Pub Date : 2018-12-01DOI: 10.1109/icacat.2018.8933679
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icacat.2018.8933679","DOIUrl":"https://doi.org/10.1109/icacat.2018.8933679","url":null,"abstract":"","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82759603","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933727
Jayasri Santhappan, P. Chokkalingam
Business forecast is a biggest factor which generally affects the economical condition of any Financial Industry. If the forecast model is not a better one then it can cause liquidation and spoil the trust of customers in the market. Early predictions based on social media clients’ opinion plays a major role in order to reduce risk on business and keep the trust of customer. According to the survey done by Fintech’s world topic analysis is treated as one of the vital factor used for the determination of client’s trends and for forecast analysis. Here we have performed a comparative analysis upon the social media data provide by Twitter in order to get an idea about the perception and understanding of clients’ requirements across the world. For the experimentation purpose we have used Tweeter data for tweet analysis, for stock price we have yahoo finance data and for number of stocks we have used morning star data set. For the processing of Tweets given by the clients we have built an automated system using Deep Learning. Here the problem is divided in to 2 parts. In first part Text classification is done using Tensorflow and Keras, Latent Dirichlet allocation (LDA), Natural Language Toolkit (NLTK-NLP).In this part using topic analysis the past tweet history is analyzed. In second part we are predicting forecastto identify multiple key business factors using Long Short term Memory (LSTM) using python/Rto. The actual aim of the system is to discover the effect of 3 fundamental parameters like security breaches, innovation, and stock exchange which are present in tweet given by the customers. Here the analysis is done on the last ten years tweets given by the clients for prediction of upcoming seven-day as well as monthly Market Cap. The actual intention of the work done here is to uncover the major diversity among two banks and bridge up the 3 gaps data breach, innovation and stock exchange in the available models. The latest information obtained in the system offers advantages to both Bank and customers to forecast Market value for the unbeaten estimation. We have obtained a prediction accuracy of 70.74% and 54.55% for monthly prediction and for weekly prediction we have obtained accuracy of 83.44% and 76.06% for Bank A and Bank B.
{"title":"An Intelligent Market Capitalization Predictive System Using Deep Learning","authors":"Jayasri Santhappan, P. Chokkalingam","doi":"10.1109/ICACAT.2018.8933727","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933727","url":null,"abstract":"Business forecast is a biggest factor which generally affects the economical condition of any Financial Industry. If the forecast model is not a better one then it can cause liquidation and spoil the trust of customers in the market. Early predictions based on social media clients’ opinion plays a major role in order to reduce risk on business and keep the trust of customer. According to the survey done by Fintech’s world topic analysis is treated as one of the vital factor used for the determination of client’s trends and for forecast analysis. Here we have performed a comparative analysis upon the social media data provide by Twitter in order to get an idea about the perception and understanding of clients’ requirements across the world. For the experimentation purpose we have used Tweeter data for tweet analysis, for stock price we have yahoo finance data and for number of stocks we have used morning star data set. For the processing of Tweets given by the clients we have built an automated system using Deep Learning. Here the problem is divided in to 2 parts. In first part Text classification is done using Tensorflow and Keras, Latent Dirichlet allocation (LDA), Natural Language Toolkit (NLTK-NLP).In this part using topic analysis the past tweet history is analyzed. In second part we are predicting forecastto identify multiple key business factors using Long Short term Memory (LSTM) using python/Rto. The actual aim of the system is to discover the effect of 3 fundamental parameters like security breaches, innovation, and stock exchange which are present in tweet given by the customers. Here the analysis is done on the last ten years tweets given by the clients for prediction of upcoming seven-day as well as monthly Market Cap. The actual intention of the work done here is to uncover the major diversity among two banks and bridge up the 3 gaps data breach, innovation and stock exchange in the available models. The latest information obtained in the system offers advantages to both Bank and customers to forecast Market value for the unbeaten estimation. We have obtained a prediction accuracy of 70.74% and 54.55% for monthly prediction and for weekly prediction we have obtained accuracy of 83.44% and 76.06% for Bank A and Bank B.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"33 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90531407","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933769
Saurabh
As we are becoming more and more dependent on computers the attack vectors on them are increasing day by day. The cyberspace is becoming the battlefield of the 21st century as we are witnessing the increasing potential of a cyber-attack on the critical infrastructure. Malware are the most sophisticated evil code It is designed to damage computer systems without the knowledge of the owner these days malware are made up with special arbitrary to evade detection from the antivirus [1] with a huge potential to damage computer systems. Malware analysis is a process for studying the components and the behavior of malware. For analyzing malware we will use two types of methods static analysis and the dynamic analysis. In the static analysis the malware are examined without running it, whereas in dynamic analysis the malware is analyzed while running it in a virtual and controlled environment. In this research we are going to focus on malware analysis using the static and the dynamic method which will help us to access damage, to know the indicators of compromise and to determine the sophistication level of an intruder and to catch the creator of the malware.
