This research presents an innovative method for enhancing security in pool mining consensus within Blockchain systems. Block mining is a demanding endeavor in decentralized systems, leading many miners to congregate within mining pools. The inherent traits of Blockchain technology, defined by decentralization and transparency, create a conducive environment for extensive mining pool participation. However, the inclusion of all the miners into mining raises concerns about miners' consensus reliability, malicious attacks, fraud detection rates, and variance in miners' incentives. These consequences necessitate the encouragement of trusted miners to pool consensus to ensure security. A clustering-based trust model in blockchain for pool mining consensus (CBTMB-PM) has been proposed to address the miner's trust dynamics and improve pool consensus efficiency. Miner's trust dynamics are assessed through clusters based on miners' behavioral attributes, which encompass historical performance, readiness, and indirect feedback recommendations of miners to improve overall pool reliability. Furthermore, a Cluster-Trust Proof of Work (CTPoW) consensus protocol has been proposed to enhance pool efficiency, ensuring that only reliable miners contribute to the consensus process by filtering out untrusted miners in the consensus process. Experimental and theoretical evaluations demonstrate the effectiveness of the CTPoW protocol. The effectiveness of the model is tested using a partially decentralized open-source Hyperledger Fabric framework for parameters such as block authorization time, validation time, block creation time, processing time, and confirmation time to analyze the practical changes observed by a group of miners through off-chain mode. It has excellent performance by comparison with some other state-of-the-art.

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.

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.

Blockchain networks continue to gain attraction in cutting-edge applications and mining within these networks has become increasingly popular. To get rewards, miners solve cryptographic puzzles and add new blocks to blockchain networks using the proof-of-work (PoW) consensus mechanism. Numerous miners opt to participate in mining pools due to the challenges of solo mining. However, selecting reputed miners for pool mining poses a significant challenge, given the decentralized nature of the blockchain system. This paper addresses this challenge by introducing a new ranking model that evaluates miners' performance and reputation through trust scores. It provides a method for optimizing pool mining performance by identifying highly reputed miners within mining pools, enhancing overall pool profitability. This endeavor necessitates the development of ranking algorithms tailored to the unique dynamics of mining pools. The research offers a meticulously designed ranking model that identifies reputed miners. We extensively evaluate the proposed model using the hyperledger blockchain framework, guaranteeing strong performance across vital metrics like block authorization time, Processing time, block creation time, validation time, and confirmation time.

Blockchain mining has received a growing amount of interest as the modern technologies started using Blockchain network as it provides immutability and secure access through consensus process. In Blockchain networks that rely on proof-of-work (POW) for consensus process, where miners compete to solve crypto-puzzles and publish new blocks in order to gain rewards. Because solo mining is challenging, most miners decide to join a mining pool. Given the variety of mining pools and the potential adoption of different reward systems, many questions remain open regarding the miner's selection in pool mining. However, ensuring trust of a node during consensus and mining process has few challenges to explore. Nodes in a mining pool contribute their processing power toward the effort of adding a block as part of consensus. If the pool is successful in these efforts, they receive a reward. Therefore, to ensure the execution of the consensus protocol a trust model is proposed in our work. Our proposed trust paradigm focused on firstly, how to evaluate the unstable behavior of miners based on their performance on the network. Secondly, how to identify highly ranked-trusted miner in mining pool to maximize pool profit. These issues necessitate ranking methods in mining pool, to rank reputed miners. Therefore, this paper proposes a trust model to identify highly ranked-trusted miners in pool mining. Further, proposed Trust Model is suitably analyzed for transaction volume, latency, and block propagation time with the Hyper ledger framework to ensure robustness.

Blockchain mining has gained popularity since modern technologies started to benefit from its properties such as immutability, secure access, etc. To publish new blocks in Blockchain, miners have to use their resources and participate in the mining process. The majority of miners choose to join a mining pool because they believe that solo mining may be challenging. Due to the existence of many mining pools and the adoption of various incentive mechanisms, they create a lot of confusion for the miners for pool selection. To add a block, miners in the mining pool dedicate their processing power to the consensus process. The pool will get rewards if their efforts are effective. It might be challenging to keep a miner's assurance throughout the consensus and mining procedures in pool mining. This study, proposed a Trust Model to ensure that the consensus protocol in pool mining will be carried out with trusted miners only. As a result, an innovative approach for trust assessment among the miners is formulated using the ML approach. Instead of allowing all miners, our approach allows only trusted miners into the mining pool for efficient mining. The conceptual framework was implemented using the Hyper ledger tool, and an in-depth evaluation of the performance in terms of transaction per second(tps), transaction validation and commit time was conducted.

The m-healthcare system can benefit medical users by providing high-quality pervasive healthcare monitoring, the growing of m-healthcare system is still strange on how we fully understand and manage the challenges facing in this m-healthcare system, especially during a medical emergency. In this paper, we propose a new secure and privacy-preserving opportunistic computing framework, called SPOC, to address this challenge. With the help of our proposed SPOC framework, each medical user who is in an emergency can achieve the user-centric privacy access control to allow only those qualified helpers to participate in the opportunistic computing to balance the high reliability of PHI process and minimizing PHI privacy disclosure in m-Health care emergency. We introduce an efficient user-centric privacy access control in SPOC framework, which is based on an attribute-based access control and a new privacy-preserving scalar product computation (PPSPC) technique, and allows a medical user to decide who can participate in the opportunistic computing to assist in processing his great PHI data.