuy, Raymark C. Parocha, R. A. Peña, E. Q. B. Macabebe
As companies continue to expand, more people have to be managed within a workplace. Along with this comes energy consumption that is increasingly becoming difficult to manage. To solve these issues, this study aims to create a system for employee monitoring and energy optimization. The researchers propose a system that can gather information from occupants and automate smart devices depending on their location. This research primarily seeks to serve as a foundation in creating a flexible, energy-efficient, and scalable system that can be innovated easily. The resulting output is expected to control Internet of Things (IoT) devices based on occupancy data obtained from Bluetooth localization. A system using Bluetooth to retrieve occupancy data was integrated with OpenHAB as the platform to connect and automate IoT devices. The proposed architecture of the system includes Bluetooth localization, IoT automation, and the system interface. This implementation used Bluetooth Low Energy (BLE) beacons sending location data to an ESP32 mesh network, increasing the scalability. The Raspberry Pi server parsed and analyzed the data using Node-RED. The data was then fed to OpenHAB to connect to the IoT devices. After the system was developed, three measurements were used to assess the system, namely location difference, response time, and status accuracy. This study proved the potential and validity of the integration of a Bluetooth positioning system with IoT automation.
{"title":"System Design for Automating Smart Internet of Things Devices Using Bluetooth Localization","authors":"Grant Eldrick M. Uy, Alan Carlisle Y. Choachuy, Raymark C. Parocha, R. A. Peña, E. Q. B. Macabebe","doi":"10.1109/IoTaIS56727.2022.9975874","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975874","url":null,"abstract":"As companies continue to expand, more people have to be managed within a workplace. Along with this comes energy consumption that is increasingly becoming difficult to manage. To solve these issues, this study aims to create a system for employee monitoring and energy optimization. The researchers propose a system that can gather information from occupants and automate smart devices depending on their location. This research primarily seeks to serve as a foundation in creating a flexible, energy-efficient, and scalable system that can be innovated easily. The resulting output is expected to control Internet of Things (IoT) devices based on occupancy data obtained from Bluetooth localization. A system using Bluetooth to retrieve occupancy data was integrated with OpenHAB as the platform to connect and automate IoT devices. The proposed architecture of the system includes Bluetooth localization, IoT automation, and the system interface. This implementation used Bluetooth Low Energy (BLE) beacons sending location data to an ESP32 mesh network, increasing the scalability. The Raspberry Pi server parsed and analyzed the data using Node-RED. The data was then fed to OpenHAB to connect to the IoT devices. After the system was developed, three measurements were used to assess the system, namely location difference, response time, and status accuracy. This study proved the potential and validity of the integration of a Bluetooth positioning system with IoT automation.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116761244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975998
Meutia Gina Salsabila, M. A. Murti, A. Z. Fuadi
kWh meter is a tool to measure the use of electrical energy. This tool is widely used at home and in industry. Most kWh meters can only display the amount of electricity used from the display on the kWh meter. This causes power users to be unable to view or monitor electricity usage remotely. This Internet of Things (IoT) based kWh meter communication design allows all data from the kWh meter to be sent to the gateway and forwarded to the IoT cloud. LoRa (Long Range) communication will be used in this research. The kWh meter that has been added with IoT technology is expected to make it easier for users to monitor the electricity consumption data from anywhere. The results of the tests in this final project, the device is able to read the data on the amount of electricity from the kWh meter. The LoRa communication module can send the data taken from the kWh meter to the gateway to be displayed in Antares. The data transmission results have an average SNR 9.81 dB, RSSI −78.14 dBm, delay 3.546 seconds, and packet loss 1.11%.
