Pub Date : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058276
Radhika Tayal, A. Shankar
Now these days, many tools have been developed by the researchers to analyze the impact of diabetes disease on common people within a definite period. However, all these tools have predicted the results based on the labeled dataset or smaller dataset. But in a recent environment, we have collected a large amount of data using both online and offline media. Consequently, data are generated from heterogeneous sources, are in unstructured form and voluminous, etc. As a result, it is not possible to use huge data by using traditional prediction algorithms because they work only on the structured dataset. In this paper, we have used the semi-supervised learning approach that works on a partially labeled dataset for predicting diabetes disease. The partial dataset is the combination of a labeled and unlabelled dataset. For prediction, we have considered 80% unlabelled datasets and 20% labeled datasets. We developed a user based interface for the user to build their prediction model using labeled and unlabeled datasets and analyze the data according to their requirements and interest. Our main objective is to develop a diabetes prediction system that can be used by the researcher and the common people using with minimal labelled datasets.
{"title":"Learning And Predicting Diabetes Data Sets Using Semi-Supervised Learning","authors":"Radhika Tayal, A. Shankar","doi":"10.1109/Confluence47617.2020.9058276","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058276","url":null,"abstract":"Now these days, many tools have been developed by the researchers to analyze the impact of diabetes disease on common people within a definite period. However, all these tools have predicted the results based on the labeled dataset or smaller dataset. But in a recent environment, we have collected a large amount of data using both online and offline media. Consequently, data are generated from heterogeneous sources, are in unstructured form and voluminous, etc. As a result, it is not possible to use huge data by using traditional prediction algorithms because they work only on the structured dataset. In this paper, we have used the semi-supervised learning approach that works on a partially labeled dataset for predicting diabetes disease. The partial dataset is the combination of a labeled and unlabelled dataset. For prediction, we have considered 80% unlabelled datasets and 20% labeled datasets. We developed a user based interface for the user to build their prediction model using labeled and unlabeled datasets and analyze the data according to their requirements and interest. Our main objective is to develop a diabetes prediction system that can be used by the researcher and the common people using with minimal labelled datasets.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130908586","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058144
Himanshu Didden, Yashvi Sikka, Misha Kakkar
This paper is about a positioning system known as Wi-Fi Positioning System or Wireless Positioning System (WPS). As the name suggests the wireless positioning system is developed on the concept of triangulation that uses the nearby Wi-Fi access point to detect the position. These access points can cover a large area when planted strategically in such a way that every position in the area could access at least three access points. The RSSI triangulation formula can then be used to calculate distance between the smartphone and the access points. This distance is known as received signal strength indication (RSSI) number. This number is then stored along with physical location name in the database to act as fingerprint of the location. To detect the indoor position, the smart phone location is compared with the fingerprints stored in the database. The physical name corresponding to the matched fingerprint is then displayed as the name of the physical location. In the study an android application is developed that uses WPS technology to work as an indoor positioning system.
{"title":"Android Based Wireless Positioning System","authors":"Himanshu Didden, Yashvi Sikka, Misha Kakkar","doi":"10.1109/Confluence47617.2020.9058144","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058144","url":null,"abstract":"This paper is about a positioning system known as Wi-Fi Positioning System or Wireless Positioning System (WPS). As the name suggests the wireless positioning system is developed on the concept of triangulation that uses the nearby Wi-Fi access point to detect the position. These access points can cover a large area when planted strategically in such a way that every position in the area could access at least three access points. The RSSI triangulation formula can then be used to calculate distance between the smartphone and the access points. This distance is known as received signal strength indication (RSSI) number. This number is then stored along with physical location name in the database to act as fingerprint of the location. To detect the indoor position, the smart phone location is compared with the fingerprints stored in the database. The physical name corresponding to the matched fingerprint is then displayed as the name of the physical location. In the study an android application is developed that uses WPS technology to work as an indoor positioning system.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131982002","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058128
Shailendra Narayan Singh, Twinkle Sarraf
After many sentiment analysis as well as many types of methods classify the reviews that is based on test data and reviewer’s ratings which uses training., after reading reviews it is seen that star rating of reviewer do not always give a precise measure of his sentiment. This paper primarily focuses on analyzing customer reviews from the e-commerce space. Upon surveying popular e-commerce websites it can be observed that in several instances the product rating given by a customer is not consistent with the product review written by him/her. The problem is made complex by the fact that there is no standard scale to measure the rating that the user gives and the rating of the product are instinctive to the customers’ view. In several cases it is seen that a product is rated 4 out of 5. However, the reviews detail that the customer’s experience with the product is not favourable. Indeed, text reviews are a true picture of the product. To get rid of this problem, the stated system will give a boolean result i.e. whether the product is good or bad and the user does not need to read all the reviews to analyze the product.
