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  1. (School of Management, Fuzhou Technology and Business University, Fuzhou, 350715, China YU_penglin@outlook.com)



Blockchain, Logistics system, PoR algorithm, Probability output regression algorithm, Data sharing, Reputation system, Data security assurance

1. Introduction

In modern society, the efficiency and accuracy of logistics transportation systems are essential for global business operations. On the other hand, the inherent decentralization of traditional logistics systems leads to serious challenges in data security and information mobility. The frequent occurrence of data leakage, tampering, and loss seriously hinders the development of the logistics industry [1,2]. In recent years, a new technology application, blockchain technology, has attracted the attention of industry and academia to solve this problem. Blockchain technology was originally invented to support Bitcoin, a decentralized digital currency. Its core features include decentralization, openness, transparency, and immutability, which gives it a unique advantage in dealing with data security. In logistics systems, blockchain technology can improve data security and prevent data leakage, tampering, and loss [3,4]. In addition, it can improve the transparency of the logistics system so that every transaction can be tracked and audited [5]. On the other hand, more in-depth research on its characteristics is needed to understand how it can improve the data security of logistics systems and realize the application of blockchain technology in logistics systems [6]. A suitable blockchain application framework was designed according to the characteristics of the logistics system. This study is innovative because it applies blockchain technology in data security assurance for logistics systems. This research is important to improve the data security and transparency of logistics systems by protecting customer privacy, preventing data leakage, improving logistics efficiency, and reducing transaction risks. Although blockchain technology has great potential in the design of data security assurance systems for logistics systems, there are still many challenges to achieving this goal, and further research and exploration are needed.

The remainder of this paper is organized as follows. Section 2 assesses the design of a blockchain-based data security guarantee system for a logistics system. Section 3 evaluates the proposed blockchain-based data security guarantee system. Section 4 experimentally verifies section 3. A summary of the research content and the shortcomings are reported in section 5.

2. Related Studies

In the increasingly complex global supply chain environment, the importance of data security in logistics systems is becoming increasingly prominent. Li et al. proposed a logistics data secure storage solution based on the blockchain, Logisticchain. During the logistics process, sensing data are encrypted and aggregated to the blockchain to ensure data security. The system utilizes blockchain networks for security auditing to ensure data is not tampered with. Through experimental platform evaluation, Logisticchain reduced the computational costs while ensuring security and privacy [7]. Kian et al. designed a multi-level administrative facility network and established a mixed integer programming model using a hierarchical multi-objective meta-heuristic algorithm as a solution. The model was applied to actual data of southeast Türkiye, providing effective solutions and management insights [8]. Steclik et al. proposed an automatic detection method for AGV job types based on stream information and reported K-Means and DBS as clustering algorithms. The proposed algorithm could detect AGV work types, identify anomalies, and provide initial steps for production optimization and predictive maintenance. In addition, it could also serve as a model source for virtual factory simulation tools [9]. Sun et al. used the logistics service platform supervised by the National Certification Center to achieve functions such as group smart contracts and legal anonymous identity authentication. The measurement results showed that the proposed cross-border regulatory system was secure and controllable, protecting the user and data privacy, preventing forgery and fraud, and achieving auditability and traceability of user behavior and data [10]. Gocer et al. proposed a new hub location method under uncertainty group decision-making, combining an improved weighted k-means clustering algorithm with a multi-criteria decision-making (MCDM) tool. This study used Türkiye's logistics data and integrated the GDM method to rank the possible positions, verifying the effectiveness of the evaluation model [11].

Recently, blockchain technology has been applied gradually to the design and operation of logistics systems owing to its unique advantages in improving data security. Singh et al. proposed an authentication (BC-PMA) method based on blockchain chaos and Parerier mapping. The experiment showed that this method had significant advantages over traditional blockchain methods in terms of authentication accuracy, false alarm rate, and computational complexity [12]. Alshamrani et al. proposed DNA-GA encryption technology and studied the introduction of blockchain methods that use hash functions to store data in block form, enabling users to decrypt data separately and improve security [13]. The method outperformed existing methods in processing large amounts of IoT data while reducing the encryption and decryption times and effectively protecting data privacy [13]. Liu et al. proposed a voucher control system based on blockchain technology [14]. They reported that the types of vulnerabilities and defects indicate improper input as the main cause. The system error rate was concentrated mainly at 0.50%. Moreover, the data indicated that the application of big data blockchain in electronic settlement provided excellent value, and the security of the electronic economy was improved by at least 60% [14]. Liu et al. [15] proposed the blockchain system B4SDC. By controlling the scale of routing discovery, fairness was achieved between collector payment and forwarder reward. Controlling the scale of routing discovery can achieve fairness between collector payment and forwarder reward. The experiment showed that B4SDC is effective in terms of motivation and safety [15]. Liang et al. proposed that edge computing should be combined with financial risk control to control the risk of big data financial systems. A survey questionnaire was designed for the model, and the results were analyzed. The experiment showed that network personnel control, environment, and technology positively impact network information security, with influencing factors of 0.26, 0.24, and 0.33, respectively [16].

