Collaborative Proof-of-Work: A Secure Dynamic Approach to Fair and Efficient Blockchain Mining

Blockchain technology, has transformed industries by enabling decentralized and secure platforms. Central to blockchain’s functionality are consensus algorithms, which allow independent nodes within the network to achieve agreement on transaction validity, block inclusion, and order without the need for a central authority. By reaching consensus, these algorithms ensure data integrity, resilience, and accuracy across the blockchain, safeguarding the network from errors or malicious attacks [1].

Proof of Work (PoW) is one of the earliest and most widely used consensus algorithms in blockchain renowned for its robust security [2]. It secures networks like Bitcoin [3] by requiring miners to solve cryptographic puzzles to validate transactions and add new blocks, ensuring that tampering or fraudulent activity is prohibitively costly. Miners compete to find a specific nonce that, when combined with the current block’s header, generates a hash value below a set difficulty target. This process aligns miners’ interests with the network’s integrity through block rewards and transaction fees, incentivizing honest participation. Despite its strengths, PoW also presents significant challenges. Its reward structure disproportionately favors strong miners with greater computational resources, consolidating mining power and undermining decentralization [4]. This centralization enables exploitative behaviors [5]. Additionally, weak miners often waste computational power as they are less likely to win rewards, leading to inefficiencies and substantial energy waste.

Concentrated mining power in PoW systems increases vulnerabilities, such as selfish mining and block withholding attacks, where dominant miners can exploit their position to disrupt operations and gain unfair rewards [6, 7]. Additionally, the substantial energy demands required to secure PoW networks raise concerns about scalability and environmental impact, emphasizing the importance of developing more energy-efficient approaches [8, 9]. To address challenges in efficiency, decentralization, security and fairness recent work explores innovative solutions such as partial contribution rewards [10], low-difficulty transactions [11], energy repurposing for productive tasks [12], and decentralized mining pools [13]. While these approaches offer promising improvements, they come with shortcomings, including misaligned incentives that weaken security, heightened vulnerability to DoS attacks from increased network overhead, centralization risks due to reliance on task managers, and scalability constraints arising from high gas fees and operational costs in blockchain networks. These limitations highlight the need for innovative frameworks that balance efficiency, fairness, and decentralization. To overcome these significant issues, in this work, we focused on the following three research questions.

RQ1: How can we optimize computational resources through dynamic mining pool formation in Collaborative Proof-of-Work (CPoW) systems while ensuring decentralized collaboration among miners?

RQ2: What strategies can ensure equitable reward distribution in CPoW models for miners with lower capabilities, without compromising incentives for stronger miners?

RQ3: How can we enhance energy efficiency in mining operations within the CPoW framework, and what trade-offs exist between energy consumption and performance?

To address these questions, this work introduces a novel Collaborative Proof-of-Work framework, which proposes a dynamic mining pool architecture. Unlike static pools, CPoW enables miners to collaborate flexibly, sharing both computational power and rewards equitably inside the network, thereby promoting decentralization. The main contributions of this work are:

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  Dynamic Mining Pool Formation. We proposed a flexible protocol that allows miners to form or leave pools dynamically based on their computational capabilities, ensuring adaptability and resource optimization.

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  Fair And Secure Reward Distribution. We designed a shared reward system that ensures equitable profit distribution among miners, while incorporating mechanisms to verify contributions and facilitating the secure transfer of rewards.

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  Theoretical Foundation and Security Analysis. We establish a rigorous theoretical framework for the Collaborative Proof-of-Work (CPoW) model, integrating key concepts, assumptions, and mechanics with a comprehensive security evaluation.

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  Energy Efficiency. Our approach can reduce the overall energy consumption of mining by distributing computational workloads more effectively through collaboration.

In the remainder of this paper, we review related work in Section II, focusing on existing collaborative mining approaches and their limitations. In Section III, we present the formal foundation, where we define the key concepts and models for our framework. Section IV explores the system workflow of our Collaborative Proof of Work (CPoW) model. In Section V, we conduct a security analysis, followed by a performance evaluation in Section VI. Finally, in Section VII, we conclude with insights on the framework’s implications and outline future directions.

Proof of Work (PoW) consensus mechanisms, widely used in blockchain networks, face critical issues regarding the fairness of block reward distribution, security against exploitative mining behaviors, and the centralization of mining power.