{"title":"Sentiment Analysis of a Product based on User Reviews using Random Forests Algorithm","authors":"Shailendra Narayan Singh, Twinkle Sarraf","doi":"10.1109/Confluence47617.2020.9058128","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058128","url":null,"abstract":"After many sentiment analysis as well as many types of methods classify the reviews that is based on test data and reviewer’s ratings which uses training., after reading reviews it is seen that star rating of reviewer do not always give a precise measure of his sentiment. This paper primarily focuses on analyzing customer reviews from the e-commerce space. Upon surveying popular e-commerce websites it can be observed that in several instances the product rating given by a customer is not consistent with the product review written by him/her. The problem is made complex by the fact that there is no standard scale to measure the rating that the user gives and the rating of the product are instinctive to the customers’ view. In several cases it is seen that a product is rated 4 out of 5. However, the reviews detail that the customer’s experience with the product is not favourable. Indeed, text reviews are a true picture of the product. To get rid of this problem, the stated system will give a boolean result i.e. whether the product is good or bad and the user does not need to read all the reviews to analyze the product.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131584903","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058135
Varun Malik, Taruna Sharma, Manish Sharma
In this research article, a square monopole multiband antenna is designed for applications including Wireless Wide Area Network which includes Digital Cellular System (1.71GHz-1.88GHz) and Personal Communication System (1.85GHz-1.99GHz), Bluetooth (2.402GHz-2.480GHz) and World Wide Interoperability for Microwave Access (3.30GHz-3.80GHz). These above said operating wireless technologies are obtained by using 2 L-Shaped stubs embedded with patch and etched L-shaped slot on radiating patch. Lengths of the stubs are optimized by using simulators and algorithm used by artificial intelligence (Radial Basis Model) Antenna results are simulated on two different EM simulators to validate and offers gain of 3.86, 4.42 and 4.18dBi respectively in operating bands.
{"title":"A Multiband (WWAN/Bluetooth/WiMAX) Square Monopole Antenna with Simple Structure for Wireless Communication System Applications And Optimization by using Artificial Intelligence","authors":"Varun Malik, Taruna Sharma, Manish Sharma","doi":"10.1109/Confluence47617.2020.9058135","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058135","url":null,"abstract":"In this research article, a square monopole multiband antenna is designed for applications including Wireless Wide Area Network which includes Digital Cellular System (1.71GHz-1.88GHz) and Personal Communication System (1.85GHz-1.99GHz), Bluetooth (2.402GHz-2.480GHz) and World Wide Interoperability for Microwave Access (3.30GHz-3.80GHz). These above said operating wireless technologies are obtained by using 2 L-Shaped stubs embedded with patch and etched L-shaped slot on radiating patch. Lengths of the stubs are optimized by using simulators and algorithm used by artificial intelligence (Radial Basis Model) Antenna results are simulated on two different EM simulators to validate and offers gain of 3.86, 4.42 and 4.18dBi respectively in operating bands.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115070904","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057972
P. S. Das, Harsh Chhabra, S. Dubey
Eradicating poverty is the numero uno objective of the United Nations for sustainable development of the world by 2030. But, in order to develop a feasible, targeted solution to this problem, an exact poverty map is required. In India, especially in rural areas, there is a dearth of reliable and frequent data related to indicators of poverty line as the national statistics division of the country releases data only once in five years. In this paper, we look at an alternative to the slow, ineffective collection of data on ground: mapping poverty from outer space using medium and high-resolution satellite imagery. Using both satellite imagery and survey data for the rural areas of India, we review how machine learning tools like convolutional neural networks have been harnessed to efficiently identify image features that help us effectively predict socio-economic indicators of poverty. We also explore how these methods offer promising means for policy makers to tackle poverty at the grassroot level and a potential for application across several domains of science.