In summary, blockchain technology has been applied to logistics systems, improving data security, traceability, and transparency. On the other hand, existing methods have low efficiency in processing large amounts of data, and there is a balance issue between protecting data privacy and transparency. Future research needs to address these limitations and explore the introduction of new technologies or optimization of existing blockchain designs. It is expected to promote the design and development of blockchain-based data security assurance systems for logistics through optimization, enabling them to play a greater role in a wide range of logistics applications and improve the overall efficiency and security of the supply chain.

3. Modeling of a Data Security Guarantee System for Logistics based on Blockchain

This chapter explores how to build a blockchain-based data security guarantee system for logistics. First, research is conducted on the Reputation-based Probability Output Regression (PoR) consensus algorithm, analyzing its necessity and advantages in ensuring the secure transmission of blockchain data. Subsequently, a specialized blockchain data security guarantee system model was constructed based on the characteristics of the logistics system. This part elaborates on the construction steps and critical technologies of the model. These studies are expected to provide a new basis for data security in logistics systems and new perspectives for future technological improvements and system optimization.

3.1 Research on the PoR Consensus Algorithm based on Reputation Mechanism

Choosing an appropriate consensus algorithm is crucial for building a blockchain-based data security guarantee system for logistics. Among them, the PoR consensus algorithm based on the reputation mechanism has gradually attracted attention because of its unique advantages. The system makes decisions based on the historical behavior and reputation of the participants, which can prevent malicious attacks and data tampering to some extent, thereby improving data security [17,18]. Fig. 1 presents the operation mode of the reputation mechanism based on blockchain.

Fig. 1. Operational model of the reputation mechanism based on blockchain.
../../Resources/ieie/IEIESPC.2024.13.4.402/fig1.png

The system was designed based on the PoR consensus algorithm (Fig. 1), where nodes exchange information through network connections. The nodes identified by the public key are divided into honest and faulty nodes, constituting a voting community. Nodes may become faulty because of crashes or a lack of information, while malicious nodes can control all faulty nodes and coordinate their behavior. The system assumes that the network is untrustworthy and unreliable, but the failure of one node will not affect the normal operation of other nodes. N nodes take turns performing calculations, sending and receiving messages, and performing local calculations. Each node is identified by a key pair. The evaluation value is generated at the end of each interaction. Nodes are typically used as receiving or evaluating nodes. The interaction tuple is expressed as Eq. (1):

(1)
$ \left\{\begin{array}{l} Transcation=\left(Dski,pk_{i},r\right)\\ \left\{r\colon 0\mathrm{<}r\mathrm{<}1\right\} \end{array}\right. $

where $pk_{i}$ is the public key of the receiving node; $r$ is the rating given by the evaluation node; $Dski$ is the encrypted transaction data signed by the evaluated node key. In the consensus stage, the $t$-round transaction is added to the pending list. Before starting a consensus round, more than 50% of the nodes with the highest reputation value are selected to form a consensus group $G_{t}$. The member nodes of the consensus group are expressed as Eq. (2):

(2)
$ pk_{i}\in G_{t} $

where $G_{t}$ is the consensus group. A new leader node $L_{t}$ selected for round $t$, packages valid transactions to calculate the new reputation value $Reputation$, and finally broadcasts the submission message to the consensus group. The selection of the leadership node is expressed as Eq. (3):

(3)
$ \left\langle Block_{t},pk_{L}Hash,\left(Block_{t}\right),Reputationlist\right\rangle $

where $Block_{t}$ represents the block, and $pk_{L}$ is the public key of the leader node. The node checks the submission information the leader node sends, including the Blockt and hash values. The node sends a new submission message after verifying the public key matching, hash integrity, and transaction validity. The block publication after verification submission can be written as Eq. (4):

