Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT

Abstract

The Industrial Internet of Things (IIoT) has revolutionized smart manufacturing by enhancing automation, operational efficiency, and data-driven decision making. However, the interconnected nature of IIoT devices raises significant concerns about security and system integrity. This paper examines the application of blockchain technology to address these challenges, with a focus on data integrity, access control, and traceability. This paper proposes a blockchain-based framework that leverages decentralized security, smart contracts, and edge computing to mitigate vulnerabilities, including unauthorized access and data manipulation. The framework is evaluated for practicality, scalability, and constraints within IIoT environments. Additionally, this paper discusses the integration of complementary security mechanisms, such as Zero Trust architecture and AI-driven anomaly detection, to provide a comprehensive cybersecurity solution for the Industrial Internet of Things (IIoT).

1. Introduction

The Industrial Internet of Things (IIoT) [1,2,3,4,5] has transformed the manufacturing, energy, and logistics industries by enabling real-time data collection, automation, and predictive maintenance. IIoT systems enhance operational efficiency, reduce downtime, and streamline production processes by connecting devices, sensors, and machinery. Smart factories, a cornerstone of the Industrial Internet of Things (IIoT) ecosystem, leverage this connectivity to boost productivity and optimize supply chain management.

However, the rapid proliferation of connected devices also amplifies the risk of cyberattacks and security breaches, making robust security measures paramount in these environments [1,2]. Recent statistics underscore the escalating threats to IIoT systems. According to Kaspersky’s ICS CERT report [3,4], over 15,000 attacks targeting industrial automation systems were recorded in the first quarter of 2024 alone—a significant increase from previous years. These attacks, which include malware, ransomware, and targeted exploits on critical infrastructure, highlight the vulnerabilities of IIoT devices to unauthorized access, data breaches, and operational disruptions. Supply chain attacks are particularly alarming, as cybercriminals increasingly target industrial organizations to compromise sensitive production data and intellectual property [5,6].

Blockchain technology emerges as a promising solution to these challenges. Its decentralized architecture eliminates the reliance on a central authority, reducing the risk of single points of failure and vulnerability to attacks. The immutable nature of blockchain ensures that once data is recorded, it cannot be altered, preserving the integrity of critical information [7,8,9,10]. Furthermore, blockchain’s transparency and cryptographic security features enable secure and auditable transactions, facilitating reliable access control and protecting sensitive industrial data [11].

This paper examines the potential of blockchain technology to secure Industrial Internet of Things (IIoT) systems, with a focus on smart factories. It proposes a framework that leverages the strengths of blockchain to address key security challenges, including unauthorized access, data integrity, and secure communication. This paper evaluates the framework’s practicality, scalability, and constraints within IIoT environments. Additionally, it discusses integrating complementary security mechanisms, such as a Zero Trust architecture and AI-driven anomaly detection [11,12,13,14], to provide a comprehensive cybersecurity solution for the Industrial Internet of Things (IIoT).

Key Contributions

• Proposed Framework: An architecture that integrates blockchain, edge computing, and smart contracts to enhance security and scalability in IIoT systems.
• Comprehensive Security: Combines blockchain with Zero Trust principles and AI-driven anomaly detection to address real-time threats and vulnerabilities.
• Evaluation: Assesses the framework’s feasibility through real-world test bed experiments, performance benchmarks, and case studies in industrial settings.
• Scalability Analysis: Evaluates alternative blockchain platforms, such as Solana and IOTA, to address high-frequency transaction demands in IIoT environments.

By addressing the critical challenges of integrity and security, this paper contributes to the advancement of blockchain technology adoption in smart manufacturing and industrial ecosystems.

2. Literature Review

Integrating blockchain technology into Industrial Internet of Things (IIoT) systems has emerged as a transformative solution to address critical challenges, including security, data integrity, scalability, and trust management. Recent research [15,16,17,18,19,20,21,22,23,24,25] highlighted the potential of blockchain to revolutionize IIoT ecosystems by providing decentralized, transparent, and tamper-proof frameworks for various industrial applications. This section reviews the key contributions from recent studies, with a focus on architectural designs, use cases, and technical challenges.

