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.

The alarming environmental concern of air pollution has a severe global impact. Accurate forecasting can help minimize its hazardous implications well in time. Air Quality forecasting is a complex problem in the domain of time series data forecasting. In this paper we propose a novel customized air quality forecaster developed using Gated Recurrent Unit network-based Encoder-Decoder model (GRUED) of Deep Learning using Inverse Distance Weighted Interpolation for forecasting air pollutant concentrations of Delhi, India. The unique composition and customization of our air quality forecaster is a more efficient and better state of the art model for pollutant concentration prediction than its counterparts. Experimental results are indicative that the proposed model outperforms the conventional Deep Learning models. The proposed model was made to forecast air pollutant concentrations of SO2, CO, NO2 and O3. Each pollutant forecast was evaluated by computing MAE and RMSE metrices. MAE values for SO2, CO, NO2 and O3 forecasts were 60.63%, 26.83%, 33.2% and 31.33% lesser for our GRUED model as compared to conventional LSTM model. RMSE values for SO2, CO, NO2 and O3 forecasts were 43.4%, 19.5%, 26.4% and 27.7% lesser for our GRUED model in comparison to LSTM model. The effectiveness and optimal performance of the suggested approach has been established experimentally.