{"title":"Design of 3 Phase kWh Meter Communication Based on Internet of Things (IoT) Using LoRa","authors":"Meutia Gina Salsabila, M. A. Murti, A. Z. Fuadi","doi":"10.1109/IoTaIS56727.2022.9975998","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975998","url":null,"abstract":"kWh meter is a tool to measure the use of electrical energy. This tool is widely used at home and in industry. Most kWh meters can only display the amount of electricity used from the display on the kWh meter. This causes power users to be unable to view or monitor electricity usage remotely. This Internet of Things (IoT) based kWh meter communication design allows all data from the kWh meter to be sent to the gateway and forwarded to the IoT cloud. LoRa (Long Range) communication will be used in this research. The kWh meter that has been added with IoT technology is expected to make it easier for users to monitor the electricity consumption data from anywhere. The results of the tests in this final project, the device is able to read the data on the amount of electricity from the kWh meter. The LoRa communication module can send the data taken from the kWh meter to the gateway to be displayed in Antares. The data transmission results have an average SNR 9.81 dB, RSSI −78.14 dBm, delay 3.546 seconds, and packet loss 1.11%.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129349425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9976018
Titus Yory Datubakka, Istikmal, A. Irawan
Fire is one of the disasters that often occur in Indonesia. One of the consequences of fires that occur in Indonesia is forest fires. In 2014 and 2015 alone, 2.6 million ha of forest fires were reported in Indonesia. One way to detect a fire source is by developing machine learning that is used for information processing in the event of a fire by utilizing patterns or information from large data sets. This research will develop an algorithm to detect fires by comparing the accuracy of the two algorithms, that is K-Nearest Neighbor (K-NN) and Naive Bayes. The dataset was obtained from a fire simulation using NodeMCU ESP8266 and IR Flame Sensor, MQ7, and DHT 11. Based on the composition of the training and test data, this research found the best algorithm is K-Nearest Neighbor tuning using GridSearch CV, where the best metric parameters are ‘Minkowski’, K = 1, p = 1, and weights ‘Uniform’, with a composition of 75% training data and 25% test data with accuracy 96.44%, precision 96.48%, recall 96.44%, and F1-Score is 96.33%.
火灾是印尼经常发生的灾害之一。印度尼西亚发生火灾的后果之一是森林火灾。仅在2014年和2015年，印度尼西亚就报告了260万公顷的森林火灾。检测火源的一种方法是通过开发机器学习，通过利用来自大型数据集的模式或信息，在火灾事件中用于信息处理。本研究将通过比较两种算法的准确性，开发一种检测火灾的算法，即k -最近邻(K-NN)和朴素贝叶斯。数据集来自使用NodeMCU ESP8266和IR火焰传感器、MQ7和DHT 11进行的火灾模拟。基于训练数据和测试数据的组合，本研究发现最佳算法是使用GridSearch CV进行K-最近邻调优，其中最佳度量参数为“Minkowski”，K = 1, p = 1，权值为“Uniform”，由75%的训练数据和25%的测试数据组成，准确率为96.44%，精度为96.48%，召回率为96.44%，F1-Score为96.33%。
{"title":"Comparison Analysis Of K-Nearest Neighbor (K-Nn) Algorithm With Naive Bayes For Fire Source Detection Mitigation","authors":"Titus Yory Datubakka, Istikmal, A. Irawan","doi":"10.1109/IoTaIS56727.2022.9976018","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9976018","url":null,"abstract":"Fire is one of the disasters that often occur in Indonesia. One of the consequences of fires that occur in Indonesia is forest fires. In 2014 and 2015 alone, 2.6 million ha of forest fires were reported in Indonesia. One way to detect a fire source is by developing machine learning that is used for information processing in the event of a fire by utilizing patterns or information from large data sets. This research will develop an algorithm to detect fires by comparing the accuracy of the two algorithms, that is K-Nearest Neighbor (K-NN) and Naive Bayes. The dataset was obtained from a fire simulation using NodeMCU ESP8266 and IR Flame Sensor, MQ7, and DHT 11. Based on the composition of the training and test data, this research found the best algorithm is K-Nearest Neighbor tuning using GridSearch CV, where the best metric parameters are ‘Minkowski’, K = 1, p = 1, and weights ‘Uniform’, with a composition of 75% training data and 25% test data with accuracy 96.44%, precision 96.48%, recall 96.44%, and F1-Score is 96.33%.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126794503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975919
R. Seeliger, Christoph Müller, S. Arbanowski
With the growing usage of high quality HD and ultra HD video content, adaptive bitrate streaming and constantly increasing demand for bitrates and distribution bandwidth, energy consumption and related costs grow exponentially in parallel. As such, it is vital to reduce the overall energy consumption of online video streaming. In this paper we aim to investigate, which parameters influence energy consumption for video streaming, on the client (device) side, as well as during encoding. To conduct this systematic investigation, we have set up a reproducible measurement environment that closely resembles real-world conditions, with different client devices, and video encoding workflows, each connected to energy measurement devices. In an advanced step, we additionally examine the effect of content aware encoding methods on power consumption, using an AI-based per-scene encoding solution. Finally, we discuss and evaluate the measurements and offer recommendations to reduce overall CO2 emissions for video streaming.
