{"title":"Secrecy Performance of Cellular Multiuser Two-Way Decode-and-Forward Relay Networks","authors":"Anshul Pandey, S. Yadav","doi":"10.1109/INFOCOMTECH.2018.8722426","DOIUrl":"https://doi.org/10.1109/INFOCOMTECH.2018.8722426","url":null,"abstract":"In this paper, we investigate the secrecy performance of cellular multiuser two-way decode-and-forward (DF) relay networks with physical-layer security constraints, wherein a multiantenna base station (BS) communicates bidirectionally with several single-antenna mobile stations (MSs) via single-antenna DF relay in the presence of a single-antenna eavesdropper. We employ antenna selection (AS) transmission scheme at the BS with MS scheduling. Specifically, we derive the closed-form expression for the secrecy outage probability (SOP) of the considered system under independent and identically distributed (i.i.d.) Rayleigh fading channels. To gain more insights into the system secrecy diversity order, we analyze the asymptotic secrecy outage behavior in the high signal-to-noise ratio (SNR) regime. Our results reveal that the SOP saturates in the medium-to-high SNR regime, and reduces the secrecy diversity order to zero. Finally, the numerical and simulation corroborate our theoretical and analytical findings.","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124143334","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}
Utkarsh Sonpatki, Sai Krishna Mothku, Rashmi Ranjan Rout
{"title":"A Congestion-Aware Routing Protocol for Voluminous Data Collection in Wireless Sensor Networks","authors":"Utkarsh Sonpatki, Sai Krishna Mothku, Rashmi Ranjan Rout","doi":"10.1109/INFOCOMTECH.2018.8722372","DOIUrl":"https://doi.org/10.1109/INFOCOMTECH.2018.8722372","url":null,"abstract":"In an event-based Wireless Sensor Network (WSN), a sensor node generates data on occurrence of an event. The generated data packets may be voluminous and the data need to be delivered at the destination with minimal delay for applications like intrusion detection in the battle-field, monitoring chemical plants, nuclear plants, health of bridges and issuing tsunami alerts. Wireless sensor networks are limited in resources, such as energy, storage, processing power and bandwidth. A huge amount of data and limited bandwidth may result in packet collisions, which leads to packet drops. To achieve the data transmission reliability, retransmission of the lost packets need to be done. However, retransmissions increase packet delivery delay and energy consumption. In this paper, a data forwarding (routing) mechanism has been proposed to control the congestion for voluminous data collection in Wireless Sensor Networks. Further, a M/M/1/B queueing model has been adopted to determine the probability of occurrence of buffer overflow at a node. In the proposed mechanism, every node chooses the best node to route the data based on the level of congestion and available buffer space at upstream neighbour nodes. Simulation has been performed and results are compared with AODV (ad hoc on-demand distance vector) routing algorithm to show the virtue of the proposed mechanism in terms of packet drop, average packet delay and packet delivery ratio.","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128029117","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}
{"title":"An Improved SVD based Image Compression","authors":"Kapil Mishra, Satish Kumar Singh, P. Nagabhushan","doi":"10.1109/INFOCOMTECH.2018.8722414","DOIUrl":"https://doi.org/10.1109/INFOCOMTECH.2018.8722414","url":null,"abstract":"Advent of information technology since last two to three decades led to the enormous generation of multimedia data. Digital images form a major share of multimedia data; hence a large interest over the area of image compression had been seen among researchers. Image compression deals with exploiting visual imperceptibility of human eye to encode data with much smaller number of bits for the same amount of information. Singular Value Decomposition (or SVD as it is commonly abbreviated) based image compression had been extensively studied in the past few decades. We present an improved approach over the technique using the orthonormal property of the matrices produced in SVD decomposition method. We show that we do not need to store full vectors in order to preserve the orthonormal matrices of eigenvectors instead if we store partial vectors then also we can generate the rest of the values using the orthonormal properties of the matrices of eigenvectors. We further derive a mathematical expression for the compression ratio for this scheme and extend it to derive an expression for the gain thus involved. We present the experimental results showing the PSNR, SSIM and compression ratio of the proposed scheme with that of the state of the art SVD compression scheme. The results indicate that the proposed scheme improves the compression ratio while maintain the image quality.","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132339706","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}
{"title":"Spam Email Detection using ID3 Algorithm and Hidden Markov Model","authors":"V. Kumar, Monika, Parveen Kumar, Ambalika Sharma","doi":"10.1109/INFOCOMTECH.2018.8722378","DOIUrl":"https://doi.org/10.1109/INFOCOMTECH.2018.8722378","url":null,"abstract":"Emails are the way to communicate over the Internet but this method of communication is bothersome by the Spam emails. Spam emails are the waste of memory, money, time and communication bandwidth. Thus, Spam emails needed to be identified and culminated. Hence, use of the ID3 algorithm for making the decision trees and the Hidden Markov Model for calculating the probabilities of the events that may occur is used in this paper as a combination to identify the emails as Spam or ham. The model labels the emails as Spam or ham by calculating total probability of an email using all posteriorly classified words in emails and then supervising all processed emails by making their decision trees. For this purpose, an Enron dataset of 5172 emails is used that contains 2086 Spam and 2086 ham pre-classified emails. The experimental result on the given dataset shows that an accuracy of 89% is obtained on the Spam emails.","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127258392","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}
{"title":"A Deep Convolutional Neural Network for Interrelationship Identification between Humans from Images","authors":"Amit Verma, T. Meenpal, B. Acharya","doi":"10.1109/INFOCOMTECH.2018.8722391","DOIUrl":"https://doi.org/10.1109/INFOCOMTECH.2018.8722391","url":null,"abstract":"The paper proposes a deep convolutional neural network for visual categorization of different interrelationships between humans from digital images. To achieve this goal, we first generated a dataset of interrelationships containing two interrelationship classes i.e. handshaking and hugging. Our proposed network having around 2 lakh neurons is trained with 8 lakh parameters. The network contains a total of seven layers i.e. two convolution layers each followed by max pooling layers and fully connected layers. Output layer contains a sigmoid function providing binary outputs i.e. 0 for one class and 1 for another class. To maintain the nonlinearity of images, Rectified Linear Units (ReLUs) have been used in each convolution and fully connected layers. The model generates an average accuracy of approximately 81%. Data augmentation technique has also been applied to reduce over-fitting.","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121229798","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}
{"title":"CICT 2018 Copyright Page","authors":"","doi":"10.1109/infocomtech.2018.8722347","DOIUrl":"https://doi.org/10.1109/infocomtech.2018.8722347","url":null,"abstract":"","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"21 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125778418","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}
Daggumalli Sudheera, K. Sirisha, K. M Inchara, B. Sivaselvan
{"title":"Knowledge Engineering Perspective of Video Compression","authors":"Daggumalli Sudheera, K. Sirisha, K. M Inchara, B. Sivaselvan","doi":"10.1109/INFOCOMTECH.2018.8722366","DOIUrl":"https://doi.org/10.1109/INFOCOMTECH.2018.8722366","url":null,"abstract":"The focus of this paper is to explore the scope of data mining techniques in video compression. Redundant information in a video is generally classified as temporal and spatial. This superflous data takes up excessive storage space and time during transmission which can be eliminated. The proposed work incorporates various data mining techniques like dimensionality reduction, clustering and mining to prune out temporal and spatial redundancies. The results show noticeable improvement in compression ratio. The compression attained by the proposed work is in the vicinity of existing codecs.","PeriodicalId":175757,"journal":{"name":"2018 Conference on Information and Communication Technology (CICT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124267574","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}