2.1. Architectural Innovations

Several studies have explored the design of blockchain architecture tailored for IIoT environments. Dai et al. [15] provided a comprehensive survey of blockchain architectures in IIoT, emphasizing the need for lightweight protocols to accommodate resource-constrained devices. Their work highlights the importance of hybrid architecture, which combines public and private blockchains to strike a balance between transparency and privacy. Similarly, Aslam et al. [16] proposed a lightweight blockchain protocol specifically designed for securing IIoT devices, demonstrating its effectiveness in reducing computational overhead while ensuring data integrity. These studies highlight the optimization of blockchain architectures for industrial settings, where performance and resource efficiency are paramount.

2.2. Security and Trust Management

Security remains a central concern in IIoT systems, where vulnerabilities can lead to significant operational disruptions and downtime. Almarri et al. [17] addressed this issue by leveraging the decentralized nature of blockchain to enhance trust management in Industrial Internet of Things (IIoT) networks. They introduce a novel consensus mechanism optimized for industrial environments, which reduces latency and energy consumption compared with traditional Proof-of-Work (PoW) approaches. In another study, Saravanabhavan et al. [18] explored blockchain-based authentication mechanisms, offering a decentralized alternative to centralized authentication servers. These mechanisms enhance security and mitigate single points of failure, thereby making IIoT systems more resilient to cyberattacks.

2.3. Data Sharing and Supply Chain Transparency

Blockchain’s ability to facilitate secure and transparent data sharing has been widely recognized in Industrial Internet of Things (IIoT) applications. Hu et al. [19] proposed a blockchain-based framework for secure data sharing in Industrial Internet of Things (IIoT) networks, with a case study in smart manufacturing demonstrating its practical applicability. Similarly, Longo et al. [20] presented a blockchain-enabled solution for supply chain management, highlighting the importance of traceability and transparency in the automotive manufacturing sector. These studies demonstrate how blockchain can streamline operations, reduce fraud, and foster collaboration among stakeholders within industrial ecosystems.

2.4. Energy Efficiency and Scalability

Given the large-scale and resource-constrained nature of these environments, energy efficiency and scalability are critical considerations for deploying blockchain in IIoT systems. Asaithambi et al. [21] introduced energy-efficient blockchain protocols specifically designed for IIoT applications, achieving substantial reductions in energy consumption without compromising security. Xu et al. [22] further addressed scalability by combining blockchain with federated learning, enabling the development of distributed machine learning models while preserving data privacy. These advancements underscore the ongoing efforts to make blockchain more sustainable and scalable for industrial use cases.

2.5. Smart Contracts and Automation

Smart contracts have emerged as a powerful tool for automating processes in Industrial Internet of Things (IIoT) systems. Rashid et al. [23] investigated the role of smart contracts in automating workflows within Industrial Internet of Things (IIoT) ecosystems, highlighting their potential to reduce human intervention and operational costs. However, they also identify challenges related to contract complexity and execution speed, which require further research. Neog et al. [24] extended this concept by applying smart contracts to predictive maintenance, demonstrating how blockchain can securely store and share maintenance data to improve operational efficiency.

Despite the promising advancements in integrating blockchain with the Industrial Internet of Things (IIoT), significant challenges prevent its widespread adoption. Studies [21,22,23,24,25] highlight critical barriers to scalability, interoperability, and regulatory compliance. Additionally, the energy consumption of blockchain protocols, particularly those based on Proof-of-Work (PoW), poses a concern for energy-sensitive industrial environments. Addressing these challenges requires innovative solutions, including the development of more efficient consensus mechanisms, improved interoperability with existing Industrial Internet of Things (IIoT) infrastructures, and the careful consideration of regulatory and ethical implications.