{"title":"Socio Economic Analysis of India with High Resolution Satellite Imagery to Predict Poverty","authors":"P. S. Das, Harsh Chhabra, S. Dubey","doi":"10.1109/Confluence47617.2020.9057972","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057972","url":null,"abstract":"Eradicating poverty is the numero uno objective of the United Nations for sustainable development of the world by 2030. But, in order to develop a feasible, targeted solution to this problem, an exact poverty map is required. In India, especially in rural areas, there is a dearth of reliable and frequent data related to indicators of poverty line as the national statistics division of the country releases data only once in five years. In this paper, we look at an alternative to the slow, ineffective collection of data on ground: mapping poverty from outer space using medium and high-resolution satellite imagery. Using both satellite imagery and survey data for the rural areas of India, we review how machine learning tools like convolutional neural networks have been harnessed to efficiently identify image features that help us effectively predict socio-economic indicators of poverty. We also explore how these methods offer promising means for policy makers to tackle poverty at the grassroot level and a potential for application across several domains of science.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120891450","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057821
Gordon Morrison, Jean-Paul Van Belle
Fully Autonomous Vehicles (AVs), or self-driving vehicles, are expected to enter the automobile market in the coming years. This technology is expected to provide society with a range of benefits, from increased mobility for the elderly and adolescents, to decreasing carbon emissions and improving traffic flow. These benefits, however, will not be achieved unless consumers are willing to accept the technology into their lives and daily routine. In acknowledging this potential barrier to AV proliferation, this study developed a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model with constructs Trust in Safety and Hedonic Motivation added. Data was collected by an online questionnaire. Effort expectancy, performance expectancy, facilitating conditions, and social influence were found to have a statistically significant positive influence on behavioural intention, with performance expectancy having the greatest impact. Trust in safety was found to consist of two separate dimensions: fears versus assurances and trust. The findings of this study can be used by government and private sectors to better understand consumers’ current perception of the technology and to introduce supporting legislation accordingly.
{"title":"Customer Intentions Towards Autonomous Vehicles in South Africa: An Extended UTAUT Model","authors":"Gordon Morrison, Jean-Paul Van Belle","doi":"10.1109/Confluence47617.2020.9057821","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057821","url":null,"abstract":"Fully Autonomous Vehicles (AVs), or self-driving vehicles, are expected to enter the automobile market in the coming years. This technology is expected to provide society with a range of benefits, from increased mobility for the elderly and adolescents, to decreasing carbon emissions and improving traffic flow. These benefits, however, will not be achieved unless consumers are willing to accept the technology into their lives and daily routine. In acknowledging this potential barrier to AV proliferation, this study developed a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model with constructs Trust in Safety and Hedonic Motivation added. Data was collected by an online questionnaire. Effort expectancy, performance expectancy, facilitating conditions, and social influence were found to have a statistically significant positive influence on behavioural intention, with performance expectancy having the greatest impact. Trust in safety was found to consist of two separate dimensions: fears versus assurances and trust. The findings of this study can be used by government and private sectors to better understand consumers’ current perception of the technology and to introduce supporting legislation accordingly.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116114975","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9057869
Pooja Tiwari, V. Garg, Abhishek Singhal, N. Puri
In the present scenario, high-speed living people need to understand more suitable and safe devices to improve their quality of life. For acquiring this target there is an advanced financial product known as Mobile Banking. This is fully secured as well as much simpler to be utilized specifically by the senior citizens and children. This research has proposed an innovative model of Mobile Banking and which is empirically tested its adoption in BRICS nation’s customers by acquiring the data via questionnaire amongst 160 BRICS nations’ reversers. In this research, the essential nature of this product will be examined in the situation of the BRICS nation as well as the readiness of the BRICS nations to accept this progress with the use of survey methodology through questionnaires. The results are articulated by using the software named as SPSS and results show that many BRICS nationals who have already been presented as respondents are willing to adopt this product if it is more secure as well as results show that Indians prefer security features over other features whereas other countries prefer convenience features of Mobile Banking.
{"title":"Mobile Banking a Myth or Misconception","authors":"Pooja Tiwari, V. Garg, Abhishek Singhal, N. Puri","doi":"10.1109/Confluence47617.2020.9057869","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9057869","url":null,"abstract":"In the present scenario, high-speed living people need to understand more suitable and safe devices to improve their quality of life. For acquiring this target there is an advanced financial product known as Mobile Banking. This is fully secured as well as much simpler to be utilized specifically by the senior citizens and children. This research has proposed an innovative model of Mobile Banking and which is empirically tested its adoption in BRICS nation’s customers by acquiring the data via questionnaire amongst 160 BRICS nations’ reversers. In this research, the essential nature of this product will be examined in the situation of the BRICS nation as well as the readiness of the BRICS nations to accept this progress with the use of survey methodology through questionnaires. The results are articulated by using the software named as SPSS and results show that many BRICS nationals who have already been presented as respondents are willing to adopt this product if it is more secure as well as results show that Indians prefer security features over other features whereas other countries prefer convenience features of Mobile Banking.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116453193","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058048
Aabhas Dhaka, Prabhishek Singh
The Rapid spread of a disease is known as an epidemic. The catastrophe brought by an epidemic not only effects the people of an area, but also brings about a lot of distress in every sector of social strata. An epidemic alerting system has a potential to carve the path how medical surveillance could become more efficient. The epidemic causing diseases are usually vector borne. The diseases are spread by pathogens present in these vectors. An epidemic alerting system could predict how the weather conditions and several other factors effect the growth and propagation of these vectors. The weather conditions could be predicted using the high-end instruments and satellites currently available. Using this prediction, we could forecast the next targets of the epidemic. To implement this epidemic alert system, four algorithms are used namely Random Forest Regression, Decision Tree Regression, Support Vector Regression and Multiple Linear Regression. For dengue, the state wise cases data of the year 2013 to 2017 has been used in the system while for chikungunya the data used is of the year 2013 to 2016. This dataset has been downloaded from a government website, i.e., https://www.data.gov.in/. For the case of dengue, the model has been trained on the data of the year 2013 to 2016 and predictions of the year 2017 have been done. On the other hand, the model has been trained on the data of the year 2013 to 2015 and predictions for the year 2017 have been made regarding Chikungunya. At last, a contrastive analysis has been made on the four algorithms used for both the diseases.