{"title":"Green streaming through utilization of AI-based content aware encoding","authors":"R. Seeliger, Christoph Müller, S. Arbanowski","doi":"10.1109/IoTaIS56727.2022.9975919","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975919","url":null,"abstract":"With the growing usage of high quality HD and ultra HD video content, adaptive bitrate streaming and constantly increasing demand for bitrates and distribution bandwidth, energy consumption and related costs grow exponentially in parallel. As such, it is vital to reduce the overall energy consumption of online video streaming. In this paper we aim to investigate, which parameters influence energy consumption for video streaming, on the client (device) side, as well as during encoding. To conduct this systematic investigation, we have set up a reproducible measurement environment that closely resembles real-world conditions, with different client devices, and video encoding workflows, each connected to energy measurement devices. In an advanced step, we additionally examine the effect of content aware encoding methods on power consumption, using an AI-based per-scene encoding solution. Finally, we discuss and evaluate the measurements and offer recommendations to reduce overall CO2 emissions for video streaming.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121215600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975901
Masduki Khamdan Muchamad, Z. Fuadi, N. Nasaruddin
The demand for smart home technology is increasing to help older people feel more comfortable at home. Smart home technology can support the elderly in independent daily activities. The Internet of Things (IoT) is currently one of the key platforms for data-driven smart homes. The automated process of recognizing or verifying an individual’s identification based on his speech is known as voice recognition or speaker recognition. The main challenge in adjusting to the evolution of conversations in society is that the systems generally refer to existing patterns in the database. Therefore, we propose a prototype design of a smart home’s deep learning-based voice control model. First, we develop the model based on the convolutional neural network (CNN) and deep neural network (DNN) to obtain the best accuracy. Then, we create a model-based CNN and DNN used to construct a voice recognition system independent of text and language. The simulation result shows that the proposed model could extract the voice sample. The result also indicates that the accuracy of using CNN is better than that of using DNN.
{"title":"Prototype Design of Deep Learning-based Voice Control Model for Smart Home","authors":"Masduki Khamdan Muchamad, Z. Fuadi, N. Nasaruddin","doi":"10.1109/IoTaIS56727.2022.9975901","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975901","url":null,"abstract":"The demand for smart home technology is increasing to help older people feel more comfortable at home. Smart home technology can support the elderly in independent daily activities. The Internet of Things (IoT) is currently one of the key platforms for data-driven smart homes. The automated process of recognizing or verifying an individual’s identification based on his speech is known as voice recognition or speaker recognition. The main challenge in adjusting to the evolution of conversations in society is that the systems generally refer to existing patterns in the database. Therefore, we propose a prototype design of a smart home’s deep learning-based voice control model. First, we develop the model based on the convolutional neural network (CNN) and deep neural network (DNN) to obtain the best accuracy. Then, we create a model-based CNN and DNN used to construct a voice recognition system independent of text and language. The simulation result shows that the proposed model could extract the voice sample. The result also indicates that the accuracy of using CNN is better than that of using DNN.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133414903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9976007
K. Letaief, Z. Fadlullah, M. Fouda
While researchers continue to incorporate intelligent algorithms in Fifth Generation (5G) and beyond networks to achieve high-accuracy decisions with ultra-low latency and significantly high throughput, the issue of privacy-preservation became a critical research area. This is because mobile service providers not only need to satisfy the Quality of Service (QoS) of users in terms of ultra-fast user connectivity but also ensure reliable, automated solutions that will enable them to design a vast multi-tenant system on the same physical infrastructure while preserving the user privacy. With the adoption of data-driven machine learning models for providing smart network slicing in 5G and beyond networks and Internet of Things (IoT) systems, the issue of privacy-preservation integration is yet to be considered. We address this issue in this paper, and design an asynchronously weight updating federated learning framework that is efficient, reliable, and preserves the privacy as well as achieve the required low latency and low network overhead. Thus, our proposal permits a reasonably accurate decision for the resource allocation for different 5G users without violating their privacy or introducing additional load to the network. Experimental results demonstrate the efficiency of the asynchronously weight updating federated learning in contrast with the conventional FedAvg (Federated averaging) strategy and the traditional centralized learning model. In particular, our proposed technique achieves network overhead reduction with a consistent and significantly high prediction accuracy, that validates its low-latency and efficiency advantages.