Hunag et al. [4] investigate the disproportionate acquisition of block mining rewards by large mining entities in PoW systems, resulting in a ”rich get richer” dynamic. The authors explain that miners with greater computational power have a higher probability of successfully mining blocks, allowing them to capture more rewards consistently. This cycle concentrates rewards among large miners, undermining decentralization as smaller miners are discouraged from participating, further centralizing power.

Studies by Feng et al[6] and Cheng et al. [7] illustrate that concentrated mining power heightens vulnerabilities in PoW systems, increasing susceptibility to selfish mining and block withholding attacks. When a small number of miners control a significant share of the network’s mining power, they can strategically withhold blocks to disrupt network operations and enhance their own rewards at the expense of smaller miners.

The substantial energy demands of PoW systems, which secure networks through intensive computation, are highlighted by Sedlmeir et al. [8] and further discussed by Li et al. [9]. These demands raise scalability and environmental concerns, emphasizing the need for more energy-efficient approaches.

StrongChain introduces ”weak solutions” to reward partial contributions in PoW, reducing energy waste and leveling the playing field for smaller miners [10]. However, this approach departs from PoW’s emphasis on full solutions, potentially weakening its security guarantees and encouraging miners to prioritize partial rewards, leading to unintended incentives.

The FruitChains protocol, proposed by Pass and Shi, addresses issues like mining variance and selfish mining by introducing “fruits”, transactions with lower mining difficulty that can be appended to blocks mined at higher difficulty [11]. This approach aims to reduce reliance on mining pools by offering more frequent rewards. However, FruitChains introduces new challenges, as low difficulty fruits can lead to high transmission overhead and may even open the network to denial-of-service attacks. Additionally, duplicate fruits are discarded, leading to inefficiencies and increased reward variance. These limitations highlight the complexities involved in modifying traditional Proof of Work (PoW) protocols.

Li et al. [12] introduce the Proof of Neural Architecture Search (PoNAS) consensus, which improves mining efficiency by redirecting computational power from repetitive hash calculations, traditionally used in Proof of Work (PoW), to neural architecture search (NAS) tasks. Instead of solely expending energy on cryptographic puzzles, PoNAS allocates this computational effort toward finding optimal neural network architectures. This shift allows mining energy to contribute both to blockchain security and to advancements in deep learning, addressing PoW’s high energy consumption by giving it a dual purpose. However, centralization risks are introduced by relying on a mining pool manager to allocate tasks and manage rewards, creating a single point of control and compromising the blockchain’s decentralized nature.

SmartPool is a decentralized mining pool implemented on Ethereum that eliminates the need for a central operator by using smart contracts to manage shares and distribute rewards [13]. Miners submit shares, which are verified probabilistically to reduce gas costs, and rewards are distributed based on these verified shares. While SmartPool enhances decentralization, it faces limitations from Ethereum gas fees and potential scalability issues.

Collectively, these studies highlight significant advancements but reveal a critical gap: existing solutions often prioritize specific aspects such as fairness, energy efficiency, or decentralization, without achieving a unified framework that balances these goals while maintaining robust security and scalability.

Proof of Work (PoW) relies on cryptographic hash functions to validate blocks in a blockchain. For a block to be accepted as valid, its hash must be lower than a specific threshold set by the network’s difficulty level, meaning it must begin with a certain number of leading zeros. The key to achieving this lies in finding a suitable nonce, an arbitrary number that, when combined with the block header and hashed, produces a hash meeting these criteria. In PoW systems, miners must search through a large range of possible nonces, $N_{\text{total}}$, repeatedly generating and testing hashes until they find one that meets the requirement. The time $T_{\text{solo}}$ required by a single miner to exhaust the nonce range $N_{\text{total}}$ depends on their computational power $C_{p}$. Since $C_{p}^{\text{weak}}\ll C_{p}^{\text{strong}}$, this implies $T_{\text{solo}}^{\text{weak}}\gg T_{\text{solo}}^{\text{strong}}$. In other words, miners with lower computational power require significantly more time to complete the search than those with higher computational power.

