As the typical peer-to-peer distributed networks, blockchain systems require each node to copy a complete transaction database, so as to ensure new transactions can by verified independently. In a blockchain system (e.g., bitcoin system), the node does not rely on any central organization, and every node keeps an entire copy of the transaction database. However, this feature determines that the size of blockchain transaction database is growing rapidly. Therefore, with the continuous system operations, the node memory also needs to be expanded to support the system running. Especially in the big data era, the increasing network traffic will lead to faster transaction growth rate. This paper analyzes blockchain transaction databases and proposes a storage optimization scheme. The proposed scheme divides blockchain transaction database into cold zone and hot zone using expiration recognition method based on Least Recently Used (LRU) algorithm. It can achieve storage optimization by moving unspent transaction outputs outside the in-memory transaction databases. We present the theoretical analysis on the optimization method to validate the effectiveness. Extensive experiments show our proposed method outperforms the current mechanism for the blockchain transaction databases.

With the rapid development of industrialization, the environmental pollution issue is becoming increasingly serious, especially the air pollution problem. As the core of the prevention and control of air pollution, air pollution prediction plays a very significant role in human survival and development. Therefore, it is highly essential to develop an accurate air pollution prediction model for mass rallies (e.g., playground and bazaar). Recent studies have suggested that multiple air contaminants, e.g., PM _2.5 and PM ₁₀ , which belong to a kind of aerosol, can carry the Covid-19 virus and spread it rapidly through the atmosphere, and this dramatically increases the risk of Covid-19 infection, particularly in the crowded and enclosed environment. Nevertheless, most existing air pollution prediction methods, which rely on large amounts of historical data for modeling and assume that the crowd flows relatively slow, are difficult to apply well to predict air pollution in mass rallies. To solve the aforementioned problem and better assist the decision-makers in managing environmental risk to human beings, in this article, we come up with a novel air pollution prediction model for mass rallies. More specifically, we first propose a temporally weighting matrix to differentiate the significance of training samples in the time domain. Then, we construct a temporal support vector regressor (TSVR), which puts more emphasis on the adjacent samples by considering the fact that the crowd usually flows promptly and disorderly in mass rallies. Finally, based on the extended TSVR, we develop a multitask TSVR (MTSVR) that simultaneously considers the related tasks. Since different air contaminants are correlated with each other, all the tasks can benefit by sharing information. The results of comparison experiments demonstrate that our presented MTSVR outperforms state-of-the-art single-task learners, multitask learners, and air pollution predictors when applied for air pollution prediction in mass rallies. Particularly, when under the six-task condition, the error values of the prediction of PM _2.5 , PM ₁₀ , and O ₃ obtained by our proposed method are relatively lower, outperforming the most advanced method tested by 15.2%, 6.1%, and 4.3%, and the precision values of the predicted values outperform the advanced method tested by 28.3%, 25.1%, and 24.8%.

Distributed storage can store data in multiple devices or servers to improve data security. However, in today's explosive growth of network data, traditional distributed storage scheme is faced with some severe challenges such as insufficient performance, data tampering, and data lose. A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed storage. Under this scheme, the following improvements have been made in this paper. This paper first analyzes the problems faced by distributed storage. Then proposed to build a new distributed storage blockchain scheme with sharding blockchain. The proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology, which can improve the efficiency of data verification between nodes. In addition, this paper uses polynomial commitment to construct a new verifiable secret share scheme called PolyVSS. This new scheme is one of the foundations for building our improved distributed storage blockchain scheme. Compared with the previous scheme, our new scheme does not require a trusted third party and has some new features such as homomorphic and batch opening. The security of VSS can be further improved. Experimental comparisons show that the proposed scheme significantly reduces storage and communication costs.

In the context of the increasingly diversified blockchain technology, interoperability among heterogeneous blockchains has become key to further advancing this field. Existing cross-chain technologies, while facilitating data and asset exchange between different blockchains to some extent, have exposed issues such as insufficient security, low efficiency, and inconsistent standards. Consequently, these issues give rise to significant obstacles in terms of both scalability and seamless communication among blockchains within a multi-chain framework. To address this, this paper proposes an efficient method for cross-chain interaction in a multi-chain environment. Building upon the traditional sidechain model, this method employs smart contracts and hash time-locked contracts (HTLCs) to design a cross-chain interaction scheme. This approach decentralizes the execution of locking, verifying, and unlocking stages in cross-chain transactions, effectively avoiding centralization risks associated with third-party entities in the process. It also greatly enhances the efficiency of fund transfers between the main chain and sidechains, while ensuring the security of cross-chain transactions to some extent. Additionally, this paper innovatively proposes a cross-chain data interaction strategy. Through smart contracts on the main chain, data from sidechains can be uploaded, verified, and stored on the main chain, achieving convenient and efficient cross-chain data sharing. The contribution of this paper is the development of a decentralized protocol that coordinates the execution of cross-chain interactions without the need to trust external parties, thereby reducing the risk of centralization and enhancing security. Experimental results validate the effectiveness of our solution in increasing transaction security and efficiency, with significant improvements over existing models. Our experiments emphasize the system's ability to handle a variety of transaction scenarios with improved throughput and reduced latency, highlighting the practical applicability and scalability of our approach.