{"title":"Efficient Wireless Network Slicing in 5G Networks: An Asynchronous Federated Learning Approach","authors":"K. Letaief, Z. Fadlullah, M. Fouda","doi":"10.1109/IoTaIS56727.2022.9976007","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9976007","url":null,"abstract":"While researchers continue to incorporate intelligent algorithms in Fifth Generation (5G) and beyond networks to achieve high-accuracy decisions with ultra-low latency and significantly high throughput, the issue of privacy-preservation became a critical research area. This is because mobile service providers not only need to satisfy the Quality of Service (QoS) of users in terms of ultra-fast user connectivity but also ensure reliable, automated solutions that will enable them to design a vast multi-tenant system on the same physical infrastructure while preserving the user privacy. With the adoption of data-driven machine learning models for providing smart network slicing in 5G and beyond networks and Internet of Things (IoT) systems, the issue of privacy-preservation integration is yet to be considered. We address this issue in this paper, and design an asynchronously weight updating federated learning framework that is efficient, reliable, and preserves the privacy as well as achieve the required low latency and low network overhead. Thus, our proposal permits a reasonably accurate decision for the resource allocation for different 5G users without violating their privacy or introducing additional load to the network. Experimental results demonstrate the efficiency of the asynchronously weight updating federated learning in contrast with the conventional FedAvg (Federated averaging) strategy and the traditional centralized learning model. In particular, our proposed technique achieves network overhead reduction with a consistent and significantly high prediction accuracy, that validates its low-latency and efficiency advantages.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132578857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9976005
Shuhai Li, Yuqi Liu
With the rapid development of artificial intelligence technology and the acceleration of the global population aging process, the application demand of service robots in the field of elderly care is growing. As the world’s most populous country, China leads the world’s aging process and has a large base of elderly population. Service robots have broad prospects in dealing with the chanllenges and problems of China’s aging society in the future. At the same time, 90% of China’s elderly people prefer to live at home. Based on this fact, this paper explores and studies the application and Prospect of smart service robots in helping the Chinese elderly aging in place, and the preference of the Chinese elderly for artificial services and robot services on different needs and tasks. Firstly, according to Maslow’s Hierarchy Model, the elderly service robots are divided into five categories, which are Life support, Healthcare & Nursing, Social Interaction & Entertainment, Safety & Security Management and Self-realization; Secondly, based on the case analysis of elderly service robots, the specific functions of each type of robot are defined, and the robot service tasks under different functions are summarized; Finally, according to different task needs, the article conducted a questionnaire survey on whether the future elderly in China prefer robot services or manual services related to certain tasks and demand, and clarified their needs and preferences with a view to promoting the effective application and development of service robots for elderly care in China.