Given that $C_{p}^{\text{weak}}\ll C_{p}^{\text{strong}}$, the probability $P_{\text{success}}^{\text{weak}}$ of a weak miner successfully solving the PoW puzzle remains extremely low compared to $P_{\text{success}}^{\text{strong}}$. As a result, weak miners will earn rewards $R_{\text{weak}}\approx 0$ over time, making mining economically unsustainable for them. This causes mining power to become concentrated among strong miners, centralizing control within a few powerful entities. This situation contradicts blockchain’s core principle of decentralization, which aims to distribute control and rewards across a wide range of participants. In a decentralized network, all miners should have a fair chance at earning rewards. Concentration of rewards among a few powerful miners undermines this openness and inclusivity.

This would lead weaker miners to collaborate and form groups in an effort to compete. Multiple weak miners can form a collaborative group $G_{\text{collab}}$. In this setup, miners within $G_{\text{collab}}$ divide the nonce search range $N_{\text{total}}$ into smaller sub-ranges.

Let, $C_{p}^{m_{i}}$ denote the computational power of miner $m_{i}$. The time $T_{\text{solo}}^{m_{i}}$ required for a single weak miner $m_{i}$ to exhaust $N_{\text{total}}$ without collaboration is given by:

In a collaborative mining setup, each miner $m_{i}\in G_{\text{collab}}$ is assigned a portion of the range $N_{i}=\frac{N_{\text{total}}}{|G_{\text{collab}}|}$. The time $T_{\text{collab}}$ required for the group $G_{\text{collab}}$ to collectively exhaust $N_{\text{total}}$ becomes:

For a group of $n$ weak miners, the time savings can be represented as

In a static collaborative mining setup, groups are fixed, with set participants and roles, limiting flexibility. By contrast, dynamic collaborative mining allows miners to join or leave $G_{\text{collab}}$ as needed, providing greater flexibility. We develop a framework that supports this dynamic collaborative mining, enabling miners to cooperate efficiently and on-demand. This approach increases the probability of successfully mining a block, maximizes rewards, and minimizes wasted computational effort. However, dynamic collaborative mining presents several key challenges, including establishing trust among participants by verifying individual contributions and ensuring fair and secure reward distribution within $G_{\text{collab}}$. These challenges are discussed in detail below.

The Collaborative Proof of Work (CPoW) framework enables secure, fair, and efficient mining within a decentralized, collaborative environment by addressing key challenges in flexibility, fairness and trust. In CPoW, three core processes, dynamic group formation, contribution management, and decentralized reward distribution work together to overcome the unique obstacles of dynamic collaborative mining.

Dynamic group formation solves the problem of flexibility by allowing miners to form well-balanced collaborative groups based on computational power. Contribution management ensures fairness by accurately tracking each miner’s work, verifying their computational effort to ensure that only those who contribute benefit from the group’s efforts, thereby preventing any undue advantage for freeriders. Finally, decentralized reward distribution removes the need for a central authority to manage and distribute rewards by enabling fair, direct, and secure allocation to each participant, eliminating the risk of withheld rewards and single point of failure.

This section presents a comprehensive overview of how these processes function together in the CPoW framework to create a dynamic collaborative mining environment as depicted in Figure 1.

In the collaborative mining framework of CPoW, secure and fair reward distribution is critical. The Contribution Verifier and Reward Manager (CVRM) plays a central role by verifying contributions and ensuring that only honest collaborators receive rewards in a secure manner. This section demonstrates the framework’s resilience against dishonest behavior and its effectiveness in preserving reward integrity.

To assess the performance of our proposed collaborative mining process, we conducted a series of experiments in a controlled environment, implementing our approach on the Ethereum (Geth version 1.11.6 [18]) network. The configuration included Ubuntu 22.04 (LTS) on a 64-bit architecture with an AMD Ryzen 5 3550H CPU, with a base speed of 2.10 GHz, 4 cores, and 8 threads. A total of nine nodes were set up, with six designated as weak miners and three as strong miners. To simulate the weak miners, we introduced a 5 ms delay before verifying each nonce value.

In this paper, we introduced a dynamic collaborative mining approach to enhance fairness and efficiency within the Ethereum network. Our collaborative PoW allows for dynamic mining pool formation and optimizes energy consumption by sharing workloads, addressing the environmental concerns of traditional PoW systems. This framework promotes greater decentralization and inclusivity by enabling miners with lower computational power to participate effectively, reducing the dominance of high-powered entities in blockchain mining. Its fair reward distribution ensures equitable compensation for weaker miners, fostering economic fairness and wider participation in the blockchain ecosystem. Overall, this research has broad implications for improving energy efficiency, equity, and inclusivity in decentralized systems.