Praveen Kumar, M. Ruthwik, M. K. Sree, Misba Saad, A. Patil
Sensors are wireless devices used for monitoring and recording the surrounding physical and environmental parameters. The data collected is transmitted continuously to a base station. A collection of such wireless sensors form a network called Wireless Sensor Networks (WSNs). Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of WSNs. Here, an emergency-adaptive, real time and robust routing protocol called EAR is presented. A comparison of the performance parameters is made with EAR and AODV by varying the number of nodes and also by changing the simulation time. The metrics chosen are Average End-to-End Delay, Throughput, Average Jitter, Packet Delivery Ratio and Carried Load. Analysis and performance study is done using QualNet 7.1 simulator.
{"title":"Implementation of an Emergency Evacuation System Using Intelligent Routing Using QualNet Simulator","authors":"Praveen Kumar, M. Ruthwik, M. K. Sree, Misba Saad, A. Patil","doi":"10.1145/2909067.2909092","DOIUrl":"https://doi.org/10.1145/2909067.2909092","url":null,"abstract":"Sensors are wireless devices used for monitoring and recording the surrounding physical and environmental parameters. The data collected is transmitted continuously to a base station. A collection of such wireless sensors form a network called Wireless Sensor Networks (WSNs). Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of WSNs. Here, an emergency-adaptive, real time and robust routing protocol called EAR is presented. A comparison of the performance parameters is made with EAR and AODV by varying the number of nodes and also by changing the simulation time. The metrics chosen are Average End-to-End Delay, Throughput, Average Jitter, Packet Delivery Ratio and Carried Load. Analysis and performance study is done using QualNet 7.1 simulator.","PeriodicalId":371590,"journal":{"name":"Women In Research","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128018220","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}
Clustering various web elements facilitates data analysis, which plays an important role in our day-to-day life. Due to massive use of internet and huge electronic data access, some useful technique is needed for web element analysis. Grouping the data according to the categories is the tasks that are helpful in data analysis. Thus, clustering of web elements in various ways is needed. Many researchers are working on cluster analysis and its use for web elements. As a result, various clustering algorithms and tools are developed and used for web analytics. Our study includes a survey of some commonly used clustering algorithms to identify the usage of clustering techniques for improving web elements analysis, in various ways. The study focuses various uses of clustering for web data such as search result clustering to improve result visualization, for segment marketing, and to detect sensitive website.
{"title":"The Study of the Usage of Data Analytic and Clustering Techniques for Web Elements","authors":"S. Mehrotra, Shruti Kohli","doi":"10.1145/2909067.2909089","DOIUrl":"https://doi.org/10.1145/2909067.2909089","url":null,"abstract":"Clustering various web elements facilitates data analysis, which plays an important role in our day-to-day life. Due to massive use of internet and huge electronic data access, some useful technique is needed for web element analysis. Grouping the data according to the categories is the tasks that are helpful in data analysis. Thus, clustering of web elements in various ways is needed. Many researchers are working on cluster analysis and its use for web elements. As a result, various clustering algorithms and tools are developed and used for web analytics. Our study includes a survey of some commonly used clustering algorithms to identify the usage of clustering techniques for improving web elements analysis, in various ways. The study focuses various uses of clustering for web data such as search result clustering to improve result visualization, for segment marketing, and to detect sensitive website.","PeriodicalId":371590,"journal":{"name":"Women In Research","volume":"23 25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128444127","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}
Mobility of nodes plays a key role in Wireless sensor network (WSN). It comes up with challenges like energy efficiency and improve network lifetime. WSN contain nodes which are having capability of sensing, computation, and wireless communications. Every node has ability to communicate with other node or Base Station (BS). Sensor nodes have limited energy as compared to BS. Energy efficiency is very important in WSN, because sensor node cannot provide energy for long time while sensing and processing data in network. For this reason mobile sink is rich in energy supply and computational power. Mobile sink can be used to improve the performance of network by shifting the load of energy depletion from sensor node to sink. The effective way to increase the network lifetime is to reposition the sink node. It also helps to reduce energy-holes problems. It is necessary to keep track of mobile sink so that data distribution should take place correctly. But tracking the location of mobile sink and updating it frequently is also a challenge for energy conservation and it affects the network performance. Various sink mobility strategies exist to increase energy efficiency. This paper mainly focuses on study of different algorithms, which are used for efficient data processing using mobile sink.
{"title":"A Survey of various Sink Mobility based Techniques in Wireless Sensor Network","authors":"Suwarna Latambale, Sumedha Sirsikar","doi":"10.1145/2909067.2909075","DOIUrl":"https://doi.org/10.1145/2909067.2909075","url":null,"abstract":"Mobility of nodes plays a key role in Wireless sensor network (WSN). It comes up with challenges like energy efficiency and improve network lifetime. WSN contain nodes which are having capability of sensing, computation, and wireless communications. Every node has ability to communicate with other node or Base Station (BS). Sensor nodes have limited energy as compared to BS. Energy efficiency is very important in WSN, because sensor node cannot provide energy for long time while sensing and processing data in network. For this reason mobile sink is rich in energy supply and computational power. Mobile sink can be used to improve the performance of network by shifting the load of energy depletion from sensor node to sink. The effective way to increase the network lifetime is to reposition the sink node. It also helps to reduce energy-holes problems. It is necessary to keep track of mobile sink so that data distribution should take place correctly. But tracking the location of mobile sink and updating it frequently is also a challenge for energy conservation and it affects the network performance. Various sink mobility strategies exist to increase energy efficiency. This paper mainly focuses on study of different algorithms, which are used for efficient data processing using mobile sink.","PeriodicalId":371590,"journal":{"name":"Women In Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126802774","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 : 1900-01-01DOI: 10.1007/978-981-10-7641-1
{"title":"Data Science and Big Data Analytics, ACM-WIR 2018, Women In Research 2018, Indore, India, 5-6 January 2018","authors":"","doi":"10.1007/978-981-10-7641-1","DOIUrl":"https://doi.org/10.1007/978-981-10-7641-1","url":null,"abstract":"","PeriodicalId":371590,"journal":{"name":"Women In Research","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121980925","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 : 1900-01-01DOI: 10.1007/978-981-10-7641-1_26
K. A. Kumar, B. Kumar, A. Veeramuthu, V. S. Mynavathi
{"title":"Unsupervised Machine Learning for Clustering the Infected Leaves Based on the Leaf-Colors","authors":"K. A. Kumar, B. Kumar, A. Veeramuthu, V. S. Mynavathi","doi":"10.1007/978-981-10-7641-1_26","DOIUrl":"https://doi.org/10.1007/978-981-10-7641-1_26","url":null,"abstract":"","PeriodicalId":371590,"journal":{"name":"Women In Research","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128948586","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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