Lightweight Blockchain Framework using Enhanced Master-Slave Blockchain Paradigm: Fair Rewarding Mechanism using Reward Accuracy Model
Introduction
Due to the rapid growth of big data and real-time continuous forecasting, the daily electronic transaction load on blockchains has increased substantially. According to big data statistics, global data will grow to 5.2 ZB by 2025, and over 2.5 EB of data are generated daily worldwide (Lynkova, 2020). Also, integrating the IoT with blockchains results in problems with scalability, network traffic, and resource usage. With the massive generation of real-time data, the role of privacy management has become vital in blockchain-based solutions (Chen et al., 2020). The principal purpose of a blockchain is to provide a “decentralized digital ledger” that maintains each node in a peer-to-peer (p2p) network (Agbo and Mahmoud, 2020). This allows permanent divergence of the blockchain with a smart contract of consensus called proof of work (PoW). Further, when any new online transaction is done, the blockchain acquires more than 50% verification of consent from the nodes connected in the network.
Exchanging sensitive information such as healthcare information is a very important aspect when it comes to patients and healthcare service providers beneficial aspect. With the increment of big data service providers use many cloud-based solutions. In the information management point of view this may arise insecurity of sensitive data with the involvement of third-party service provider (Li, Wu, Jiang and Srikanthan, 2020). When it comes to sensitivity of data CIA (confidentiality, integrity, and availability) provides a useful insight to manage security of data. With the major advantage of non-reputability blockchain is the most suitable solution to enhance the performance, management, and security of Information system (Berdik et al., 2021).
Currently, blockchains are finding their place in various fields combined with IoT (Zhao et al., 2020), including fog computing (Baniata, Anaqreh and Kertesz, 2020), Connected Autonomous Vehicles (CAVs) (Oham et al., 2020), smart cities (Esposito, Ficco and Gupta, 2020), and healthcare (Hardin and Kotz, 2020). With the rapid generation of Big data, a prospective auditing mechanism (Li, Wu, Jiang and Srikanthan, 2020) is needed to produce useful information. In decision-enabling prediction making, digital twin (Putz, Dietz, Empl and Pernul, 2020) is very useful. This enhances transparent and secure access transaction data with up-to-date information. Hence, a blockchain provides high accuracy with limited maintenance and extensive merit cost savings.
Powerful blockchain solutions are available with various decentralized cryptography-based platforms like Bitcoin, Ethereum (Hu et al., 2020), and Hyperledger Fabric (Xu, Sun, Luo and Cao, 2021). Due to the large capacity of ledgers, high consumption of computing memory and power resources must be tolerated by each blockchain node to synchronize large quantities of data.
Because of the increasing network traffic, high resource usage, and potential scalability problems in IoT–blockchain integration, we believe that a lightweight blockchain framework is required. The main objective of this paper is to propose a lightweight framework to reduce the cost of computationally intensive mining processess and network traffic while fairly rewarding concurrent miners. The new framework would reduce electricity usage and the scalability problems during a blockchain–IoT integration by subdividing the blockchain into separate shards. Consequently, this paper also proposes a model that fairly rewards successful miners who currently do not receive cryptocurrency commensurate with their efforts. A lightweight blockchain framework called a master–slave blockchain (MSB) is adopted as a solution to the inefficient extendibility of blockchains (Ma et al., 2018). With this solution, the MSB separates tasks into two layers: the master node(s) and the slave agents (SAs). That way, the adopted architecture becomes lightweight and can be run on a mobile device and computationally low-burden sensor devices with less resource usage than that of a standard blockchain.
An important potential application for MSBs is in the medical field. There, SAs can actively participate in verifying and validating new medical transactions before they are added to a blockchain. Patients acting as SAs could connect to a p2p network using their smart devices. In the MSB, the master node collects transactions. When a new transaction is about to trigger, the master node distributes computing tasks among SAs to identify the arbitrary value of the number used only once (nonce) with a consensus of proof of equivalent work (PeW) (Proof of equivalent Work), which is a consensus used in a blockchain for miners to solve a hash puzzle. This nonce value is used to confirm that the transaction is valid. The first miner who acquires the correct answer gets permission to generate a new block. In many blockchain solutions, PoW is used as the consensus algorithm. However, PoW requires high computational power with a large memory, whereas with an MSB all nodes in the network do the blockchain mining, verification, and creation of a new block (Nakamoto, 2008). The PeW works in a partial computing model environment where master nodes are involved in disbursing partial computing tasks among SA miners. The SA miner who identifies the correct nonce value first will be rewarded, and the master node will create the new block. In an MSB with the PeW consensus, a specific partial computing task will be done by master nodes and SAs. Therefore, it will reduce the processing power requirement at each node.
