The Coronavirus pandemic and the work-from-anywhere has created a shift toward cloud-based services. The pandemic is causing an explosion in cloud migration, expected that by 2025, 95% of workloads will live in the cloud. One of the challenges of the cloud is data security. It is the responsibility of cloud service providers to protect user data from unauthorized access. Historically, a third-party auditor (TPA) is used to provide security services over the cloud. With the tremendous growth of demand for cloud-based services, regulatory requirements, there is a need for a semi to fully automated self sovereign identity (SSI) implementation to reduce cost. It's critical to manage cloud data strategically and extend the required protection. At each stage of the data migration process, such as data discovery, classification, and cataloguing of the access to the mission-critical data, need to be secured. Cloud storage services are centralized, which requires users must place trust in a TPA. With the SSI, this can become decentralized, reducing the dependency and cost. Our current work involves replacing TPA with SSI. A cryptographic technique for secure data migration to and from the cloud using SSI implemented. SSI facilitate peer-to-peer transactions, meaning that the in-between presence of TPA needs no longer be involved. The C2C migration performance is recorded and found the background or foreground replication scenario is achievable. Mathematically computed encrypted and decrypted ASCII values for a word matched with the output by the algorithm. The keys generated by the algorithm are validated with an online validator to ensure the correctness of the generated keys. RSA based mutual TLS algorithm is a good option for SSI based C2C migration. SSI is beneficial because of the low maintenance cost, and users are more and more using a cloud platform. The result of the implemented algorithm shows that the SSI based implementation can provide a 13.32 Kbps encryption/decryption rate which is significantly higher than the TPA method of 1 Kbps.
Affective Computing is one of the central studies for achieving advanced human-computer interaction and is a popular research direction in the field of artificial intelligence for smart healthcare frameworks. In recent years, the use of electroencephalograms (EEGs) to analyze human emotional states has become a hot spot in the field of emotion recognition. However, the EEG is a non-stationary, non-linear signal that is sensitive to interference from other physiological signals and external factors. Traditional emotion recognition methods have limitations in complex algorithm structures and low recognition precision. In this article, based on an in-depth analysis of EEG signals, we have studied emotion recognition methods in the following respects. First, in this study, the DEAP dataset and the excitement model were used, and the original signal was filtered with others. The frequency band was selected using a butter filter and then the data was processed in the same range using min-max normalization. Besides, in this study, we performed hybrid experiments on sash windows and overlays to obtain an optimal combination for the calculation of features. We also apply the Discrete Wave Transform (DWT) to extract those functions from the preprocessed EEG data. Finally, a pre-trained k-Nearest Neighbor (kNN) machine learning model was used in the recognition and classification process and different combinations of DWT and kNN parameters were tested and fitted. After 10-fold cross-validation, the precision reached 86.4%. Compared to state-of-the-art research, this method has higher recognition accuracy than conventional recognition methods, while maintaining a simple structure and high speed of operation.
There are thousands of flights carrying millions of passengers each day, having three or more Internet-connected devices with them on average. Usually, onboard devices remain idle for most of the journey (which can be of several hours), therefore, we can tap on their underutilized potential. Although these devices are generally becoming more and more resourceful, for complex services (such as related to machine learning, augmented/virtual reality, smart healthcare, and so on) those devices do not suffice standalone. This makes a case for multi-device resource aggregation such as through femto-cloud. As our first contribution, we present the utility of femto-cloud for aerial users. But for that sake, a reliable and faster Internet is required (to access online services or cloud resources), which is currently not the case with satellite-based Internet. That is the second challenge we try to address in our paper, by presenting an adaptive beamforming-based solution for aerial Internet provisioning. However, on average, most of the flight path is above waters. Given that, we propose that beamforming transceivers can be docked on stationery ships deployed in the vast waters (such as the ocean). Nevertheless, certain services would be delay-sensitive, and accessing their on-ground servers or cloud may not be feasible (in terms of delay). Similarly, certain complex services may require resources in addition to the flight-local femto-cloud. That is the third challenge we try to tackle in this paper, by proposing that the traditional fog computing (which is a cloud-like but localized pool of resources) can also be extended to the waters on the ships harboring beamforming transceivers. We name it Floating Fog. In addition to that, Floating Fog will enable several new services such as live black-box. We also present a cost and bandwidth analysis to highlight the potentials of Floating Fog. Lastly, we identify some challenges to tackle the successful deployment of Floating Fog.