(4)
$ \left\langle VERIFY,Block_{t}Hash\left(Block_{t}\right),ReputationList\right\rangle $

where each node $pk_{i}$ that successfully verifies $Block_{t}$ and $ReputationList$ will send the verification information back to $G_{t}$. The reputation of a node is based on its historical rating, reflecting the trust of other nodes in it. Generally, reputation systems rely on the feedback evaluation of nodes and a consideration of their interaction satisfaction with other nodes in the network. To this end, three applicable reputation principles are proposed. First, the liquidity of reputation values, i.e., the calculation of reputation values, is based on the reputation values of the nodes providing ratings. Second, the time range of reputation values, i.e., the contribution of past reputation values to current reputation values is relatively small. Third, the openness of reputation values means that all community members can view the reputation values of all nodes. The initial evaluation of node behavior is expressed as Eq. (5):

(5)
$ S_{i1\ldots n}=\left\{S_{i1},\cdots S_{in}\right\} $

where $S_{i}$ represents the reputation of receiving node $i$. Default reputation values are assigned to all nodes as predictive indicators during system initialization. During each voting round, nodes can receive ratings from multiple other nodes. The normalization of $S_{i}$ is expressed as Eq. (6):

(6)
$ S_{i,n}=\frac{\left(S_{i,n}-\min _{i}\left(S_{i,n}\right)\right)+1}{\left(\max _{i}\left(S_{i,n}\right)-\min _{i}\left(S_{i,n}\right)\right)+1} $

where $\left[S_{ij}\right]$ is the defined rating matrix. After each round, these assessments will be generated for all nodes in the network. The new reputation values of nodes in the $t$ round are calculated by mixing these ratings with the evaluation node reputation values in the $t-1$ round. The reputation value of the mixed nodes is written Eq. (7):

(7)
$ P=\overset{\rightarrow }{S}\ast \overset{\rightarrow }{R} $

where $\overset{\rightarrow }{S}=\left[S_{ij}\right]$ and $\overset{\rightarrow }{R}=\left[R_{in}\right]$. The evaluation node provides the evaluation levels. The node mixes the reputation value of the initial node with the current ranking generated from the rating. The reputation value of the $t$-round node is expressed as Eq. (8)

(8)
$ R_{i,t+1}=\alpha \ast P+\left(\alpha -1\right)\ast R_{t} $

where $\alpha $ is a constant determined from system initialization. The value is set between 0 and 1. It determines which part of the formula has higher priority, and $P$ is the evaluation. If the value is close to 1, the newly generated reputation value will give higher priority to rating $P$, while the previously generated reputation value has lower priority, which conforms to the reputation principle. This helps reduce the node impact of changing behavior over time [19]. The sigmoid function in Eq. (9) constrains these values and prevents fluctuations in the reputation values:

(9)
$ R_{i,t+1}^{'}=\frac{R_{i,t+1}}{\sqrt{1+\left(R_{i,t+1}\right)^{2}}} $

The PoR consensus algorithm is based on the flowchart of a blockchain logistics system in Fig. 2.

A compliant logistics node is selected as the proposer responsible for generating new transaction data blocks (Fig. 2). The proposer verifies the integrity and coherence of the packaged logistics data, and the passed data is packaged into new data blocks and hash values. The proposer sends the new data block to other nodes for signature and rights verification, confirming its legality and sufficient rights. Only the proposer who has passed the verification can receive their new data blocks from the other nodes and add them to the blockchain. This process ensures that only logistics nodes with certain rights can participate in consensus.

Fig. 2. Flowchart of the PoR consensus algorithm in a blockchain-based logistics system.
../../Resources/ieie/IEIESPC.2024.13.4.402/fig2.png

