Blockchain data encoding has been proposed as a solution to reduce the storage load on full nodes in blockchain systems. However, the high latency and cost associated with the decoding process make coding techniques difficult to apply to frequently accessed blockchain data. This limitation is particularly significant in blockchain applications for the Internet of Things, where nodes generally have limited storage capabilities. To address this issue, we propose a novel blockchain encoding storage architecture. We classify the frequently used state data in blockchain into hot data and cold data, with hot data stored normally and cold data encoded. In addition, we designed a new transaction processing mechanism that prioritizes transactions involving specific data within a single block production process, thereby reducing the frequency of data decoding. At the same time, we use erasure codes and Merkle trees to ensure the security of the stored data. Experimental results show that in our approach, the account data storage on full nodes is significantly lower than that of normal full nodes, while the block transaction volume and block generation time are not substantially different from the normal state. Finally, we verify the robustness of our method against node dropouts through experiments.

Smart contracts are programs that permanently store and automatically execute on the blockchain system such as Ethereum. Due to the non-tamperable nature of the underlying blockchain, smart contracts are difficult to update once deployed, which requires redeploying the contracts and migrating the data. It means that the observation of smart contract evolution in the real world makes more sense. Hence, in this paper, we conducted the first large-scale empirical study to characterize the evolution of smart contracts in Ethereum. For evolution identification, we presented a contract similarity-based search algorithm, digEvolution, and evaluated its effectiveness with five different search strategies. Then we applied this algorithm to 80,152 on-chain contracts we collected from Ethereum, to dig out the evolution among these contracts. We then explored three research questions. We first studied whether the evolution of smart contracts is common (RQ1), then we studied how do the Gas consumption (RQ2) and the vulnerability (RQ3) of smart contracts vary during the evolution. Our research results show that the evolution of smart contracts is not very common. There are some contract components that have vulnerability but still be called by users. The Gas consumption of most smart contracts doesn’t vary during the evolution, contract is Gas-efficient before and after the evolution. The vulnerability of most smart contracts doesn’t vary during the evolution, both are secure before and after the evolution.

Although smart contracts are immutable once they are deployed, the reality is that they need upgrades to fix bugs or add new features. Nowadays, there are a few upgrade methods in Ethereum, some of which can change the contract without changing the contract address that users interact with. This upgrade way increases potential danger and results in users' distrust, because it may secretly change the function of the contract and cause users financial loss. We examine two of these upgrade methods, i.e., proxy pattern and metamorphic contract. For the proxy pattern, we propose a bytecode-based method for detecting these upgradeable contracts, which achieves a 99.37% F1-score. We use the bytecode-based method to detect the contracts in the first 12 million blocks of Ethereum and find 126,500 upgradeable contracts. For the metamorphic contracts, we employ an Ethereum replay tool to replay the transactions and find the metamorphic contracts according to the SELFDESTRUCT and CREATE2 instructions. We find that 64.3% of the contracts upgraded using this way are malicious MEV bots. Finally, we summarize the reasons for smart contract upgrades and make development recommendations.