{"title":"Advance Malware Analysis Using Static and Dynamic Methodology","authors":"Saurabh","doi":"10.1109/ICACAT.2018.8933769","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933769","url":null,"abstract":"As we are becoming more and more dependent on computers the attack vectors on them are increasing day by day. The cyberspace is becoming the battlefield of the 21st century as we are witnessing the increasing potential of a cyber-attack on the critical infrastructure. Malware are the most sophisticated evil code It is designed to damage computer systems without the knowledge of the owner these days malware are made up with special arbitrary to evade detection from the antivirus [1] with a huge potential to damage computer systems. Malware analysis is a process for studying the components and the behavior of malware. For analyzing malware we will use two types of methods static analysis and the dynamic analysis. In the static analysis the malware are examined without running it, whereas in dynamic analysis the malware is analyzed while running it in a virtual and controlled environment. In this research we are going to focus on malware analysis using the static and the dynamic method which will help us to access damage, to know the indicators of compromise and to determine the sophistication level of an intruder and to catch the creator of the malware.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"44 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74076110","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933700
Navneet Bhargava, Aparna R. Gupta, Litesh Bopche
The Genetic Algorithm is practical to resolve the obstacles of tiny samples and provide better prognostication for non linear behaviors and it is desirable for the Dissolved Gas Analysis in Power Transformers. The GA generates the initial accumulation at random prosper and scrutiny space faster and modifies the global search cognition and convergent speed. As question arises whether the data was nonlinear or not? It was decided to do the data analysis first. Thus the gas concentration in ppm (parts per million) of all the DGA samples was checked for non linearity.
{"title":"Statistical Analysis of Data for Dissolved Gases in Transformer","authors":"Navneet Bhargava, Aparna R. Gupta, Litesh Bopche","doi":"10.1109/ICACAT.2018.8933700","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933700","url":null,"abstract":"The Genetic Algorithm is practical to resolve the obstacles of tiny samples and provide better prognostication for non linear behaviors and it is desirable for the Dissolved Gas Analysis in Power Transformers. The GA generates the initial accumulation at random prosper and scrutiny space faster and modifies the global search cognition and convergent speed. As question arises whether the data was nonlinear or not? It was decided to do the data analysis first. Thus the gas concentration in ppm (parts per million) of all the DGA samples was checked for non linearity.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"11 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74418100","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933732
S. Chaman, Aniket Ghadi, Ninad Ketkar
This paper proposes a novel Sixth Sense Teaching Aid (SSTA) which incorporates sixth sense technology in projectors for educational purpose. The Projector-PC system was earlier used only for displaying the presentations but with the aid of the proposed system, users can touch on any projected surfaces for interaction purpose. In the SSTA system, the graphical user interface (GUI) buttons are projected on any flat surface like wall and it deals with touch detection of the projected screen using red color parameter both for still image and real time images. The algorithm to perform touch detection is executed in two stages: 1) Feature extraction and button’s touch detection using red color thresholding algorithm which reduces the computational complexity of the processing module; and 2) Performance of assigned operation according to touch action judgment. New born technology named Sixth Sense technology is also implemented in SSTAfor getting relevant information from the internet, whenever we touch any projected Figure or headline. The proposed SSTA system is able to do real time touch detection with 97 percent accuracy which is demonstrated through projected GUI and using a data set collected under different settings of illumination variation, hand orientation and occlusion.
{"title":"Sixth Sense Teaching Aid","authors":"S. Chaman, Aniket Ghadi, Ninad Ketkar","doi":"10.1109/ICACAT.2018.8933732","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933732","url":null,"abstract":"This paper proposes a novel Sixth Sense Teaching Aid (SSTA) which incorporates sixth sense technology in projectors for educational purpose. The Projector-PC system was earlier used only for displaying the presentations but with the aid of the proposed system, users can touch on any projected surfaces for interaction purpose. In the SSTA system, the graphical user interface (GUI) buttons are projected on any flat surface like wall and it deals with touch detection of the projected screen using red color parameter both for still image and real time images. The algorithm to perform touch detection is executed in two stages: 1) Feature extraction and button’s touch detection using red color thresholding algorithm which reduces the computational complexity of the processing module; and 2) Performance of assigned operation according to touch action judgment. New born technology named Sixth Sense technology is also implemented in SSTAfor getting relevant information from the internet, whenever we touch any projected Figure or headline. The proposed SSTA system is able to do real time touch detection with 97 percent accuracy which is demonstrated through projected GUI and using a data set collected under different settings of illumination variation, hand orientation and occlusion.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"19 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87245861","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933794
Abhiruchi Rathi, Neenansha Jain
Microstrip patch antenna plays a vital role in WLAN communication, mobile communication, 3G, 4G, and WiFi, WI-MAX devices in different range. Z-shape microstrip patch antenna is very interesting shape for researchers. In this research work proposed a Z-shape microstrip antenna (MSA), and apply defected ground structure (DGS) on the ground side. Designed antenna is a dual band antenna that is intended to work at 1 to 10 GHz and shows good result in this range. After modelling and simulation, designed, implemented. These results are compared with different previous design on the basis of return loss (S-11) VSWR and other antenna parameters.