3.2 Building a Blockchain-based Data Security System Architecture for Logistics

After exploring the PoR consensus algorithm based on the reputation mechanism, the next focus is on building the architecture of the data security guarantee system for logistics. The system model will draw on the core idea of the PoR consensus algorithm, especially the reputation mechanism, to achieve security protection for logistics data. In logistics systems, the integrity, real-time, and tamper resistance of data are crucial. Therefore, achieving efficient data processing and transmission while ensuring data security will be the main challenge of this research. This study used principal component analysis methods to address this challenge. The research aims to improve the security and transparency of logistics system data through blockchain technology, focusing on the important role of principal component analysis in optimizing data processing and its mathematical foundation. The application of blockchain in the architecture design of logistics systems provides detailed descriptions of data storage mechanisms and consensus processes between nodes to ensure the immutability and authenticity of information. By establishing a functional hierarchy model of the system, the article clearly describes the responsibilities, data flow paths, and blockchain operation mechanisms of various entities within the logistics system, providing a structured and reliable academic explanation for data security assurance solutions. Principal component analysis aims to achieve the analysis results of "showing more with less" through dimensionality reduction [20]. Dimensionality reduction is a technique that reduces the number of variables in a dataset while preserving most of the information in the original dataset. By applying PCA, a small number of primary components can be extracted from a large number of variables, which reflect the most important information and structure of the source dataset. The mathematical expression of principal component analysis can be written as Eq. (10) by defining a series of linear combinations of raw data variables weighted by eigenvectors corresponding to the eigenvalues of the data covariance matrix:

(10)
$ \left\{\begin{array}{l} y_{1}=a_{11}X_{1}+a_{12}X_{2}+a_{13}X_{3}+\cdots +a_{1n}X_{n}\\ y_{2}=a_{21}X_{1}+a_{22}X_{2}+a_{23}X_{3}+\cdots +a_{2n}X_{n}\\ \cdots \\ y_{m}=a_{m1}X_{1}+a_{m2}X_{2}+a_{m3}X_{3}+\cdots +a_{mn}X_{n} \end{array}\right. $

where $m$ is the number of samples; $n$ is the evaluation indicator; $a_{i1}$, $a_{i2}$, $a_{i3}$, $\cdots $$a_{ip}$ ($i$=1,2, $\cdots $, $m$) are the eigenvectors corresponding to the eigenvalues of the covariance matrix of $X$; $X_{n}$ is the standardized data; $y_{m}$ is the determined principal component. Using the extreme value standardization method,

(11)
$ Y_{ij}=\frac{x_{ij}-mi\left(X_{j}\right)}{\max \left(X_{j}\right)-\min \left(X_{j}\right)} $

where $j$ is a natural number from 1 to $n$. The matrix for calculating the correlation coefficient is expressed as Eq. (12):

(12)
$ r_{ij}=\frac{\sum _{a=1}^{m}\left(x_{ai}-\overline{x}_{i}\right)\left(x_{aj}-\overline{x}_{j}\right)}{\sqrt{\sum _{a=1}^{m}\left(x_{ai}-\overline{x}_{i}\right)^{2}\sum _{a=1}^{m}\left(x_{aj}-\overline{x}_{i}\right)^{2}}} $

where $r_{ij}$ is the correlation coefficient between variables $x_{i}$ and $x_{j}$; $\overline{x}_{i}$ and $\overline{x}_{j}$ are the mean of $x_{i}$ and $x_{j}$. The method calculates the comprehensive weight of each indicator, as expressed in Eq. (13):

(13)
$ w_{j}=\frac{w'_{j}w''_{j}}{\sum _{j=1}^{n}w'_{j}w''_{j}},j=1,2,3,\cdots n $

where $w'_{j}$ is the main component analysis weight; $w''_{j}$ is the entropy weight method weight; $w_{j}$ is the comprehensive weight of each indicator. The power function of the order parameters of each subsystem is used to measure the influence of each order parameter on the system and its contribution to the system. The calculation program parameter system order is expressed as Eq. (14):

(14)
$ y_{i}\left(c_{i}\right)=\sum _{j=1}^{n}w_{j}y_{i}\left(c_{ij}\right),i=1,2,;w_{j}\geq 0;\sum _{j=1}^{n}w_{j}=1 $

where $w_{j}$ is the weight of the order parameters of each subsystem; $y_{i}\left(c_{i}\right)$ is the degree of order of each subsystem. The blockchain logistics system data security platform mainly meets the needs of classifying and storing truck and user data on and off the chain, as well as ensuring the consistency of waybill information. For the secure storage of logistics information, an "on chain+off chain" approach is adopted to improve storage efficiency and ensure security. All logistics information is stored offline. Structured information is stored through IPFS, and important logistics information, such as waybill information, is stored on the chain. The hash calculation results of offline storage data are encrypted and uploaded to the blockchain to achieve data tamper prevention and verification, ensuring data integrity and reliability. The management module is responsible for user management and query services, while the consensus module is responsible for block generation, information storage verification, and inter-node consensus, guaranteeing data accuracy and consistency. Regarding actual business scenarios, the platform involves trucks, drivers, cargo owners, management, and cargo sources.

