A learning-based efficient query model for blockchain in internet of medical things

This paper proposes a learning-based model for the resource-constrained edge nodes in the blockchain-enabled Internet of Medical Things (IoMT) systems to realize efficient querying. Three layers are designed in the new model: data evaluation layer, data storage layer and data distribution layer. The data evaluation layer extracts the features from medical data and evaluates their values based on the Extreme Learning Machine (ELM) method. Then, in the data storage layer, according to the value of medical data, a novelty data structure called Merkle–Huffman tree (M-H tree) is established. Compared with the Merkle tree, high-value data (frequently accessed data) in M-H tree is saved closer to the root node and can be found faster. In the data distribution layer, the sharding-based blockchain model is adopted to increase the storage scalability of the IoMT system. Finally, the experimental results show that the new learning-based model can effectively improve the query speed of the blockchain-enabled medical system by about 3.5% and free up large amounts of storage space on IoMT devices.