{"title":"Modified Z-Shape MSA with DGS for WLAN Ranges","authors":"Abhiruchi Rathi, Neenansha Jain","doi":"10.1109/ICACAT.2018.8933794","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933794","url":null,"abstract":"Microstrip patch antenna plays a vital role in WLAN communication, mobile communication, 3G, 4G, and WiFi, WI-MAX devices in different range. Z-shape microstrip patch antenna is very interesting shape for researchers. In this research work proposed a Z-shape microstrip antenna (MSA), and apply defected ground structure (DGS) on the ground side. Designed antenna is a dual band antenna that is intended to work at 1 to 10 GHz and shows good result in this range. After modelling and simulation, designed, implemented. These results are compared with different previous design on the basis of return loss (S-11) VSWR and other antenna parameters.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"29 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87167079","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933674
K. Pandey, D. Shukla
The current time technology is growing very faster and data is generating very fast so data characteristics have changed in form of data to big data. If anybody wants to mine some related data in big data environment then present data mining algorithm fails to mine relationship in big data and it takes a lot of time for processing. MapReduce approach is a most efficient algorithm in big data framework which handles a huge amount of data and gives fast result. The Apriori algorithm is more powerful algorithm for mining on interesting relationships between dataset in any type of databases or same databases. In present time a lot of MapReduce base Apriori algorithms are available but its Map and Reduce function run to multiple times and works only for the transaction database. This paper describes what is big data with its characteristics, concept of Association rules with the Apriori algorithm in big data, problems in the existing MapReduce base Apriori algorithm. We propose new improve MapReduce approach base Apriori algorithm for mining on a relationship with the help of given one suitable example where Reduce function runs only one time after running on Map function and this proposed algorithm run on any type of database.
{"title":"Mining on Relationships in Big Data era using Improve Apriori Algorithm with MapReduce Approach","authors":"K. Pandey, D. Shukla","doi":"10.1109/ICACAT.2018.8933674","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933674","url":null,"abstract":"The current time technology is growing very faster and data is generating very fast so data characteristics have changed in form of data to big data. If anybody wants to mine some related data in big data environment then present data mining algorithm fails to mine relationship in big data and it takes a lot of time for processing. MapReduce approach is a most efficient algorithm in big data framework which handles a huge amount of data and gives fast result. The Apriori algorithm is more powerful algorithm for mining on interesting relationships between dataset in any type of databases or same databases. In present time a lot of MapReduce base Apriori algorithms are available but its Map and Reduce function run to multiple times and works only for the transaction database. This paper describes what is big data with its characteristics, concept of Association rules with the Apriori algorithm in big data, problems in the existing MapReduce base Apriori algorithm. We propose new improve MapReduce approach base Apriori algorithm for mining on a relationship with the help of given one suitable example where Reduce function runs only one time after running on Map function and this proposed algorithm run on any type of database.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"59 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84484259","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933798
Raksha Tiwari, Tripti Saxena
Vehicular ad hoc network is a rising direction of research in improves network and communication. It is a type of ad hoc network in which there is a decentralized type ofwireless network. It is a communication less which makes them powerless against assaults like dos. The incoming traffic flooding the victim originates from many different sources. this effectively makes it impossible to stop the attack simply by blocking a single source. an existing paper they used maliciousand irrelevant packet detection algorithm for detecting malicious node on the basis of node velocity and the frequency of packet generated depend on node maximum velocity. Basically vehicles move with speedy which cannot efficaciousrecognize malicious nodes. in our proposed work, we calculatethe average speed of vehicles and check the performance ofvehicles so that we can recognize the true malicious nodes thenapplying reliable function to detect malicious nodes. In our results, we improved packet delivery ratio, routing overheadand throughput of the network.
