This study examined the requirements for privacy-preserving and interoperability in healthcare data sharing and proposed a blockchain-based solution. The Hyperledger Fabric framework was adopted due to its enterprise-grade data processing capabilities and enhanced privacy protection functions. In addition to the Fabric’s built-in privacy-preserving functions, healthcare data-specific smart contracts with hierarchical access control were developed to strengthen privacy protection in data sharing. The proposed healthcare data-sharing framework is based on Australian medical practices with the aim to upgrade, rather than to replace, the existing data management models. The outcome of this study demonstrates the feasibility of applying blockchain technology to improve privacy-preservation while enhancing interoperability in healthcare data management.

Autonomous moving objects (AMOs), such as automated guided vehicles (AGVs) and autonomous robots, have widely been used in the industry for decades. In an intelligent transport system with a great number of AMOs involved, it is important to eliminate potential congestion and deadlocks among AMOs to maintain a well-organized traffic flow. In this paper, we propose an algorithm that adapts bitonic merge sort algorithm for concurrent scheduling and routing of a great number (i.e., 4n ²) of AMOs on an ntimesn mesh topology of path network without congestion or deadlocks among AMOs during their moves. The results are tested by experiments with randomly generated data and the comparison of a related model.