{"title":"How Can Smart Service Robot Help the Elderly Aging in Place: Application, Prospect and Preference","authors":"Shuhai Li, Yuqi Liu","doi":"10.1109/IoTaIS56727.2022.9976005","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9976005","url":null,"abstract":"With the rapid development of artificial intelligence technology and the acceleration of the global population aging process, the application demand of service robots in the field of elderly care is growing. As the world’s most populous country, China leads the world’s aging process and has a large base of elderly population. Service robots have broad prospects in dealing with the chanllenges and problems of China’s aging society in the future. At the same time, 90% of China’s elderly people prefer to live at home. Based on this fact, this paper explores and studies the application and Prospect of smart service robots in helping the Chinese elderly aging in place, and the preference of the Chinese elderly for artificial services and robot services on different needs and tasks. Firstly, according to Maslow’s Hierarchy Model, the elderly service robots are divided into five categories, which are Life support, Healthcare & Nursing, Social Interaction & Entertainment, Safety & Security Management and Self-realization; Secondly, based on the case analysis of elderly service robots, the specific functions of each type of robot are defined, and the robot service tasks under different functions are summarized; Finally, according to different task needs, the article conducted a questionnaire survey on whether the future elderly in China prefer robot services or manual services related to certain tasks and demand, and clarified their needs and preferences with a view to promoting the effective application and development of service robots for elderly care in China.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131694637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975898
Aly Ilyas, P. Wellyantama, S. Soekirno, Maulana Putra, Dyah Prihartini Djenal, A. M. Hidayat
Indonesia is currently focusing on its big goal to become The World’s Maritime Axis. For this reason, several sectors such as the infrastructure of the port, the development of the fishing, and tourism industry should be improved. The use of accurate tides level data is indispensable to support these developments. However, the number of instruments to observe tides data is limited compared to the covered area since Indonesia has the third longest coastline in the world. Recently, the frequent use of Artificial Intelligence (AI) has also offered an alternative solution to provide prediction data, including tides level data. Thereby, Artificial Neural Networks (ANN) as the subfield of AI is then chosen to make a prediction of tides level data. The type of ANN used in this study is two-layer Feed Forward Neural Network (FFNN). The previous observed tides data using atmospheric data (temperature and pressure) and moon position as the features are used to train the network. In order to evaluate the performance of ANN model, the result of the prediction is then compared to the observed tides level data using Automatic Weather Station (AWS). The result shows that the predicted tide level data has a strong correlation with the observed data with coefficient correlation of 0.9238. Furthermore, Root Mean Square Error (RMSE) as the statistics parameters to evaluate the performance of ANN model is found to be low around 0.077 meters. This preliminary result suggests that the FFNN has a good performance in predicting tides level data and therefore can be applied to provide tides level data on a larger scale in Indonesia.
{"title":"The Implementation of Artificial Neural Network (ANN) in the Prediction of Tides Level Data in Indonesia","authors":"Aly Ilyas, P. Wellyantama, S. Soekirno, Maulana Putra, Dyah Prihartini Djenal, A. M. Hidayat","doi":"10.1109/IoTaIS56727.2022.9975898","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975898","url":null,"abstract":"Indonesia is currently focusing on its big goal to become The World’s Maritime Axis. For this reason, several sectors such as the infrastructure of the port, the development of the fishing, and tourism industry should be improved. The use of accurate tides level data is indispensable to support these developments. However, the number of instruments to observe tides data is limited compared to the covered area since Indonesia has the third longest coastline in the world. Recently, the frequent use of Artificial Intelligence (AI) has also offered an alternative solution to provide prediction data, including tides level data. Thereby, Artificial Neural Networks (ANN) as the subfield of AI is then chosen to make a prediction of tides level data. The type of ANN used in this study is two-layer Feed Forward Neural Network (FFNN). The previous observed tides data using atmospheric data (temperature and pressure) and moon position as the features are used to train the network. In order to evaluate the performance of ANN model, the result of the prediction is then compared to the observed tides level data using Automatic Weather Station (AWS). The result shows that the predicted tide level data has a strong correlation with the observed data with coefficient correlation of 0.9238. Furthermore, Root Mean Square Error (RMSE) as the statistics parameters to evaluate the performance of ANN model is found to be low around 0.077 meters. This preliminary result suggests that the FFNN has a good performance in predicting tides level data and therefore can be applied to provide tides level data on a larger scale in Indonesia.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124358111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975870
Chenn-Jung Huang, Kai-Wen Hu, Cheng-Yang Hsieh
Urbanization is an inevitable trend in the future. By 2050, more than two-thirds of the world’s population is expected to live in metropolitan areas. However, traffic congestion caused by the rapid growth of vehicle traffic is getting deteriorated in metropolitan areas. Although governments of many countries have proposed various traffic management schemes to alleviate traffic congestion in urban areas during peak hours, the large volume of traffic during peak hours caused by the dramatic increase in private-owned vehicle continues to cause significant economic losses to the public and affect the future development of metropolitan areas. Meanwhile, it is well known that that the world’s vehicle manufacturers focus on the development of self-driving electric vehicles (EVs). As a result, the combination of self-driving EVs and widespread ride-sharing services will not only reduce the frequency of human traffic accidents, but will also alleviate the current traffic congestion. Although route selection and charging of conventional EVs have been extensively explored in the literature, the operational characteristics of ride-sharing services need to be investigated in the context of route selection and charging of shared self-driving electric vehicle fleets. In addition, the recent literature has focused on the mixed flow of manual vehicles and self-driving vehicles, but little attention has been paid to the future traffic management issues of the coexistence of private EVs and shared self-driving fleets. In view of this, this work considers the mixed traffic conditions of private EVs and shared self-driving fleets and proposes an integrated solution for shared self-driving EV fleet ride-sharing regulation and mixed traffic congestion prevention. The experimental results revealed that the solutions proposed in this work can not only be used by shared-vehicle operators for their fleet ride-sharing strategies, but will also be used by traffic management organizations of each country as a reference for future urban traffic management policies in light of the future mixed traffic conditions where private EVs and shared-vehicle fleets coexist.
