1. Background and Problems

• Sending data over unstable networks often results in data loss or delay.

• Traditional solutions such as retransmission (TCP) can worsen network congestion.

• Erasure codes' add redundancy to recover lost data without retransmission, suitable for unreliable networks.

1. Erasure Coding Approaches

a. Block Erasure Codes:

• Example: Reed-Solomon (RS).

• Breaks data into small pieces and adds systematic redundancy.

• Requires at least a certain number of fragments for data reconstruction.

• Used in RAID, optical media, and QR Code.

• Not suitable for dynamic networks because of rigid implementation.

b. Fountain Codes:

• Example: LT Codes, Raptor Codes.

• Produces an infinite stream of coded packets.

• Suitable for scenarios with unpredictable loss rates.

• Optimal for one-to-many communications such as multimedia streaming.

c. Random Linear Network Coding (RLNC):

• Increases flexibility by choosing random coefficients in the encoding.

• Packets can be regenerated without prior decoding (recoding).

• Suitable for dynamic and decentralized networks such as Web3, IoT, and 5G.

1. Advantages of RLNC Compared to Other Approaches

a. Tolerance to data loss:**

• RLNC is able to cope with cumulative loss in multi-hop networks, where block and fountain codes fail.

b. Recoding:

• Intermediate nodes can regenerate coded packets without decoding, reducing overhead and improving efficiency.

• Adaptability to dynamic networks:

• RLNC does not require synchronization and can adapt in real-time to changing network conditions.

• Scalability:

• RLNC maintains constant throughput even as the number of hops increases, making it ideal for large and decentralized networks.

1. Conclusion

• RLNC excels in various aspects (loss tolerance, efficiency, and adaptability) compared to block code and fountain.

• Due to its scalability, RLNC is the best choice for Web3 applications and other decentralized networks.

1. Background and Problems

• Sending data over unstable networks often results in data loss or delay.

• Traditional solutions such as retransmission (TCP) can worsen network congestion.

• Erasure codes' add redundancy to recover lost data without retransmission, suitable for unreliable networks.

1. Erasure Coding Approaches

a. Block Erasure Codes:

• Example: Reed-Solomon (RS).

• Breaks data into small pieces and adds systematic redundancy.

• Requires at least a certain number of fragments for data reconstruction.

• Used in RAID, optical media, and QR Code.

• Not suitable for dynamic networks because of rigid implementation.

b. Fountain Codes:

• Example: LT Codes, Raptor Codes.

• Produces an infinite stream of coded packets.

• Suitable for scenarios with unpredictable loss rates.

• Optimal for one-to-many communications such as multimedia streaming.

c. Random Linear Network Coding (RLNC):

• Increases flexibility by choosing random coefficients in the encoding.

• Packets can be regenerated without prior decoding (recoding).

• Suitable for dynamic and decentralized networks such as Web3, IoT, and 5G.

1. Advantages of RLNC Compared to Other Approaches

a. Data loss tolerance:**

• RLNC is able to cope with cumulative loss in multi-hop networks, where block and fountain codes fail.

b. Recoding:

• Intermediate nodes can regenerate coded packets without decoding, reducing overhead and improving efficiency.

• Adaptability to dynamic networks:

• RLNC does not require synchronization and can adapt in real-time to changing network conditions.

• Scalability:

• RLNC maintains constant throughput even as the number of hops increases, making it ideal for large and decentralized networks.

1. Conclusion

• RLNC excels in various aspects (loss tolerance, efficiency, and adaptability) compared to block and fountain codes.

• Due to its scalability, RLNC is the best choice for Web3 applications and other decentralized networks.