The rise of mining pools in Blockchain networks has improved reward distribution but introduced critical challenges related to centralization and malicious miner activity, which threaten the integrity of decentralized consensus. Addressing this gap, this paper proposes the Reputation?based Consensus Protocol (RCP), a novel framework designed to enhance trust and security in mining pools by incorporating a transparent and dynamic reputation system. Unlike traditional consensus algorithms like Proof of Work (PoW) and Proof of Stake (PoS), which do not differentiate between trustworthy and malicious participants, RCP evaluates miners based on a multi?dimensional scoring mechanism, including historical reputation, willingness reputation, and indirect feedback reputation. This targeted approach allows the network to prioritize reputable miners for block creation, thereby mitigating attacks and improving consensus reliability. By integrating RCP with modular Blockchain frameworks such as Hyperledger, this protocol not only strengthens miner accountability but also sets the foundation for more secure and trustworthy decentralized networks. The proposed model has the potential to redefine mining pool operations and significantly contribute to the evolution of secure Blockchain consensus protocols.

In blockchain technology, mining refers to the process of validating new transactions and adding them to a permanent public ledger called distributed ledger technology (DLT). While solo mining becomes challenging for individual miners due to limitations of solo miner's hash rate and reward consistency, miners typically opt to join mining pools to combine resources and increase reward consistency. Mining pools have significantly impacted the Proof of Work (PoW) consensus, which relies heavily on decentralized mining pools to secure and validate transactions on a blockchain network. It necessitates miners to solve complex mathematical puzzles to add new blocks to the blockchain. However, it is imperative to address the potential drawbacks of PoW consensus. Establishing trust among miners in PoW mining pools is challenging due to the inherent risks associated with the decentralized nature of Blockchain. In this study, a decision management system is developed by leveraging the characteristics of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), termed Strategic Miner Selection with TOPSIS (SMNST). Firstly, this SMNST evaluates the miner's ranks based on the decision criteria of the dynamic blockchain network. Secondly, the choice of block generation is further optimized through ranked miners only to reach an ideal solution in the pool consensus. Simulation results were demonstrated using Bitcoin Testnet3, and the effectiveness of miners in mitigating risks in PoW mining pools was analyzed through hash rate, latency, pool up-time, and block propagation time metrics.

Since the adoption of the Blockchain network by modern technologies, which benefit from its immutability and secure access provided by its consensus process, blockchain mining has gained popularity. Blockchain networks' miners rely on the consensus procedure to contribute data to the network. Miners use their resources to publish new blocks and receive rewards, and they participate in the mining process. Since solo mining might be difficult, most miners decided to join a mining pool. Several issues surrounding the miner's choice with pool mining remain unanswered because of the range of many mining pools and the potential adoption of various incentive systems. There are a few issues to consider, though, about maintaining a miner's trust throughout the consensus and mining processes. As part of the consensus, miners that are part of a mining pool contribute their processing power toward the task of adding a block. The pool will be rewarded if they are successful in their endeavors. To guarantee the execution of the consensus protocol in pool mining, a trust evaluation model is presented in our study. As a result, trust assessment among miners is formulated in this research it is evaluated as a classification problem, and an innovative approach using machine learning is offered to classify trust. First, the vector for the trust features is built using the parameters of the Blockchain. A trust classifier can then be created by training it with samples of data that miners have acquired and that contain transaction activities. Also, the provided Trust model verifies that the total computing power of the pool for which miners are requested and the miner's computation power should be 50 percent greater than or equal to the total computation power this is for to mitigate attacks brought on by untrusted miners through forks in the Blockchain. The Hyperledger tool was used as the framework to assure robustness in analyzing transaction delay, and transaction throughput, and to achieve improved Blockchain performance. The research was carried out to demonstrate the viability of our Trust Model and to show that the trained trust classifier has comparatively high processing power.