Pub Date : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257843
Md. Naimul Islam Suvon, R. Khan, Mehebuba Ferdous
Automatic number plate identification in today's world plays a vital role in vehicle tracking and organization. Our proposed model of automation in the detection and recognizing vehicles through the use of number plate computerization is expected to create a new scope of evolution for large cities. The system can be used for the parking system of motor vehicles, as well as to collect tolls. The detection of the Bangla number plates from different cities and multi-class vehicles is the first step of the proposed system. The number plate detection has been performed with the computer vision approach, and the You Only Look Once v3 (YOLOv3) algorithm. Next, the Tesseract optical character recognition system, in conjunction with the Bangla character recognition model, has been used for vehicle indexing and convolutional neural network for the character recognition from the detected number plate. Numerical results demonstrate that the accuracies of license plate detection for the computer vision and YOLOv3 are 91% and 95%, respectively. For the character recognition, the accuracy for Tesseract and convolutional neural network are 90% if the license plate is detected and cropped successfully and 91.38%, respectively. Finally, our system has been tested using the convolutional neural network method in an environment of real-world where our system's Pi Camera captured video as input, which has a total of 18 different cars. From 18 cars, it has successfully detected 17 cars, which makes our overall system accuracy 88.89%.
车牌自动识别在当今世界的车辆跟踪和组织中起着至关重要的作用。我们提出的自动检测和识别车辆的模型，通过使用车牌电脑化，有望为大城市创造一个新的发展范围。该系统可用于机动车的停车系统，也可用于收取通行费。从不同城市和不同类别的车辆中检测孟加拉车牌是拟议系统的第一步。车牌检测采用计算机视觉方法和You Only Look Once v3 (YOLOv3)算法进行。接下来，将Tesseract光学字符识别系统与孟加拉字符识别模型结合，用于车辆索引，并将卷积神经网络用于从检测到的车牌中识别字符。数值结果表明，计算机视觉和YOLOv3的车牌检测准确率分别为91%和95%。对于字符识别，在车牌检测和裁剪成功的情况下，Tesseract和卷积神经网络的准确率分别为90%和91.38%。最后，我们的系统已经在现实世界的环境中使用卷积神经网络方法进行了测试，我们系统的Pi Camera捕获视频作为输入，总共有18辆不同的汽车。从18辆车中，它成功检测了17辆车，使我们的整体系统准确率达到88.89%。
{"title":"Real Time Bangla Number Plate Recognition using Computer Vision and Convolutional Neural Network","authors":"Md. Naimul Islam Suvon, R. Khan, Mehebuba Ferdous","doi":"10.1109/IICAIET49801.2020.9257843","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257843","url":null,"abstract":"Automatic number plate identification in today's world plays a vital role in vehicle tracking and organization. Our proposed model of automation in the detection and recognizing vehicles through the use of number plate computerization is expected to create a new scope of evolution for large cities. The system can be used for the parking system of motor vehicles, as well as to collect tolls. The detection of the Bangla number plates from different cities and multi-class vehicles is the first step of the proposed system. The number plate detection has been performed with the computer vision approach, and the You Only Look Once v3 (YOLOv3) algorithm. Next, the Tesseract optical character recognition system, in conjunction with the Bangla character recognition model, has been used for vehicle indexing and convolutional neural network for the character recognition from the detected number plate. Numerical results demonstrate that the accuracies of license plate detection for the computer vision and YOLOv3 are 91% and 95%, respectively. For the character recognition, the accuracy for Tesseract and convolutional neural network are 90% if the license plate is detected and cropped successfully and 91.38%, respectively. Finally, our system has been tested using the convolutional neural network method in an environment of real-world where our system's Pi Camera captured video as input, which has a total of 18 different cars. From 18 cars, it has successfully detected 17 cars, which makes our overall system accuracy 88.89%.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125928203","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 : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257840
Ryota Arai, M. Ohsuga
The average human life expectancy has been gradually extended; thus, development of novel approaches to extend healthy life expectancy have gained considerable research attention. Although it is well-known that habitual exercise is an effective way of extending healthy life expectancy, we have found that few people actually continue exercising regularly. Consequently, we have devised a system for promoting exercise using a smartphone and an artificial intelligence (AI) speaker. After confirming that the number of repetitions of an exercise recommended to a user (via the exercise content presented to them) can be detected using smartphone sensors, we integrated the smartphone and AI speaker into one system via the cloud. This system allows the user to receive advice from the AI speaker after exercising. In the future, we will investigate whether the proposed method can significantly encourage changes in exercise-related awareness and behavior compared to existing smartphone apps used for health management.
