In this modernistic age of innovative technologies like big data processing, cloud computing, and Internet of things, the utilization of multimedia information is growing daily. In contrast to other forms of multimedia, videos are extensively utilized and streamed over the Internet and communication networks in numerous Internet of Multimedia Things (IoMT) applications. Consequently, there is an immense necessity to achieve secure video transmission over modern communication networks due to the third-party exploitation and falsification of transmitted and stored digital multimedia data. The present methods for secure communication of multimedia content between clouds and mobile devices have constraints in terms of processing load, memory support, data size, and battery power. These methods are not the optimum solutions for large-sized multimedia content and are not appropriate for the restricted resources of mobile devices and clouds. The High-Efficiency Video Coding (HEVC) is the latest and modern video codec standard introduced for efficiently storing and streaming of high-resolution videos with suitable size and higher quality. In this paper, a novel hybrid cryptosystem combining DNA (Deoxyribonucleic Acid) sequences, Arnold chaotic map, and Mandelbrot sets is suggested for secure streaming of compressed HEVC streams. Firstly, the high-resolution videos are encoded using the H.265/HEVC codec to achieve efficient compression performance. Subsequently, the suggested Arnold chaotic map ciphering process is employed individually on three channels (Y, U, and V) of the compressed HEVC frame. Then, the DNA encoding sequences are established on the primary encrypted frames resulted from the previous chaotic ciphering process. After that, a modified Mandelbrot set-based conditional shift process is presented to effectively introduce confusion features on the Y, U, and V channels of the resulted ciphered frames. Massive simulation results and security analysis are performed to substantiate that the suggested HEVC cryptosystem reveals astonishing robustness and security accomplishment in contrast to the literature cryptosystems.

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A new hybrid approach by integrating the support vector machine (SVM) with firefly algorithm (FFA) is proposed to estimate shape (k) and scale (c) parameters of the Weibull distribution function according to previously established analytical methods. The extracted data of two widely successful methods utilized to compute parameters k and c were used as learning and testing information for the SVM-FFA method. The simulations were performed on both daily and monthly scales to draw further conclusions. The performance of SVM-FFA method was compared against other existing techniques to demonstrate its efficiency and viability. The results conclusively indicate that SVM-FFA method provides further precision in the predictions. Nevertheless, for daily estimations, the applicability of proposed method could not be feasible owing to high day-by-day fluctuations of parameters k, whereas the results of monthly estimation are completely appealing and precise. In summary, the SVM-FFA is a highly viable and efficient technique to estimate wind speed distribution on monthly scale. It is expected that the proposed method would be profitable for wind researchers and experts to be used in many practical applications, such as evaluating the wind energy potential and making a proper decision to nominate the optimal wind turbines. V C 2015 American Institute of Chemical Engineers Environ Prog, 35: 867-875, 2016

The increasing growth in the demand for cloud computing services, due to the increasingly digital transformation and the high elasticity of the cloud, requires more efforts to improve the electrical energy efficiency of cloud data centers. In this paper, an energy-efficient hybrid (EEH) framework for improving the efficiency of consuming electrical energy in data centers is proposed and evaluated. The proposed framework is based on both the requests' scheduling and servers' consolidation approaches rather than depending on only one approach as in existing related works. The EEH framework sorts the customers' requests (tasks) according to their time and power needs before performing the scheduling. It has a scheduling algorithm that considers power consumption when taking its scheduling decisions. It also has a consolidation algorithm that determines the underloaded servers to be slept or hibernated, the overloaded servers, the virtual machines to be migrated and the servers that will receive migrated virtual machines. In addition, the EEH framework includes a migration algorithm for transferring migrated virtual machines to new servers. Results of simulation experiments indicate the superiority of the EEH framework over approaches that depend on using only one approach to reduce power consumption in terms of Power Usage Effectiveness (PUE), Data Center Energy Productivity (DCEP), average execution time, throughput and cost-saving. Index terms-green computing, scheduling, consolidation, power consumption.