# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 4 Methods
### Data Compression  
In blockchain storage technology, data compression is a key technique [95] to address the storage and transmission challenges posed by the continuous growth of blockchain data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Solution** & **Technology** & **Storage Efficiency** \\ \hline Dorsala [91] & Deduplication & Hash value comparison, smart contracts & High \\ Zhang [92] & BDKM & Block-level deduplication & High \\ Xiao [93] & ESDedup & Similarity computation, smart contracts & High \\ Zhang [94] & MLE & Enhanced the authenticator generation algorithms & High \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data deduplication for reducing storage redundancy. Particularly in public blockchains like Bitcoin, the accumulation of data over time leads to a significant increase in blockchain size, resulting in high consumption of storage resources. Data compression techniques optimize storage space utilization by encoding specific fields within blockchain data. For example, in the context of the Bitcoin blockchain, compression methods re-encode fields such as transaction version numbers, referenced transaction hashes, and transaction output indices to reduce their byte usage [96]. By calculating the entropy (information content) of these fields, compression algorithms employ variable-length encoding to replace fixed-length fields, thereby maximizing compression efficiency and reducing storage redundancy [97]. This approach not only alleviates storage pressure on blockchain full nodes but also improves network synchronization efficiency and data transmission performance. Furthermore, these techniques ensure that data compression preserves blockchain security and data integrity, maintaining its core characteristics of decentralization and transparency. [PERSON] et al. [98] introduced an RNS-based Adaptive Block Compression (R-ABC) scheme, which leverages the Residue Number System (RNS) to achieve substantial storage redundancy reduction in blockchain systems, with a particular emphasis on constrained-resource environments like the Industrial Internet of Things (IIoT). This scheme integrates distributed storage with a dynamic compression mechanism, effectively decreasing storage overhead while maintaining data integrity and auditability. By employing RNS, transaction data are transformed into remainder under multiple moduli, eliminating the necessity for full transaction record storage. In conventional blockchain architectures, every node must store complete blocks and transaction data, leading to untenable storage demands as on-chain transaction volumes escalate. R-ABC addresses this by representing transaction data through multiple remainder distributed across nodes, with each node retaining only a subset. Using the Chinese Remainder Theorem (CRT), nodes can reconstruct full transaction data by obtaining any missing remainder from other nodes as required. This design not only alleviates storage strain on individual nodes but also preserves data completeness and traceability. The R-ABC scheme further introduces an adaptive compression mechanism that allows nodes to tailor the quantity of stored remainder based on their available storage capacity. Nodes with greater storage resources can retain more residues, while nodes with limited resources may store fewer, optimizing resource allocation. This adaptive feature is reinforced with a resource-utilization incentive, wherein nodes that store a greater share of the remainder gain independence during data reconstruction, reducing reliance on inter-node requests. To enhance data security and minimize storage redundancy, the R-ABC scheme integrates a Bloom filter within the block header, enabling efficient verification of residue integrity. The Bloom filter stores commitment values for all transaction residue sets, allowing nodes to verify data validity when retrieving the remainder from other nodes.