In the modern era of creating and consuming digital content, efficient, and scalable video processing and archiving systems are essential. This paper explores and leverages the broad and extensive functionalities of the Amazon Web Services (AWS), that aim to streamline video processing workflows, enhance content delivery, and ensure cost-effective long-term storage. The paper utilizes the Amazon Simple Storage Service (S3) as the primary storage, AWS Lambda to automate workflow and efficiently sends transcoding jobs to the Amazon Elastic Transcoder where it processes the video files into its optimal formats, ensuring high quality transcoded videos. Additionally, the Amazon Glacier is incorporated for archiving the infrequently accessed videos, where the lifecycle policy feature automates the transition after 30 days, providing durable and secure storage solution. The adoption of Amazon CloudFront significantly improves the end-user experience by reducing latency and secure access to the processed videos. The integration of AWS managed services in this paper results in a scalable, secure and cost-effective solution for video processing and archiving in the cloud.

As global and local efforts tackle energy consumption and environmental sustainability, it is crucial to conduct detailed studies on energy demand. This study investigated the effects of wind, relative humidity, temperature, precipitation, and the number of operating days on the monthly energy consumption of a specific building using statistical techniques such as Pearson correlation analysis and time series modeling. Seasonal-trend decomposition using LOESS (STL) was utilized to model the deterministic component in the data and seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models to further capture the seasonality of energy consumption while taking account of the external effects of weather and operational factors. The forecasting accuracy of the models was benchmarked to naive modeling in terms of normalized Root Mean Squared Error (nRMSE) and Mean Absolute Error (nMAE), Mean Absolute Percentage Error (MAPE), and Skill Score (SS). The results indicate that among the exogenous variables, only the number of operating days significantly correlates with the target variable. Ensemble technique and inclusion of operating days, wind speed, ambient temperature, and total precipitation in the models significantly enhanced the forecasting accuracy. Consequently, the STL-Ensemble 2 model provides optimal forecasting accuracy in predicting building energy consumption with 8.65% nRMSE, 6.84% nMAE, and 7.92% MAPE, which is far superior to the naive model with 27.45% nRMSE, 24.07% nMAE, and 27.75% MAPE, and STL-SARIMA with 10.03% nRMSE, 8.67% nMAE, and 10.21% MAPE. Future research can use more granular data resolution and further explore advanced forecasting methods such as machine learning techniques to achieve improved model performance and realized effects of operational and weather variables.

In opportunistic networks, no end-to-end path is available from source to destination due to frequent movement of nodes with high speed. In such type of networks, transmission takes places between nodes during a contact event. These types of networks, follow store-carry and forward mechanism to forward messages from source to destination, Intermediate node stores messages into buffer and carries these messages until it meets another node. Already existing flooding protocols like epidemic may congest the network due to excessive flooding of the messages over the network. Replication based routing protocols introduces in which messages are replicating according to quota value. The replication based protocols have some limitations like delay which degrades the performance of network. Our proposed technique overcome the limitations of replication based routing protocols. Proposed technique provides replication based forwarding with optimal buffer management to increase the delivery ratio and minimize the delay. Extensive simulation of proposed technique is done in ONE simulator with different scenarios and comparing result of proposed scheme with already existing schemes such as epidemic, Rep-nodes and Spray & Wait. Result shows that our scheme has outperform as compare to already existing schemes in terms of delivery ratio and delay.