Fig. 3. System scenario diagram.
../../Resources/ieie/IEIESPC.2024.13.4.402/fig3.png

Fig. 3 presents a schematic diagram of the system scenario, showing the functions and collaborative work methods of the key entities in the logistics blockchain platform. Trucks are logistics carriers in the platform, and their transport vehicles are responsible for cargo handling tasks. As registered users, drivers are responsible for operating trucks and completing transportation orders the platform assigns. The shipper, also a registered user, is responsible for publishing the transportation requirements to the platform and tracking the transportation status of goods in real time. Administrators have the highest authority to supervise and manage all entities in the system, ensuring the orderly operation of the platform. The source of goods refers to the facilities that cooperate with the platform to provide warehousing services for goods. Its location is an indispensable geographical element in logistics strategic planning, determining the route and cost of goods transportation. As the chosen underlying blockchain platform, Super Ledger Fabric’s open source, flexible network architecture, and extensive community support ensure the customizability and fast iteration ability of the system. The platform uses the efficiency, security, programmability, and scalability of super ledgers to meet the needs of logistics data security and efficiency. Fig. 4 shows the architecture of the data security assurance system for logistics.

Fig. 4. Architecture of the data security guarantee system for logistics.
../../Resources/ieie/IEIESPC.2024.13.4.402/fig4.png

The architecture is divided into five layers: data collection layer, data layer, consensus and network layer, presentation layer, and user layer. The data collection layer collects and transmits data through RF devices, information collection terminals, and application sensors to improve the efficiency of the logistics information platform. The data layer determines the storage form of logistics information data, with processed information abstracts stored in the blockchain network and complete data and hash values stored in a relational database. The consensus and network layer includes P2P networks, authentication mechanisms, propagation mechanisms, and PoR consensus algorithms. The consensus service framework of Hyperledger Fabric supports multiple consensus algorithms with high flexibility and configurability, supports consensus strategies, and improves the flexibility and applicability of consensus algorithms. The presentation layer utilizes the B/S architecture and JSP technology to present data. The user layer includes the driver, freight owner, and freight source. The driver and freight source are responsible for information entry, and the freight owner and management can query the logistics information of the goods. Fig. 5 presents the operational mechanism of the blockchain-based data security guarantee system for a logistics platform.

Fig. 5. Operating mechanism of the blockchain-based data security guarantee system for a logistics platform.
../../Resources/ieie/IEIESPC.2024.13.4.402/fig5.png

The user registers as a system node to use the function. The system deploys logistics information traceability supervision and smart contracts on the blockchain to ensure effective supervision of data information. Data information is classified and stored in a distributed system and synchronized to the blockchain to ensure data consistency. Management personnel regularly or according to their permissions, trigger data comparison, analyze query results, and submit processing requests if there are any abnormalities. According to the user's identity, the system calls the smart contract to provide a range of query information. The system aims to provide users with effective logistics information traceability and regulatory services, ensuring data security and accuracy. Fig. 6 shows the synchronization process of the regional module.

Fig. 6. Synchronization process of regional modules.
../../Resources/ieie/IEIESPC.2024.13.4.402/fig6.png

The system utilizes a reputation-based consensus algorithm for consensus module operations, including establishing consensus groups, leading node elections, and block publishing, to ensure the storage consistency of logistics information. The administrator module of the system is responsible for user management and order processing. After new users join, their authenticity will be confirmed through an authentication mechanism and historical data will be synchronized from adjacent nodes.

4. Analysis of a Blockchain-based Data Security Guarantee System Model for a Logistics System

The application of blockchain in logistics system data security has shown significant results, promoting innovation in data processing and transmission technology. On the other hand, a deeper understanding and research are needed to optimize the operation of consensus modules further and improve the system user management and order processing capabilities. These explorations are expected to provide a new basis and perspective for securing logistics system data and optimizing blockchain applications.

4.1 Application Effect of a Blockchain-based Data Security Guarantee System for Logistics

In the experiment, 4860 logistics data were selected as the data source. These records included cargo information, transportation records, order records, and relevant inspection and review forms. The experiment categorized four dimensions: data integrity, real-time data, data tamper resistance, and data transmission efficiency. Table 1 lists the parameter settings of the model.

Table 1. System parameters.