In response to these challenges, this paper proposes a novel blockchain-based framework designed to address critical vulnerabilities in Industrial Internet of Things (IIoT) systems, including unauthorized access and data manipulation. The framework leverages decentralized security mechanisms to ensure data integrity and trust, utilizes smart contracts for automated process enforcement, and employs edge computing to enhance real-time data processing while reducing latency. By integrating these technologies, the proposed solution mitigates security risks while maintaining operational efficiency in resource-constrained Industrial Internet of Things (IIoT) environments. The framework is rigorously evaluated to assess its practicality, scalability, and compatibility with the dynamic and demanding nature of Industrial Internet of Things (IIoT) systems. The evaluation results demonstrate its potential to effectively address the existing security challenges while supporting the growing demands of industrial applications. This study underscores the importance of advancing blockchain technology to meet the specific needs of IIoT ecosystems, paving the way for secure, scalable, and sustainable industrial solutions.

3. Background

This section provides detailed background information on the IIoT and blockchain technology, setting the stage for their integration in subsequent sections. It highlights the challenges of IIoT systems, particularly in terms of cybersecurity. It explains how blockchain’s unique features can mitigate these issues. The inclusion of figures and references adds depth and credibility to the discussion.

3.1. Industrial Internet of Things (IIoT)

The Industrial Internet of Things (IIoT) [19,25] refers to a network of interconnected devices, sensors, and systems deployed in various industrial settings, including manufacturing, energy, logistics, and critical infrastructure. These components interact in real time, enabling automation, predictive maintenance, and process optimization. The primary technical goal of the Industrial Internet of Things (IIoT) is to enhance operational efficiency, improve decision making, and minimize downtime through continuous data exchange and integration.

In practice, the IIoT enables advanced capabilities such as real-time monitoring, data analytics, and machine learning. For instance, the IIoT integrates automated machines, sensors, and cloud-based platforms to facilitate smart factory operations in manufacturing environments. These systems can oversee production lines, monitor inventory, predict maintenance needs, and optimize workflows to enhance efficiency. The architecture of such systems typically involves edge computing for immediate data processing, cloud services for storage and advanced analysis, and communication protocols like MQTT (Message Queuing Telemetry Transport) or OPC-UA (Open Platform Communications Unified Architecture) to ensure seamless connectivity between devices [11].

To support the adoption of the Industrial Internet of Things (IIoT), the Industrial Internet Consortium (IIC) developed the Industrial Internet Reference Architecture (IIRA) [12], based on the ISO/IEC/IEEE 42010:2011 standard. The IIRA provides a comprehensive framework for system design, categorizing IIoT systems into four primary viewpoints: business, use, functional, and implementation. Each viewpoint offers guidance on the various aspects of system architecture, including defining business objectives, outlining functional requirements, and detailing implementation steps for hardware, software, and network infrastructure.

While the IIRA offers a scalable and adaptable framework, it does not provide specific technical details about individual technologies or protocols. To address this limitation, South Korea’s Smart Factory Promotion Team introduced a smart factory reference model, which focuses on industry-specific requirements for smart factory systems [25,26]. This model defines the key terms and operational conditions of smart factories, presenting a layered approach that covers all phases of the manufacturing process—from order placement to production and shipment. Although the smart factory model does not delve deeply into technical implementations, it outlines the necessary automation and control systems at various stages of the factory lifecycle, ranging from field automation to application system integration. Figure 1 illustrates the IIRA, smart factory reference models, and major cyberattacks targeting each architecture layer.

As IIoT systems generate vast amounts of data, their architecture must ensure smooth and secure data flow across interconnected devices. However, the growing number of connected devices also expands the potential attack surface for cybersecurity threats. Each device represents an additional entry point for cyberattacks, making IIoT systems increasingly vulnerable to unauthorized access, data breaches, and operational disruptions. A PwC (PricewaterhouseCoopers) survey [6] revealed that 59% of organizations struggle to secure IoT devices, with vulnerabilities often stemming from legacy systems and inadequate security practices. These cybersecurity threats underscore the urgent need for robust cybersecurity frameworks to safeguard industrial data and maintain continuous operations, as even a single compromised component can result in widespread system failures.