{"title":"Congestion-Avoiding Routing and Charging Scheduling Mechanism for Shared Autonomous Electric Vehicle Networks in Urban Areas","authors":"Chenn-Jung Huang, Kai-Wen Hu, Cheng-Yang Hsieh","doi":"10.1109/IoTaIS56727.2022.9975870","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975870","url":null,"abstract":"Urbanization is an inevitable trend in the future. By 2050, more than two-thirds of the world’s population is expected to live in metropolitan areas. However, traffic congestion caused by the rapid growth of vehicle traffic is getting deteriorated in metropolitan areas. Although governments of many countries have proposed various traffic management schemes to alleviate traffic congestion in urban areas during peak hours, the large volume of traffic during peak hours caused by the dramatic increase in private-owned vehicle continues to cause significant economic losses to the public and affect the future development of metropolitan areas. Meanwhile, it is well known that that the world’s vehicle manufacturers focus on the development of self-driving electric vehicles (EVs). As a result, the combination of self-driving EVs and widespread ride-sharing services will not only reduce the frequency of human traffic accidents, but will also alleviate the current traffic congestion. Although route selection and charging of conventional EVs have been extensively explored in the literature, the operational characteristics of ride-sharing services need to be investigated in the context of route selection and charging of shared self-driving electric vehicle fleets. In addition, the recent literature has focused on the mixed flow of manual vehicles and self-driving vehicles, but little attention has been paid to the future traffic management issues of the coexistence of private EVs and shared self-driving fleets. In view of this, this work considers the mixed traffic conditions of private EVs and shared self-driving fleets and proposes an integrated solution for shared self-driving EV fleet ride-sharing regulation and mixed traffic congestion prevention. The experimental results revealed that the solutions proposed in this work can not only be used by shared-vehicle operators for their fleet ride-sharing strategies, but will also be used by traffic management organizations of each country as a reference for future urban traffic management policies in light of the future mixed traffic conditions where private EVs and shared-vehicle fleets coexist.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116944353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975969
S. Lo, H. Chou
The development of tools and materials for those people who are visually impaired is a crucial topic in the research area of assistive technologies. In mandarin learning, most printing materials for K-4 children have phonetic symbols to assist students in learning pronunciation. One challenge in the research area is recognizing the mandarin phrases in those printing materials and transforming them into audiobooks or Braille. To our knowledge, this is the first study examining the Optical Character Recognition (OCR) performance toward phonetic symbols in mandarin. In this study, we conduct experiments on recognizing images with mandarin phrases with phonetic symbols by the side using the OCR system. We propose candidate methods to improve recognition efficiency in the future based on preliminary results.
{"title":"Evaluating and Improving Optical Character Recognition (OCR) Efficiency in Recognizing Mandarin Phrases with Phonetic Symbols","authors":"S. Lo, H. Chou","doi":"10.1109/IoTaIS56727.2022.9975969","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975969","url":null,"abstract":"The development of tools and materials for those people who are visually impaired is a crucial topic in the research area of assistive technologies. In mandarin learning, most printing materials for K-4 children have phonetic symbols to assist students in learning pronunciation. One challenge in the research area is recognizing the mandarin phrases in those printing materials and transforming them into audiobooks or Braille. To our knowledge, this is the first study examining the Optical Character Recognition (OCR) performance toward phonetic symbols in mandarin. In this study, we conduct experiments on recognizing images with mandarin phrases with phonetic symbols by the side using the OCR system. We propose candidate methods to improve recognition efficiency in the future based on preliminary results.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117028975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：