Parameter

Description

Value

Hardware/Software

Details

Source

CPU

Central Processing Unit

Intel Core i5

Hardware

Used for processing blockchain transactions and smart contracts

System Build

RAM

Random Access Memory

8GB

Hardware

Used for temporarily storing the processed data

System Build

OS

Operating System

Ubuntu 18.04

Software

Used for managing hardware and software resources

System Build

Blockchain Platform

Blockchain Technology Used

Hyperledger Fabric

Software

Used for creating the blockchain network and managing the transactions

System Build

Database

Data Storage

MySQL

Software

Used for storing the off-chain data. It stores complete data and hash value

System Build

Consensus Algorithm

Mechanism for Transaction Validation

PoR (Proof of Retrieval)

Software

Used for validating the transactions and creating new blocks

System Bui

The following were used: a hardware device with Intel Core i5 as the central processing unit (CPU) for processing blockchain transactions and smart contracts; 8GB random access memory (RAM) for the temporary storage of processed data; Running Ubuntu 18.04 operating system (OS) for managing hardware and software resources; a Hyperledger Fabric’s blockchain platform to create the blockchain networks and manage transactions; MySQL database to store off-chain data, including complete data and hash values; a consensus algorithm using PoR (Proof of Search) for verifying transactions and creating new blocks.

Consensus latency refers to the delay between the client issuing the request and receiving feedback from the system that the request has been accepted. The consensus latency graph in Fig. 7 presents the specific relationship between latency and the number of nodes and algorithm types.

Fig. 7. Consensus Delay Graph.
../../Resources/ieie/IEIESPC.2024.13.4.402/fig7.png

The consensus delay is closely related to the number of nodes and algorithm types. The initial delay of the PoR algorithm was 220 ms when the number of nodes was 10, and the delay increased to 420 ms when the number of nodes was increased to 110. After introducing the reputation mechanism, the initial delay of the PoR algorithm was 143 ms. When the number of nodes was increased to 110, the delay only increased to 330ms, which was 90 ms less than the original PoR algorithm, indicating that the reputation mechanism can effectively improve the consensus efficiency. The optimized PoR algorithm still has significant advantages in latency over new algorithms, such as Efficient Blockchain Consensus Algorithm (EBCC), Distributed Ledger Technology (DLT) optimization algorithm, and Adaptive Data Authentication Model (ADAM), revealing the crucial role of the PoR algorithm in enhancing the system performance. The consensus delay was restored after the main node was disconnected, as shown in Fig. 8.

Fig. 8. Recovery latency graph.
../../Resources/ieie/IEIESPC.2024.13.4.402/fig8.png

The recovery delay reflects the time it takes for the blockchain system to restore consensus after the main node drops (Fig. 8). The initial recovery time of the original PoR algorithm was 327 ms, and the recovery time increased to 587 ms when the number of nodes was increased to 110. The PoR with the reputation mechanism had a recovery time of 364 ms, and the recovery time was reduced by 236 ms compared to the original PoR when the node was increased to 110. This significantly improves recovery efficiency. The optimized PoR further reduced the recovery time by 35 ms compared to the PoR with the reputation mechanism, indicating the effectiveness of the optimization strategy. Therefore, the recovery delay is influenced by the number of nodes and the type of algorithm. Accordingly, reasonable selection and optimization of the algorithms can optimize recovery efficiency. Fig. 9 presents the throughput of the system.

Fig. 9. System throughput.
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Significant differences in throughput performance were observed among the three algorithms under different node numbers (Fig. 9). Before the number of nodes reaches 60, the throughput of PoR lags behind that of the PoR with the reputation mechanism. When the number of nodes was increased to 60, the throughput of PoR exceeded that of PoR with the reputation mechanism. After the number of nodes reached 90, the PoR throughput decreased and was reversed by the PoR added to the reputation mechanism. Hence, the throughput is influenced by the number of nodes and algorithm types, and algorithms must be optimized to improve the system throughput. A comparative analysis of newly released algorithms was added to the study to comprehensively review the latest research progress in logistics system data security. Selected algorithms from top conferences and journals include the following: Efficient Blockchain Consensus Algorithm (EBCC) published at the Global Blockchain Technology Conference (GBTC), which significantly shortens transaction confirmation time through its multi-layered verification mechanism; The Distributed Ledger Technology (DLT) optimization presented in the Journal of Advanced Intelligent Systems that utilizes intelligent contract automation to improve throughput; the Adaptive Data Authentication Model (ADAM) introduced in the Workshop on Intelligent Data Processing and Security, which combines machine learning technology to achieve effective prediction and verification of transaction data. Compared to the optimized PoR algorithm on 4860 logistics data samples, the proposed model showed performance advantages in dimensions such as data integrity, real-time performance, tamper resistance, and transmission efficiency. Figs. 9-1 to 9-3 present the throughput of each algorithm, revealing the applicability of the algorithm under comprehensive performance considerations.