{"title":"Development of Exercise Promotion System Using Smartphone and AI Speaker","authors":"Ryota Arai, M. Ohsuga","doi":"10.1109/IICAIET49801.2020.9257840","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257840","url":null,"abstract":"The average human life expectancy has been gradually extended; thus, development of novel approaches to extend healthy life expectancy have gained considerable research attention. Although it is well-known that habitual exercise is an effective way of extending healthy life expectancy, we have found that few people actually continue exercising regularly. Consequently, we have devised a system for promoting exercise using a smartphone and an artificial intelligence (AI) speaker. After confirming that the number of repetitions of an exercise recommended to a user (via the exercise content presented to them) can be detected using smartphone sensors, we integrated the smartphone and AI speaker into one system via the cloud. This system allows the user to receive advice from the AI speaker after exercising. In the future, we will investigate whether the proposed method can significantly encourage changes in exercise-related awareness and behavior compared to existing smartphone apps used for health management.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125095506","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 : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257849
M. Limbah, H. Lago, P. Soh, K. Teo
In this work, a conformal antenna designed using polydimethylsiloxane (PDMS) is presented, with copper used as its conducting elements. Such material combination enabled the realization of a highly flexible antenna structure for potential implementation on the human body in microwave imaging systems. This antenna is designed based on a planar monopole aimed at operation in the lower ultra-wideband (UWB) frequencies. The choice of this band is to enable a deeper penetration of radiation into the human body. It operates from 2.92 to 4.75 GHz, with a bandwidth of 1826 MHz and a gain of 2.41 dB in its planar condition. When evaluated in a bent configuration with a radius of 20 mm, 40 mm and 60 mm, a bandwidth of 47.59% (1826 MHz), 45.72% (1741 MHz) and 42.68% (1770 MHz) with a gain of 2.41 dB, 2.73 dB and 3 dB is observed respectively. These results indicate that the antenna can be potentially be used on a human body for microwave imaging purposes.
{"title":"Wideband Conformal Polymeric Based Antenna for Microwave Medical Imaging System","authors":"M. Limbah, H. Lago, P. Soh, K. Teo","doi":"10.1109/IICAIET49801.2020.9257849","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257849","url":null,"abstract":"In this work, a conformal antenna designed using polydimethylsiloxane (PDMS) is presented, with copper used as its conducting elements. Such material combination enabled the realization of a highly flexible antenna structure for potential implementation on the human body in microwave imaging systems. This antenna is designed based on a planar monopole aimed at operation in the lower ultra-wideband (UWB) frequencies. The choice of this band is to enable a deeper penetration of radiation into the human body. It operates from 2.92 to 4.75 GHz, with a bandwidth of 1826 MHz and a gain of 2.41 dB in its planar condition. When evaluated in a bent configuration with a radius of 20 mm, 40 mm and 60 mm, a bandwidth of 47.59% (1826 MHz), 45.72% (1741 MHz) and 42.68% (1770 MHz) with a gain of 2.41 dB, 2.73 dB and 3 dB is observed respectively. These results indicate that the antenna can be potentially be used on a human body for microwave imaging purposes.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125235389","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 : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257819
M. Teow
This paper introduces a formal framework to model the convolutional visual feature learning in a convolutional neural network, which is called compositional subspace representation. The objective is to explain the convolutional visual feature learning computation using a rigid and structural method. The theoretical basis of the proposed framework is, the best way for representation to model a complex learning function is by using a composition of simple two-dimensional piecewise-linear functions to form a multilayers successive cascaded projection function for complex representation. Under the same hypothesis, the proposed framework also explains the hierarchical feature learning representation in a convolutional neural network, the well-acknowledged significant advantage of convolutional neural networks in visual computing. The proposed framework has experimented with image classification using the Fashion-MNIST dataset. Experimental assessments using learning curves analysis, confusion matrix, and visual assessment are presented and discussed. The experimental results were consistent with the theoretical expectation.
