Mobile Cloud computing is a technology of delivering services, such as software, hardware (virtual as well) and bandwidth over the Internet. Mobile devices are enabled in order to explore, especially Smart phones. The mobile cloud computing technology is growing rapidly among the customers and many companies such as Apple, Google, Facebook and Amazon with rich users. Users can access their data at any time, at any place, even with any device including mobile devices by using the cloud storage services, although these properties offer flexibility and scalability in controlling data, however, at the same time it reminds us with new security threats. These security issues can be resolved by proper handling of data. The cloud server provider can secure the data by applying the encryption and decryption techniques while storing the data over the cloud. In this paper, we proposed some encryption and decryption methods for securing the data over the cloud so that an unauthorized person or machine cannot access the confidential data owing to encrypted form.

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These days, the usage of machine-learning-enabled dynamic Internet of Medical Things (IoMT) systems with multiple technologies for digital healthcare applications has been growing progressively in practice. Machine learning plays a vital role in the IoMT system to balance the load between delay and energy. However, the traditional learning models fraud on the data in the distributed IoMT system for healthcare applications are still a critical research problem in practice. The study devises a federated learning-based blockchain-enabled task scheduling (FL-BETS) framework with different dynamic heuristics. The study considers the different healthcare applications that have both hard constraint (e.g., deadline) and resource energy consumption (e.g., soft constraint) during execution on the distributed fog and cloud nodes. The goal of FL-BETS is to identify and ensure the privacy preservation and fraud of data at various levels, such as local fog nodes and remote clouds, with minimum energy consumption and delay, and to satisfy the deadlines of healthcare workloads. The study introduces the mathematical model. In the performance evaluation, FL-BETS outperforms all existing machine learning and blockchain mechanisms in fraud analysis, data validation, energy and delay constraints for healthcare applications.

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Nowadays, the usage of mobile devices is progressively increased. Until, delay sensitive applications (Augmented Reality, Online Banking and 3D Game) are required lower delay while executed in the mobile device. Mobile Cloud Computing provides a rich resource environment to the constrained-resource mobility to run above mentioned applications, but due to long distance between mobile user application and cloud server introduces hybrid delay (i.e., network delay and process delay). To cope with the hybrid delay in mobile cloud computing for delay sensitive applications, we have proposed novel hybrid delay task assignment (HDWA) algorithm. The preliminary objective of the HDWA is to run the application on the cloud server in an efficient way that minimizes the response time of the application. Simulation results show that proposed HDWA has better performance as compared to baseline approaches.