4.2 Performance Analysis of a Blockchain-based Data Security Guarantee System for Logistics

Performance analysis helps gain a deeper understanding of the applicability and limitations of blockchain in large-scale logistics data security systems, providing a reference and basis for practical applications and future research in related fields. Experiments were conducted on two-dimensional datasets (transaction frequency and processing time) to verify the effectiveness of this algorithm. The scatter matrix results of the processing time and transaction frequency dimensions were analyzed, as shown in Fig. 10.

Fig. 10. Scatter matrix graph under four-dimensional data.
../../Resources/ieie/IEIESPC.2024.13.4.402/fig10.png

Certain differences in data processing time and transaction frequency dimensions were observed (Fig. 10). In Figs. 10(a) and (d), the processing time dimension dataset was processed to form a relatively dense distribution, with a distribution density of 0.68. The distribution of the transaction frequency dimension dataset was relatively sparse after processing, but it was rich in information, with a distribution density of up to 0.78. In Figs. 10(b) and (c), the precise response performance of the transaction frequency dataset was better than that of the processing time dataset, with a high response accuracy of 89%. The model then tests the sharing performance of logistics data, examining two dimensions: transaction frequency and processing time. Fig. 11 shows the data-sharing performance test results.

Fig. 11. Data sharing test chart.
../../Resources/ieie/IEIESPC.2024.13.4.402/fig11.png

Every thousand encryptions require 2350 ms (Fig. 11). Re-encryption conversion and decryption require 3154 ms and 4263 ms, respectively. In the blockchain system, achieving data sharing on the chain once only takes 8 ms, showing significantly improved efficiency compared to the 75 ms before optimization. When the number of data shares was 100, the blockchain system only needed 773 ms, which was much faster than the 5915 ms measured before optimization. Hence, the blockchain-based data security guarantee system for logistics has excellent performance in data security.

5. Conclusion

In the modern logistics industry, where data security issues are increasingly prominent, the decentralized and transparent nature of blockchain technology provides new solutions. This study constructed a blockchain-based data security guarantee system for logistics and conducted a performance test. During the experiment, the PoR algorithm and PoR algorithm optimized by the reputation mechanism were used. The optimized PoR algorithm successfully reduced the latency by 120, highlighting the importance of optimization strategies. The recovery time was reduced by 236 ms compared to the original PoR when the recovery time of the PoR with reputation mechanism was 364 ms, and the node was increased to 110. Moreover, the throughput of the original PoR decreased when the number of nodes reached 90. In addition, the precise response effect of the transaction frequency dataset was superior to that of the processing time dataset, with an accuracy of up to 89%. Regarding data sharing, when the number of data shares was 100, the optimized blockchain system only required 773 ms, which is a significant improvement over the 5915 ms before optimization. This study effectively guarantees data security in the logistics industry, reducing latency, improving throughput, and achieving secure large-scale data processing. On the other hand, this study had some limitations, such as further validating the processing effect on other types of data and improving the universality of optimization strategies. In the future, more types of data will be tested to verify the stability and efficiency of the system. The reputation mechanism will be explored further and optimized to improve the throughput and stability of the PoR algorithm and ensure the data security of the logistics system.

ACKNOWLEDGMENTS

The research is supported by: 2021 Fujian Provincial Social Science Fund Project: “Research on the Coordinated Development of Regional Logistics and Regional Economy in Fujian Province under the Background of High Quality Development” (Project No. FJ2021X018). 2021 Fujian Provincial Education and Research Project for Middle and Young Teachers (Science and Technology) “Research on Optimization of Agricultural Product Supply Chain Information Based on Blockchain Technology” (Project No. JAT210628).

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Author

Penglin Yu
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Penglin Yu obtained her BE in transportation from Wuhan University of Science and Technology in 2010. She obtained her ME in Logistics Management from Fuzhou University in 2012. Presently, she is working as an associate professor in the School of Management, Fuzhou Technology and Business University. Her areas of interest are agricultural logistics and logistics blockchain.