{"title":"Comparative Analysis of Epidemic Alert System using Machine Learning for Dengue and Chikungunya","authors":"Aabhas Dhaka, Prabhishek Singh","doi":"10.1109/Confluence47617.2020.9058048","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058048","url":null,"abstract":"The Rapid spread of a disease is known as an epidemic. The catastrophe brought by an epidemic not only effects the people of an area, but also brings about a lot of distress in every sector of social strata. An epidemic alerting system has a potential to carve the path how medical surveillance could become more efficient. The epidemic causing diseases are usually vector borne. The diseases are spread by pathogens present in these vectors. An epidemic alerting system could predict how the weather conditions and several other factors effect the growth and propagation of these vectors. The weather conditions could be predicted using the high-end instruments and satellites currently available. Using this prediction, we could forecast the next targets of the epidemic. To implement this epidemic alert system, four algorithms are used namely Random Forest Regression, Decision Tree Regression, Support Vector Regression and Multiple Linear Regression. For dengue, the state wise cases data of the year 2013 to 2017 has been used in the system while for chikungunya the data used is of the year 2013 to 2016. This dataset has been downloaded from a government website, i.e., https://www.data.gov.in/. For the case of dengue, the model has been trained on the data of the year 2013 to 2016 and predictions of the year 2017 have been done. On the other hand, the model has been trained on the data of the year 2013 to 2015 and predictions for the year 2017 have been made regarding Chikungunya. At last, a contrastive analysis has been made on the four algorithms used for both the diseases.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123687843","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058189
Aqsa Zafar, Dr. Sanjay Chitnis
Depression detection from Social Networking sites has been studied broadly in previous years. These sites provide a platform for their users to share their life events, emotions, and everyday routine. Many researchers demonstrated that content generated by the users is an efficient way to know about their mental state. By mining user-generated content, depression can be predicted. By collecting all the necessary and relevant information from the social networking sites from the posts, we can predict the person’s mood or negativity. This survey paper focuses on prior research done regarding detecting depression levels based on content from social network sites.
{"title":"Survey of Depression Detection using Social Networking Sites via Data Mining","authors":"Aqsa Zafar, Dr. Sanjay Chitnis","doi":"10.1109/Confluence47617.2020.9058189","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058189","url":null,"abstract":"Depression detection from Social Networking sites has been studied broadly in previous years. These sites provide a platform for their users to share their life events, emotions, and everyday routine. Many researchers demonstrated that content generated by the users is an efficient way to know about their mental state. By mining user-generated content, depression can be predicted. By collecting all the necessary and relevant information from the social networking sites from the posts, we can predict the person’s mood or negativity. This survey paper focuses on prior research done regarding detecting depression levels based on content from social network sites.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124868784","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 : 2020-01-01DOI: 10.1109/Confluence47617.2020.9058312
S. Khapre, Suhail Chopra, Arshad Khan, Pavika Sharma, A. Shankar
Wireless sensor network is a self-organizing network that relies on the interconnection between nodes in the network to transmit data. In order to optimize the ability of WSN nodes to propagate and process data and reduce node energy consumption, the optimized routing method of wireless sensor networks based on improved ant colony algorithm is studied. Improved ant colony algorithm for excellent global optimization is used to optimize the minimum hop routing problem in wireless sensor networks. And is verified by simulation experiments.
{"title":"Optimized routing method for wireless sensor networks based on improved ant colony algorithm","authors":"S. Khapre, Suhail Chopra, Arshad Khan, Pavika Sharma, A. Shankar","doi":"10.1109/Confluence47617.2020.9058312","DOIUrl":"https://doi.org/10.1109/Confluence47617.2020.9058312","url":null,"abstract":"Wireless sensor network is a self-organizing network that relies on the interconnection between nodes in the network to transmit data. In order to optimize the ability of WSN nodes to propagate and process data and reduce node energy consumption, the optimized routing method of wireless sensor networks based on improved ant colony algorithm is studied. Improved ant colony algorithm for excellent global optimization is used to optimize the minimum hop routing problem in wireless sensor networks. And is verified by simulation experiments.","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128663321","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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