# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 4 Methods
### Erasure Coding
When a full node needs access to a block not stored locally, it connects to a small number of helper nodes, allowing the precise regeneration of the target block by downloading minimal data. This method substantially reduces both storage and recovery overhead. Experiments indicate that, compared to traditional multi-replica storage and storage schemes based on RS or LDPC codes, the ER-MSR-based storage scheme not only lowers storage costs but also effectively reduces communication and computational overhead during block recovery, enhancing the storage efficiency and scalability of blockchain systems. This makes it well-suited to handle the increasing node count and data volume in expanding blockchain networks. Erasure coding techniques can also be applied in Internet of Things (IoT) environments. With the rapid increase in IoT devices, blockchain storage demands continue to grow. In IoT scenarios, newly generated data are frequently accessed initially but experience a significant drop in access frequency over time. Traditional blockchain storage strategies, which often rely on full replication, ensure high data availability but lead to considerable storage overhead and data redundancy. To address these challenges, [PERSON] et al. [88] proposed MTEC, a Multi-tier Blockchain Storage Framework using erasure coding, to optimize storage efficiency and reduce redundancy. The MTEC framework organizes nodes into multiple storage tiers based on availability, allowing data storage across different layers. Each layer's nodes store portions of the blockchain data and are configured with specific block deletion threshold times. This design enables nodes to adjust storage strategies flexibly without compromising data availability. MTEC effectively utilizes this feature by setting optimal threshold times to ensure efficient data storage and fast access. The framework categorizes nodes into multiple tiers based on performance and availability, allocating distinct storage tasks according to resource capacity. In this architecture, lower-tier nodes, which have limited resources, store partial block data, while higher-tier nodes store more extensive data. By applying RS encoding at different tiers and adjusting encoding parameters dynamically based on node resources, the system achieves a balance between storage efficiency and fault tolerance. This approach prevents redundant network-wide storage through efficient data allocation, significantly enhancing storage efficiency. To ensure each tier maintains at least 99% data availability, MTEC sets an upper limit on the number of stored blocks. Additionally, for locally stored blocks that remain not accessed beyond the specified threshold time, deletion occurs to conserve storage space. If deleted blocks are needed later, they must be retrieved from other nodes, which introduces a trade-off between storage overhead and network bandwidth. Through this design, MTEC reduces storage overhead while ensuring data availability. Experimental results show that MTEC outperforms traditional storage solutions in terms of storage efficiency and query performance, achieving a storage overhead reduction of 86.3%. These results demonstrate that MTEC holds significant practical value and potential for meeting IoT application requirements. Table 4 illustrates the effectiveness of leveraging erasure coding technology to further reduce storage redundancy in blockchain systems. The table lists applications of this technology in various scenarios.