Lifetime of Wireless Sensor Network (WSN) is an important issue which affects its implementation in various real time applications. The major factor behind the energy consumption in WSN is its data collection mechanism. The direct data transmission from each sensor node to the Base Station (BS) consumes more energy than other alternatives. Also it is unnecessary, due to redundant data transmission because of geographically closer nodes. Clustering is the best data collection architectural model for WSN since it takes care of in-network processing which handles redundant data within the network. The techniques used for the network having uniform node distribution are not suitable for the networks which have nonuniformly distributed nodes. This paper contributes a novel clustering algorithm: Fuzzy Logic Based Energy Efficient Clustering Hierarchy (FLECH) for nonuniform WSN. The clusters in FLECH are created using proper parameters which increases the lifetime of the WSN. Fuzzy logic in FLECH is wisely used to combine important parameters like residual energy, node centrality, and distance to BS for electing best suitable nodes as CH and increases the network lifetime. FLECH performance is verified in different scenarios and the results are compared with LEACH, CHEF, ECPF, EAUCF, and MOFCA. The simulation results clearly indicate the lifetime increase by FLECH over other algorithms and its energy conservation per round of data collection in the network.
Bigdata era is seeing the data burst occurring in a multitude of angles that are better expressed in terms of the 4Vs (Volume, Velocity, Velocity, Veracity). While trying to infer information from data, care should be exercised as not to reveal the identity of the data owner, which breaches the privacy rights. Leakage of information can happen right from the data collection point, at the data storage area, followed by the distribution of data to data users/miners and finally with published results. A cross-matching of all these points with the 4Vs (growing still) of big data, puts a huge challenge on how to extract the maximum possible information, without compromising on the privacy of the data owner. Anonymization of the original data should be done at one or more of the above-mentioned stages before the data are given for the mining process. This work makes a survey of the various anonymization techniques followed to transform the data in such a way that the privacy of the data owner is not compromised. Also, the sample data drawn should resemble and represent the original dataset in the maximum possible number of dimensions. The results of the various methodologies have been analyzed and the observations have been presented.
In today's world, textual data has made momentous progress in social media. The rise of digital communication via text has paved the way to emoji, a pictographically represented way of expressing emotions. In digital communication, Emoji gives a visual appeal to the text, which improves communication and new vistas of exchange and creativity. While emoji entry prediction based on text is well optimized, based on the neural network model, predicting the future emojis from images is not so easy due to lack of knowledge on the same. While effective models already exist for generating text descriptions of images, less attention has been given to models of symbolic description. We have used two models for predicting emoji from images, convolutional neural network architecture for image classification and an emoji2vec embedding into word2vec model. We have also done a sentiment analysis of the text for predicting future emoji labels. Our model captures the relation between emojis in an optimized way. This model has optimized the search time for future emoji entry predictions from images.
Water is the main resource for agriculture. Management of water in agricultural field is a challenging process. To manage the water content in the agricultural field, smart irrigation system has been proposed by using fuzzy based decision support system on Hyperspectral Image benchmark dataset. Hyperspectral images are the process of collected and processed the images from electromagnetic spectrum. Recent studies show that hyperspectral images are very accurate in collecting the soil moistures value. Dataset is collected in five-day field of campaign the soil is the type of clayey slit and it is non vegetation. Hyperspectral datasets which consist of range value between 454 to 598 nm. Value is gathered from the 285 hyperspectral snapshot camera recording images with 125 spectral bands with the spectral resolution of 4 nm. Experimental results of this method achieve the accuracy of 0.98. Hence the proposed method reduces the water wastage to an extent.
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