Enhancements and extensions in pervasive computing have enabled penetration of cloud computing enabled services into almost all walks of human life. The expansion of computational capabilities into everyday objects and processes optimizes end users requirement to directly interact with computing systems. However, the amalgamation of technologies like Cloud Computing, Internet of Things (IoT), Deep Learning etc are further giving way to creation of smart ecosystem for smart human living. This transformation in the whole pattern of living as well as working in enterprises is generating high expectations as well as performance load on existing cloud implementation as well as cloud services. In this complete scenario, there are simultaneous efforts on optimizing as well as securing cloud services as well as the data available on the cloud.
This manuscript is an attempt at introducing how cloud computing has become pivotal in the current enterprise setting due to its pay-as -you -use character. However, the allurement of using services without having to procure and retain involved hardware and software also has certain risks involved. The main risk involved in choosing cloud is compromising security concerns. Many potential customers avoid migrating towards cloud due to security concerns. Security concerns for the cloud implementations in the recent times have grown exponentially for all the varied stakeholders involved. The aim of this manuscript is to analyze the current security challenges in the existing cloud implementations. We provide a detailed analysis of existing cloud security taxonomies enabling the reader to make an informed decision on what combination of services and technologies could be used or hired to secure their data available on the cloud.

Beamspace MIMO performs beam-selection which can substantially reduce the number of power-consuming radio frequency (RF) chains without perceptible performance deterioration. However, for capacity-approaching performance, accurate information of the beamspace-channel of large-size is required for beam-selection, which is contesting in case of little number of RF-chains. To overcome such problem, I proposed an efficient support-detection (SD) algorithm for channel-estimation with low pilot-overhead and short number of RF chains. The key idea of SD-algorithm is to divide the whole issue of beamspace channel-estimation into a series of sub-issues, where each of them considers only one sparse channel-component. The support of each channel component is detected reliably by deploying the sparse structure attributes of the beamspace-channel. The effect of this channel-component is eliminated from the whole channel-estimation issue. Thus, the sparse beamspace-channel can be estimated with low pilot-overhead. Simulation Results shows that the proposed schemes perform much better than the conventional compressed-sensing (CS) schemes.