{"title":"Experimenting Deep Convolutional Visual Feature Learning using Compositional Subspace Representation and Fashion-MNIST","authors":"M. Teow","doi":"10.1109/IICAIET49801.2020.9257819","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257819","url":null,"abstract":"This paper introduces a formal framework to model the convolutional visual feature learning in a convolutional neural network, which is called compositional subspace representation. The objective is to explain the convolutional visual feature learning computation using a rigid and structural method. The theoretical basis of the proposed framework is, the best way for representation to model a complex learning function is by using a composition of simple two-dimensional piecewise-linear functions to form a multilayers successive cascaded projection function for complex representation. Under the same hypothesis, the proposed framework also explains the hierarchical feature learning representation in a convolutional neural network, the well-acknowledged significant advantage of convolutional neural networks in visual computing. The proposed framework has experimented with image classification using the Fashion-MNIST dataset. Experimental assessments using learning curves analysis, confusion matrix, and visual assessment are presented and discussed. The experimental results were consistent with the theoretical expectation.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114661591","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 : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257842
Adel R. Alharbi, Shwaa D. Alharbi, Amer Aljaedi, Oluwatobi Akanbi
Any popular news website in our modern life, offering details to millions of users every day. Although computer technology continues to grow, the number of disease data is rising. How to structure the document to enable data recognition dynamically has become one of the main challenges for sophisticated web services. Traditional systematic classification of news text requires not only a lot of human and financial assets but it also hardly accomplishes fast classification function. In this work, we introduce a new method relying on both the Latent Dirichlet Allocation and the Neural Networks that are used in the Arabic document classification. Our approach adopts the Vector Space Model to interpret documents in applications for the text classification. In this process, the text is represented as a term vector; n-grams. These methods can not distinguish semantic or textual content; this results in considerable space for features and semantic losses. In this research, the new proposal utilizes a “topics” sampled as text characteristics by the Latent Dirichlet Allocation method. Effectively it eliminates the problems described. We have extracted important themes (topics) of all the texts. Each theme is identified by a different descriptor distribution, and then each text is depicted on the vectors of certain themes. Our experiments indicate that the proposed solution is capable of achieving high efficiency with an accuracy rate of 85.11% for the Arabic text classification task.
{"title":"Neural Networks Based on Latent Dirichlet Allocation For News Web Page Classifications","authors":"Adel R. Alharbi, Shwaa D. Alharbi, Amer Aljaedi, Oluwatobi Akanbi","doi":"10.1109/IICAIET49801.2020.9257842","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257842","url":null,"abstract":"Any popular news website in our modern life, offering details to millions of users every day. Although computer technology continues to grow, the number of disease data is rising. How to structure the document to enable data recognition dynamically has become one of the main challenges for sophisticated web services. Traditional systematic classification of news text requires not only a lot of human and financial assets but it also hardly accomplishes fast classification function. In this work, we introduce a new method relying on both the Latent Dirichlet Allocation and the Neural Networks that are used in the Arabic document classification. Our approach adopts the Vector Space Model to interpret documents in applications for the text classification. In this process, the text is represented as a term vector; n-grams. These methods can not distinguish semantic or textual content; this results in considerable space for features and semantic losses. In this research, the new proposal utilizes a “topics” sampled as text characteristics by the Latent Dirichlet Allocation method. Effectively it eliminates the problems described. We have extracted important themes (topics) of all the texts. Each theme is identified by a different descriptor distribution, and then each text is depicted on the vectors of certain themes. Our experiments indicate that the proposed solution is capable of achieving high efficiency with an accuracy rate of 85.11% for the Arabic text classification task.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129569203","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 : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257823
Marlyn Maseri, Mazlina Mamat
This paper describes the performance analysis of designed speech recognition system whereby the front end method uses MFCCs feature extraction algorithm and defined HMM recognition as the back end. The dataset includes 30 phonemes and 2200 utterances by different speakers. Each speech signal is sampled to 16kHz, 16-bit PCM, and in a mono channel format. The extracted feature of each signal consists of 39 feature vectors which are 12 Mel Cepstrum Coefficients, Log Energy, Delta (first-order derivative) coefficients, and Acceleration coefficients (second-order derivative). The Baum-Welch algorithm is applied for HMM training and the Viterbi algorithms for decoding. The overall system performance accuracy of this experiment is 95.00%.
