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A latest tera to zeta era has been created during huge volume of data sets, which keep on collected from different social networks, machine to machine devices, google, yahoo, sensors etc. called as big data. Because day by day double the data storage size, data processing power, data availability and digital world data size in zeta bytes. Apache Hadoop is latest market weapon to handle huge volume of data sets by its most popular components like hdfs and mapreduce, to achieve an efficient storage ability and efficient processing on massive volume of data sets. To design an effective algorithm is a key factor for selecting nodes are important, to optimize and acquire high performance in Big data. An efficient and useful survey, overview, advantages and disadvantages of these scheduling algorithms provided also identified throughout this paper.

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In modern secure Wireless Sensor Networks (WSN), the sensor-nodes need extra energy owing to secure transmission of perceived information. So the energy-utilization of sensor-node should calculate while transfer the sensed-attributes securely to network. In this experimentation, we are proposing a revised Low Energy Adaptive Clustering Hierarchy (LEACH) protocol as LEATCH along secure information transmission (privacy and node authentication) in various levels using Quality of Protection Modeling Language (QoPML), which balance the Security-Energy trade-offs. This research experimentally analyzes the impact of data privacy, authentication operations on energy-utilization at sensor-node level while applying a LEACH & LEATCH. The obtained outcomes indicate the optimized LEATCH is outperforming correlated to the basic Leach with respect to minimal energy-utilization, time efficiency and expands lifetime of modern-secure-WSNs.

In recent days, image communication has evolved in many fields like medicine, entertainment, gaming, mail, etc. Thus, it is an immediate need to denoise the received image because noise that is added in the channel during communication alters or deteriorates information contained in the image. Any image processing techniques concerned with image denoising or image noise removal has to be started with the spatial domain and end with the transform domain. A lot of research was carried out in the spatial domain by modifying the performance of different image filters such as mean filters, median filters, Laplacian filters, etc. Recently much research was carried out in Transform techniques under the transform domain, with evolutionary computing tools (ECT). ECT has proven to be dominant when compared with traditional denoising techniques in combination with wavelets in the transform domain. In this article, the authors applied a novel ECT such as SGOA on the denoising problem for denoising monochrome as well as color images and performance for denoising was evaluated using several image quality metrics.