Relay Mining: Incentivizing Full Non-Validating Nodes Servicing All RPC Types

TL;DR

Relay Mining introduces a scalable, crypto-economic framework for incentivizing full non-validating RPC reads in a permissionless network. It combines commit-and-reveal proofs with a Sparse Merkle Sum Trie and a ClosestMerkleProof scheme to verifiably accumulate and prove read-work, while dynamically modulating difficulty based on observed relay volumes via an EMA-like feedback loop. The approach maps read traffic management to on-chain parameters (e.g., $r$ and $b$), enabling multi-tenant, geopolitically distributed RPC services without centralized Gateways. Experimental analysis on Pocket Network data demonstrates the method’s viability at high volumes, with acceptable bias and robust performance under diverse traffic scenarios, suggesting a practical path toward decentralized, verifiable read RPCs in Web3.

Abstract

Relay Mining presents a scalable solution employing probabilistic mechanisms, crypto-economic incentives, and new cryptographic primitives to estimate and prove the volume of Remote Procedure Calls (RPCs) made from a client to a server. Distributed ledgers are designed to secure permissionless state transitions (writes), highlighting a gap for incentivizing full non-validating nodes to service non-transactional (read) RPCs. This leads applications to have a dependency on altruistic or centralized off-chain Node RPC Providers. We present a solution that enables multiple RPC providers to service requests from independent applications on a permissionless network. We leverage digital signatures, commit-and-reveal schemes, and Sparse Merkle Sum Tries (SMSTs) to prove the amount of work done. This is enabled through the introduction of a novel ClosestMerkleProof proof-of-inclusion scheme. A native cryptocurrency on a distributed ledger is used to rate limit applications and disincentivize over-usage. Building upon established research in token bucket algorithms and distributed rate-limiting penalty models, our approach harnesses a feedback loop control mechanism to adjust the difficulty of mining relay rewards, dynamically scaling with network usage growth. By leveraging crypto-economic incentives, we reduce coordination overhead costs and introduce a mechanism for providing RPC services that are both geopolitically and geographically distributed. We use common formulations from rate limiting research to demonstrate how this solution in the Web3 ecosystem translates to distributed verifiable multi-tenant rate limiting in Web2.

Figures (13)

• Figure 1: Communication between a dApp with permissionless and untrusted full nodes (left) versus trusted and centralized full nodes (right).
• Figure 2: The RPC Trilemma
• Figure 3: Interaction between the Application, Servicers and World State before, during and after a Session.
• Figure 4: Four Bit Sparse Merkle Sum Trie
• Figure 5: Bias (left) and variability (right) for each sample point in the experiment. The X and Y axis are in logarithmic scale.