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DBTMA relies entirely on RTS/CTS dialogue for un-collided transmission of data. The purpose is to improve the QoS at MAC layer by developing it over 802.11e standard. However, DBTMA does not guarantee real-time constraints without efficient method for controlling the network loads. The main challenges in MANETs include prediction of the available bandwidth, establishing communication with neighboring nodes and predicting the consumption of bandwidth flow. These challenges are provided with solutions using Contention-Aware Admission Control (CACP) protocol. In this paper, the EDBTMA protocol is combined with CACP protocol that introduces bandwidth calculation using admission control strategy. The calculation includes certain metrics like: admission control and bandwidth consumption. To compute the bandwidth of channel, bandwidth utilization and traffic priority is distinguished through dual busy tone is proposed. This operates distinctly on its own packet transmission operation. This CACP mechanism defends the conventional traffic flows from new nodes and based on the measured status information of the channel, it QoS of the admitted flows is maintained. This ensures maximum amount of bandwidth flows accommodated by resources and determines the resources in a system meet the new flow requirements while maintaining existing bandwidth flow levels.

Focusing on the issue of host load estimating in mobile cloud computing, the Long Short Term Memory networks (LSTM)is introduced, which is appropriate for the intricate and long-term arrangement information of the cloud condition and a heap determining calculation dependent on Glowworm Swarm Optimization LSTM neural system is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are in prediction accuracy higher than equivalent prediction algorithms.

Summary The ability of the Cloud to share information and provide certain services to the network linked people. The resources are provided according to the need of the user. In cloud, the user's data are moved to the large data storage, which must be secured. The various organization have expressed concerns about security aspects of cloud computing. One of the major perspectives is to provide security to one's data, which is stored remotely from the user's location. This paper describes an enhanced approach for the already used data security model in cloud environment. The proposed data security model includes generation of OTP using HMAC (Hash based message authentication code) for user authentication process. This paper also includes a comparative MD5 and SHA algorithms for the better implementation of the model. This model best suits for any of the layers in it, to achieve this we use certain encryption algorithms that convert original text to the form that is not understood by the third party. Finally, data availability can be considered as a major concern, which is viewed as threat associated with the cloud environment. To overcome this problem, we generally replicate our data and store it in various locations.

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