{"title":"Reliable Function used to Improved Security by Eliminating Malicious Nodes in VANET","authors":"Raksha Tiwari, Tripti Saxena","doi":"10.1109/ICACAT.2018.8933798","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933798","url":null,"abstract":"Vehicular ad hoc network is a rising direction of research in improves network and communication. It is a type of ad hoc network in which there is a decentralized type ofwireless network. It is a communication less which makes them powerless against assaults like dos. The incoming traffic flooding the victim originates from many different sources. this effectively makes it impossible to stop the attack simply by blocking a single source. an existing paper they used maliciousand irrelevant packet detection algorithm for detecting malicious node on the basis of node velocity and the frequency of packet generated depend on node maximum velocity. Basically vehicles move with speedy which cannot efficaciousrecognize malicious nodes. in our proposed work, we calculatethe average speed of vehicles and check the performance ofvehicles so that we can recognize the true malicious nodes thenapplying reliable function to detect malicious nodes. In our results, we improved packet delivery ratio, routing overheadand throughput of the network.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"20 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81750468","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933574
Nita Patil, S. Sawarkar
Visual concept detection is the task of determining concept present in image or video by extracting low level features and training of classifiers in general. Researchers have used various features and classifiers for concept detection. In this paper performance evaluation of fusion of features and classifier is presented. Color moment, HSV histogram, wavelet transform and combination of these features have been used in proposed system. Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed for classification. The proposed system is implemented on Corel 1K image dataset and Trecvid 2007 benchmark video dataset. The system performance is evaluated using predictive measures of precision, recall and f score. Using simple fusion of features average precision of SVM classifier is better than ANN. The proposed global feature fusion based method is simple yet effective in concept detection task.
{"title":"Concept Detection using Multiple Feature Set and Classifiers","authors":"Nita Patil, S. Sawarkar","doi":"10.1109/ICACAT.2018.8933574","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933574","url":null,"abstract":"Visual concept detection is the task of determining concept present in image or video by extracting low level features and training of classifiers in general. Researchers have used various features and classifiers for concept detection. In this paper performance evaluation of fusion of features and classifier is presented. Color moment, HSV histogram, wavelet transform and combination of these features have been used in proposed system. Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed for classification. The proposed system is implemented on Corel 1K image dataset and Trecvid 2007 benchmark video dataset. The system performance is evaluated using predictive measures of precision, recall and f score. Using simple fusion of features average precision of SVM classifier is better than ANN. The proposed global feature fusion based method is simple yet effective in concept detection task.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"123 2 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88493745","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 : 2018-12-01DOI: 10.1109/ICACAT.2018.8933784
Neeraj Chandnani, C. N. Khairnar
Internet of Things (IoT) is a network paradigm in which data aggregation and data security plays a vital role. Data aggregation in IoT describes collection of information from different users and data security means encryption of collected data using cryptography method. The proposed work comprises of devices and gateway to perform data aggregation and data encryption. Data aggregation is performed using clustering in which data are clustered and secured by Particle Swarm Optimization (PSO) algorithm which finds the cluster head. After finding cluster head, nodes requests to join as cluster member. PSO computes fitness function using metrics i.e. energy, end-to-end delay, scoring factor, packet drops and successful packet transformation. After completion of clustering process, data encryption process is held in which, cluster head collects data from the cluster members and encrypts it using Elliptic Curve Cryptography (ECC) method. Finally, encrypted data are dispatched to gateway device. Experimental result shows, the proposed work on Secure Particle Swarm Optimization (SPSO)prompts better performance in following metrics i.e. delay, throughput and energy consumption.
{"title":"A Novel Secure Data Aggregation in IoT using Particle Swarm Optimization Algorithm","authors":"Neeraj Chandnani, C. N. Khairnar","doi":"10.1109/ICACAT.2018.8933784","DOIUrl":"https://doi.org/10.1109/ICACAT.2018.8933784","url":null,"abstract":"Internet of Things (IoT) is a network paradigm in which data aggregation and data security plays a vital role. Data aggregation in IoT describes collection of information from different users and data security means encryption of collected data using cryptography method. The proposed work comprises of devices and gateway to perform data aggregation and data encryption. Data aggregation is performed using clustering in which data are clustered and secured by Particle Swarm Optimization (PSO) algorithm which finds the cluster head. After finding cluster head, nodes requests to join as cluster member. PSO computes fitness function using metrics i.e. energy, end-to-end delay, scoring factor, packet drops and successful packet transformation. After completion of clustering process, data encryption process is held in which, cluster head collects data from the cluster members and encrypts it using Elliptic Curve Cryptography (ECC) method. Finally, encrypted data are dispatched to gateway device. Experimental result shows, the proposed work on Secure Particle Swarm Optimization (SPSO)prompts better performance in following metrics i.e. delay, throughput and energy consumption.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"112 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86758220","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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