{"title":"Performance Analysis of Implemented MFCC and HMM-based Speech Recognition System","authors":"Marlyn Maseri, Mazlina Mamat","doi":"10.1109/IICAIET49801.2020.9257823","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257823","url":null,"abstract":"This paper describes the performance analysis of designed speech recognition system whereby the front end method uses MFCCs feature extraction algorithm and defined HMM recognition as the back end. The dataset includes 30 phonemes and 2200 utterances by different speakers. Each speech signal is sampled to 16kHz, 16-bit PCM, and in a mono channel format. The extracted feature of each signal consists of 39 feature vectors which are 12 Mel Cepstrum Coefficients, Log Energy, Delta (first-order derivative) coefficients, and Acceleration coefficients (second-order derivative). The Baum-Welch algorithm is applied for HMM training and the Viterbi algorithms for decoding. The overall system performance accuracy of this experiment is 95.00%.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130053016","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 : 2020-09-26DOI: 10.1109/iicaiet49801.2020.9257855
M. Faizal, Ahmad Fauzi, Abdullah Gani, Yong Jin Park, Shigeru Omatsu, Aslina Baharum, Bablu K. Ghosh, Norbayah Mohd Suki, Yana Mazwin Mohmad Hassim, Y. Leau
{"title":"National and International Advisory Board","authors":"M. Faizal, Ahmad Fauzi, Abdullah Gani, Yong Jin Park, Shigeru Omatsu, Aslina Baharum, Bablu K. Ghosh, Norbayah Mohd Suki, Yana Mazwin Mohmad Hassim, Y. Leau","doi":"10.1109/iicaiet49801.2020.9257855","DOIUrl":"https://doi.org/10.1109/iicaiet49801.2020.9257855","url":null,"abstract":"","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132432916","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 : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257820
Naohiro Nakamoto, Hiroyuki Kobayashi
In recent years, the development of autonomous driving technologies has been very active in the world. However, the shortage of human resources is a serious problem. Therefore, in Japan, the autonomous driving IT human resources strategy has been proposed. Although, the problem of shortage cannot be solved because it is aimed at the excellent top personnel. In order to solve the problem, the authors are developing a platform that contributes to the realization of the problem. In this paper, the authors report on the improvement of ACTT, which is an autonomous driving assistance platform developed in the past study. Previous ACTT had complex configurations. Therefore, the authors improved them and conducted evaluation experiments as a new. In addition, in order to implement automatic driving technologies, ROS package “Navigation Stack” has been implemented in the simulator. In the future, the authors will implement it into current ACTT to complete the platform.
{"title":"Improvement of ACTT that is Autonomous Driving Technologies Support Platform for Education and Research","authors":"Naohiro Nakamoto, Hiroyuki Kobayashi","doi":"10.1109/IICAIET49801.2020.9257820","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257820","url":null,"abstract":"In recent years, the development of autonomous driving technologies has been very active in the world. However, the shortage of human resources is a serious problem. Therefore, in Japan, the autonomous driving IT human resources strategy has been proposed. Although, the problem of shortage cannot be solved because it is aimed at the excellent top personnel. In order to solve the problem, the authors are developing a platform that contributes to the realization of the problem. In this paper, the authors report on the improvement of ACTT, which is an autonomous driving assistance platform developed in the past study. Previous ACTT had complex configurations. Therefore, the authors improved them and conducted evaluation experiments as a new. In addition, in order to implement automatic driving technologies, ROS package “Navigation Stack” has been implemented in the simulator. In the future, the authors will implement it into current ACTT to complete the platform.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132712504","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 : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257832
Wan Ying Chai, M. K. Tan, K. Teo, H. Tham
Thermal runaway of exothermic reaction, especially with batch chemical plants, is a critical issue because it can affect production quantity and quality, causes profit loss, and in worst case can threaten the safety of workers and lead to plant accident. The conventional PID controller which is usually used in many industries needs to be operated under supervision in order to perform the temperature control well. In this work, Mamdani type fuzzy logic controller is proposed to track an optimal reference temperature profile. Both process model and proposed controller are simulated using MATLAB. The results show the designed fuzzy logic controller is well-performed in set point tracking and able to cope with the simulated disturbance condition.
