Cloud computing is a significant model for permitting on-demand network access to shared data, software's, infrastructure, and platform resources. However, cloud storage needs a certain level of availability, confidentiality, and integrity. Information sensitivity and value encourage users to select a highly secure protocol. This work proposes a new mechanism to increase the user trust in cloud computing using the secret sharing technique. The proposed algorithm is using Base64 encoding to convert any file type to ASCII strings. Base64 strings do not need any extra process to be compressed and this can speed up the share building process. Each string is used to produce N shares (using Shamir Secret Sharing Scheme) where each share is stored in a separate location in the cloud.
Massive multiple-input-multiple-output (MIMO) systems support advanced applications with high data rates, low latency, and high reliability in next-generation mobile networks. However, using machine learning in massive MIMO resource allocation is challenging due to quality of service (QoS) and network complexity across layers. This work presents a novel framework for adapting the scheduling and antenna allocation in massive MIMO systems using deep reinforcement learning (DRL). Rather than directly assigning execution parameters, the proposed framework utilizes DRL to select combinations of algorithms based on the current traffic conditions. The DRL model is trained using a specialized Markov decision process (MDP) model with a componentized action structure and is evaluated in realistic traffic scenarios. The results show that the proposed framework increases satisfied users by 7.2% and 12.5% compared to static algorithm combinations and other cross-layer adaptation methods. This demonstrates the effectiveness of the framework in managing and optimizing resource allocation in a flexible and adaptable manner.
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