Another major concern is that the current MSB blockchain mechanism can handle only one successful miner (SA) at a time (Ma et al., 2018). If simultaneous results are sent by multiple SAs with valid nonces, the master node will select only one SA and discard all the other responses sent by other successful miners (SAs) considering them to be orphan miners (Zheng et al., 2018). The existing MSB solutions reward only the one successful miner who identifies the nonce first. No mechanism exists to handle a situation where multiple miners identify the nonce at the same time. In our proposed lightweight blockchain framework, we introduce a fair model based on two significant requirements: reduce resource costs and reward miners more fairly. In this study, to explain the new mechanism clearly, we show how the proposed MSB could be used in an electronic healthcare system (EHS). However, this is only one application; the concept of a lightweight MSB architecture applies to many other fields such as the IoT, supply chain management, energy, and commodity transactions.
To increase the miners’ motivation to engage in blockchain mining and to devise a way to fairly reward multiple successful miners (SAs), it is necessary to build an accurate reward mechanism. Further, in selecting an optimal reward recipient, for each miner the time taken to identify the nonce (ɤ) and the miner's reward rate history (ϕ) should be the significant factors that must be considered when computing the reward accuracy (AR) of determining which miner is rewarded.
The main contributions of this paper include the following:
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Introduce a lightweight blockchain framework to reduce the cost of computationally intensive mining process and network traffic
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Provide miner (SA) driven validation process to approve the new transaction before adding to the blockchain
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Model a pervasive paradigm that facilitates both service provider and customer to communicate and infer reliable statistical data in decision making
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Improve the reliability of electronic transactions, with the consensus of PeW thus, all the slave agents connected to the MSB equally get the chance to engage in the validation process as miners
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Design reward accuracy model for rewarding successful miners (SAs) fairly with the weight of significant factors: (i) time taken to identify the nonce (ɤ) and (ii) reward success rate history (ϕ)
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Analyze the feasibility of the proposed reward accuracy model by comparing the impact of time taken to identify the nonce (ɤ) and reward success rate history (ϕ) over the reward accuracy (AR)
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Calculate the complexity of the proposed MSB to evaluate the performance
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Analyze required blockchain mining trials (ω), to compensate cost spent on mining process (Pm) and network traffic (Nt) by a successful miner failed as a success rewardee
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Adapted an adversary model to obtain the fraction of participating master nodes and slave agents that need to be compromised by the adversaries to compromise Master-slave blockchain.
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Adopt MSB to improve lightweight and efficient data synchronization technology which can be accessed by any smart device.
The remainder of the paper has been organized as follows: Section 2 describes complete algorithms with mathematical models used in MSB. Section 3 analyzes the feasibility of the proposed reward accuracy model. Section 4 summarizes the related work. Finally, conclusions and future directions are outlined in Section 5.
Section snippets
Materials and methods
First, we develop the proposed mechanism for the MSB paradigm with consensus, defined as PeW. For this study, we used a Bitcoin blockchain. However, the MSB approach can be implemented using private, public, and hybrid blockchains. This section has been organized into five subsections. The first describes the adaptation of the MSB framework into EHS. Fig. 1 illustrates the structure of a patient's data as it is stored either in each patient block (on-chain data) or in the cloud (off-chain
Results
This section has three subsections. Section A describes the simulation measures. Section B provides the results of comparing existing methods. Section C discusses the results generated from the reward accuracy model. Section D provides the results obtained from the new statistical model introduced to analyze the feasibility of the reward accuracy model. Section E provides the results obtained from the security measures of the proposed adversary model.
Discussion
In addition to improving the efficiency and reliability of electronic records, a blockchain makes an important contribution to preserving the confidentiality of sensitive data. The privacy of that data can be preserved using decentralized differential privacy (Sun et al., 2019). New electronic transactions can be verified and validated by SAs using their personal computers, tablets, or smartphones. The computation required by this approach should be lightweight, require less capacity, lower the
Research challenges and opportunities
Although the proposed blockchain framework is a good solution for reducing unnecessary resource wastage (such as electricity) due to network traffic and an inefficient reward mechanism during blockchain mining, this framework must be still improved upon in the following aspects:
Scalability: Integration of a blockchain with the IoT and fog computing to store and compute data at the edge nodes, combined with the off-chain mechanism, might provide a solution to scalability problems. However, this
Conclusion
In this study, a broad analysis was done to apply a lightweight blockchain framework to an EHS to maintain the storage and security of the EMR. The purpose of the study was to determine an appropriate fit for the MSB paradigm to reward successful miners fairly. With the proposed reward accuracy model, the system has advanced efficiency in equally catering to concurrent successful miners (SAs); they are given an averaged reward value based on two essential factors: time taken to identify a nonce
Author Contribution
O.E. and M.N.H. conceived the study idea and developed the analysis plan. O.E. wrote the initial paper. M.N.H. obtained the results, helped to write and finalize the manuscript. All authors read the manuscript.
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