# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 4 Methods
### Data Compression
This feature effectively guards against malicious nodes supplying erroneous data, significantly reducing redundancy while ensuring secure, complete data dissemination, thereby preventing resource waste from invalid storage. To resolve transaction data length inconsistencies, the R-ABC scheme includes transaction normalization, dividing transactions into fixed-size data blocks for compression. Variable transaction sizes can cause inefficiencies in storage; however, normalization standardizes block sizes, ensuring uniform compression. This method accommodates transactions of varying lengths, further optimizing storage utilization. The experimental analysis validated the R-ABC scheme's efficacy in reducing storage demands, enhancing data availability, decreasing network transmission overhead, and protecting against adversarial node interference. [PERSON] et al. [99] proposed a Compress-Storage Architecture that applies data compression techniques to blockchain storage, particularly suitable for multimedia data scenarios such as video surveillance. This architecture combines data compression with blockchain technology through a consensus mechanism called \"Proof-of-WorkStore\" (PoWS), achieving decentralized data processing and immutable storage. Specifically, the system begins with miner nodes compressing the received video data, where the compression process includes transformation, quantization, and entropy encoding steps. This aims to reduce data volume while ensuring that the quality of the compressed data meets predefined standards such as the Peak Signal-to-Noise Ratio (PSNR). The compressed data are not stored directly on the blockchain; instead, they are kept in a Distributed Hash Table (DHT) system, with only metadata (such as hash values of data locations, compression algorithms, and parameters, timestamps, etc.) retained on-chain. This approach preserves the immutability of the blockchain while addressing the challenges of large-scale data storage. The PoWS mechanism requires miners to provide proof of storage while performing compression tasks, ensuring that the compressed data are correctly stored at the designated location. Miners are rewarded for compressing the data, which not only facilitates data compression but also incentivizes miners to offer storage space. Verification nodes in the network check whether the compressed data provided by miners meets quality standards and verify that the storage nodes indeed retain the compressed data. Upon successful validation, corresponding blocks are added to the blockchain, ensuring data integrity and security. Through this method, the system significantly reduces storage space requirements while leveraging the decentralized and immutable characteristics of blockchain to provide an efficient and secure solution for multimedia data storage. In blockchain-based decentralized storage, [PERSON] [100] proposed the EDCOMA scheme, which significantly reduces storage redundancy and storage overhead on nodes through a dual compression method and a lightweight random challenge mechanism. EDCOMA employs two stages: data block compression (D-compression) and polynomial compression (P-compression), gradually compressing data volumes to ultimately generate an efficient and lightweight data authenticator. In the data block compression stage, files are divided into multiple data blocks, which are then grouped according to the compression ratio, generating simplified compression coefficients that reduce the initial data size. In the polynomial compression stage, EDCOMA combines these compression coefficients into a polynomial and calculates a polynomial commitment at a random point to create the data authenticator. This authenticator not only represents multiple compressed data blocks but also significantly reduces the storage needs of nodes, enabling the system to maintain data integrity while greatly lowering storage redundancy. The experimental results verify EDCOMA's significant advantages in optimizing storage overhead.