{"title":"Fuzzy Logic Control of Exothermic Batch Process","authors":"Wan Ying Chai, M. K. Tan, K. Teo, H. Tham","doi":"10.1109/IICAIET49801.2020.9257832","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257832","url":null,"abstract":"Thermal runaway of exothermic reaction, especially with batch chemical plants, is a critical issue because it can affect production quantity and quality, causes profit loss, and in worst case can threaten the safety of workers and lead to plant accident. The conventional PID controller which is usually used in many industries needs to be operated under supervision in order to perform the temperature control well. In this work, Mamdani type fuzzy logic controller is proposed to track an optimal reference temperature profile. Both process model and proposed controller are simulated using MATLAB. The results show the designed fuzzy logic controller is well-performed in set point tracking and able to cope with the simulated disturbance condition.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"19 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116340913","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 : 2020-09-26DOI: 10.1109/IICAIET49801.2020.9257871
Vincent Chung, N. Tuah, Kit Guan Lim, M. K. Tan, I. Saad, K. Teo
Energy-efficient optimization algorithm in wireless sensor network (WSN) is often based on solely cluster routing or multi-hop routing. The cluster optimization algorithm will form a cluster network by dividing the sensor nodes into few clusters where each cluster has a cluster head (CH) for data collection. On the other hand, multi-hop optimization algorithm will form a multi-hop network by transmitting data to base station (BS) through data multi-hopping between sensor nodes. However, cluster optimization algorithm suffers from the overburdens of CH nodes, while multi-hop optimization algorithm suffers from the overburdens of nodes which are near to the BS. Therefore, Genetic Algorithm-Cuckoo Search (GACS) is proposed and developed based on the multi-hop clustering model in this paper. GACS optimizes both intra-cluster and inter-cluster communications to enhance energy efficiency in WSN, extending the network lifetime. Based on the performance evaluation, GACS outperforms both Genetic Algorithm (GA)-based cluster optimization algorithm and Cuckoo Search (CS)-based multi-hop optimization algorithm.
{"title":"Metaheuristic Multi-Hop Clustering Optimization for Energy-Efficient Wireless Sensor Network","authors":"Vincent Chung, N. Tuah, Kit Guan Lim, M. K. Tan, I. Saad, K. Teo","doi":"10.1109/IICAIET49801.2020.9257871","DOIUrl":"https://doi.org/10.1109/IICAIET49801.2020.9257871","url":null,"abstract":"Energy-efficient optimization algorithm in wireless sensor network (WSN) is often based on solely cluster routing or multi-hop routing. The cluster optimization algorithm will form a cluster network by dividing the sensor nodes into few clusters where each cluster has a cluster head (CH) for data collection. On the other hand, multi-hop optimization algorithm will form a multi-hop network by transmitting data to base station (BS) through data multi-hopping between sensor nodes. However, cluster optimization algorithm suffers from the overburdens of CH nodes, while multi-hop optimization algorithm suffers from the overburdens of nodes which are near to the BS. Therefore, Genetic Algorithm-Cuckoo Search (GACS) is proposed and developed based on the multi-hop clustering model in this paper. GACS optimizes both intra-cluster and inter-cluster communications to enhance energy efficiency in WSN, extending the network lifetime. Based on the performance evaluation, GACS outperforms both Genetic Algorithm (GA)-based cluster optimization algorithm and Cuckoo Search (CS)-based multi-hop optimization algorithm.","PeriodicalId":300885,"journal":{"name":"2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126150009","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}
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