No abstract

Today, the Internet of Things (IoT) is used for convenience in everyday life in many areas. Owing to the fact that the data collected from the 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 the 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. INDEX TERMS Access control, computer security, cryptography, data security, data storage systems, distributed computing, Internet of Things.

A virtual machine with a conventional offloading scheme transmits and receives all context information to maintain program consistency during communication between local environments and the cloud server environment. Most overhead costs incurred during offloading are proportional to the size of the context information transmitted over the network. Therefore, the existing context information synchronization structure transmits context information that is not required for job execution when offloading, which increases the overhead costs of transmitting context information in low-performance Internet-of-Things (IoT) devices. In addition, the optimal offloading point should be determined by checking the server’s CPU usage and network quality. In this study, we propose a context management method and estimation method for CPU load using a hybrid deep neural network on a cloud-based offloading service that extracts contexts that require synchronization through static profiling and estimation. The proposed adaptive offloading method reduces network communication overheads and determines the optimal offloading time for low-computing-powered IoT devices and variable server performance. Using experiments, we verify that the proposed learning-based prediction method effectively estimates the CPU load model for IoT devices and can adaptively apply offloading according to the load of the server.

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.

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.