Over the past decade, there has been a significant surge in consumer application services and server connectivity, and this trend is expected to double in 2030. The primary contributors to the increased demand for network resources are devices connected through third-party service providers and mobile operators. Many prominent consumer services rely on a client-server architecture, which can introduce latency delays in the communication channel. Additionally, peer-to-peer (P2P) communication places a substantial load on eHealth servers, leading to service delays. In this research paper, we propose a model for scheduling heterogeneous workloads and recommending resources for eHealth edgecloud connectivity using Federated Split Learning (FSL) model for smart cities. Distributed FSL offers a robust solution for handling both direct and indirect user requests through a distributed mobile core operator stack. This technique empowers eHealth administrators to locally learn optimal policies and make informed decisions by prioritizing resource allocation and scheduling. We demonstrate the effectiveness of this technique through an active simulation server designed for track-driven caching policy and local policy scheduling, ultimately enhancing resource recommendation in eHealth applications. The proposed technique is focused on the development of a heterogeneous workload recommendation system and obtained accuracy of 89.63% over 200 users trails.

The problem of transparency in donation systems has long been a topic for discussion. However, the emphasis on transparency raises privacy concerns for donors and recipients, with some people attempting to hide donations or the receipt of money. Therefore, a donation system that guarantees transparency and privacy is required to avoid negative side effects. In this study, we developed a system that protects personal information by using a one-time account address system based on a blockchain while emphasizing transparency. The developed system could contribute to the creation of a sustainable and safe donation environment and culture.

Today, the Internet of Things (IoT) is used for convenience in everyday life in many areas. Because data collected from IoT are generated in large quantities, cloud computing is inevitably used to store and analyze the data. However, cloud storage is not owned by the user, so it is unreliable. Verifying the integrity of data collected in an IoT environment and stored in a cloud has two problems: a large amount of data needs to be verified, and the data verification should be done directly on the IoT device. Many methods for data integrity verification use trusted third parties and devices that provide sufficient resources. However, it is difficult to directly apply existing research to IoT devices that have limited resources. This paper proposes a secure cloud storage service for an IoT environment that is based on a provable data possession model and uses Bloom filters. The experimental results showed that the proposed method saves time and has no significant differences in the verification rate with existing methods, even though the Bloom filter causes false positives. Therefore, the proposed service can effectively process a large amount of data generated in an IoT environment.

Korean education-related evaluation agencies utilize a centralized system that directly manages learner data. This leaves the intellectual property of the organizations and the personal information of the students vulnerable to leakage should the central server be attacked. In this study, the researchers propose a multilateral personal portfolio authentication system that guarantees the reliability, integrity, and transparency of the data such as learner's schooling history. The system uses the features of blockchain in a distributed network wherein a learner submits schooling data to a peer in the network. The data is then verified through an agreement among the peers and recorded in a chronologically encrypted ledger. The proposed system is implemented based on Hyperledger Fabric and the analysis thereof conducted by evaluating its processing speed, capacity, and security. The analysis indicates that the system is able to successfully intercept the transactions of unvalidated users, thereby preventing the recording of incorrect data by an unauthorized user. Furthermore, it provides a record of previously made changes to a learner's profile, thus improving the integrity and reliability of the data. This system provides a platform to share learner information safely and promptly among schools, certification authorities, and higher learning institutions.

As the sharing economic market grows, the number of users is also increasing but many problems arise in terms of reliability between providers and users in the processing of services. The existing methods provide shared economic systems that judge the reliability of the provider from the viewpoint of the user. In this paper, we have developed a system for establishing mutual trust between providers and users in a shared economic environment to solve existing problems. In order to implement a system that can measure and control users’ situation in a shared economic environment, we analyzed the necessary factors in a cyber physical system (CPS). In addition, a user measurement system based on a CPS structure in a sharing economic environment is implemented through analysis of the factors to consider when constructing a CPS.