3.2. Integrated Architectural Framework for Smart Industrial Systems

The architecture presented in this work combines principles from the Industrial Internet of Things (IIoT) and smart factory systems to form a comprehensive framework that supports intelligent, secure, and efficient industrial operations. This integrated model draws from two foundational references: the IIC-IIRA (Industrial Internet Consortium Industrial Internet Reference Architecture) and the Korea Smart Factory Foundation Reference Model. By aligning these architectures, the system enables seamless data flow, real-time analytics, automation, and robust security across the multiple layers of an industrial ecosystem.

At its core, and as shown in Figure 1A, the IIC-IIRA architecture follows a layered structure that facilitates both the vertical and horizontal integration of systems. The IIC-IIRA model defines three primary layers—enterprise, transport, and edge—each with distinct roles in managing data, processing logic, and device interaction. The enterprise layer serves as the strategic center, hosting high-level applications, analytics engines, and centralized data repositories. It ensures enterprise-wide coordination, decision making, and integration with external systems. Below it, the transport layer functions as an intermediary that orchestrates the communication between the upper enterprise systems and the lower edge and field devices. This layer handles data orchestration, local storage, and intermediate computation to support real-time responsiveness. At the lowest level, the edge layer operates at the physical–digital interface, where IoT devices collect data and edge nodes perform localized processing and control actions. This hierarchical design reduces latency, enhances autonomy, and improves bandwidth efficiency.

Complementing this structure is the Korea Smart Factory Foundation Reference [27], as shown in Figure 1B, which emphasizes application-driven control, automation, and monitoring within a manufacturing context. In this model, the application system acts as the central intelligence hub, integrating AI-based analytics and issuing control commands based on operational insights. These commands are executed by the control automation layer, which comprises controllers and sensors responsible for regulating machinery and processes in real time. Concurrently, the monitoring/operation data component gathers information from IoT-enabled field automation systems and cloud services, feeding it back into the application layer for continuous optimization. A key aspect of this model is its explicit consideration of cybersecurity threats, identifying common attack vectors such as DDoS, MITM, code injection, and physical breaches. Security mechanisms are embedded throughout each layer to ensure the integrity, confidentiality, and availability of critical systems and data.

Together, these models illustrate a cohesive architectural vision for next-generation industrial environments. They emphasize distributed data management, intelligent edge processing, cloud scalability, and secure communication. The integration of AI and advanced analytics allows for predictive maintenance, process optimization, and autonomous decision making. Moreover, the layered approach ensures modularity, adaptability, and interoperability across diverse technologies and platforms. By combining the structured rigor of the IIC-IIRA with the practical focus of the Korean smart factory model, this architecture provides a scalable foundation for deploying intelligent industrial solutions that meet evolving technological and operational demands.

3.3. Blockchain Technology

Blockchain [28,29,30] is a distributed ledger technology that offers a secure, transparent, and immutable system for recording transactions. It has expanded its applications beyond cryptocurrencies, such as Bitcoin, to various industries due to its ability to ensure data integrity and enhance security. The technology operates on a decentralized network where transactions are grouped into blocks. These blocks are linked together chronologically in a chain. This structure ensures data cannot be altered without the consensus of the network, thus maintaining its tamper-resistant and transparent nature. Each block contains a unique header and is identified by its block header hash. The blocks are chained together by their encrypted headers, with the Genesis block serving as the foundation. Figure 2 illustrates the data structure of blocks in a blockchain system.

The genesis block is the first block in a cryptocurrency blockchain and has a height of zero; subsequent blocks increase in height. This block is unique as it does not reference a previous block and sets the initial state of the network. It is hard coded into the blockchain’s software. It establishes the parameters that govern the blockchain’s operation, such as the initial difficulty level for mining, consensus rules, and the initial distribution of tokens. The Genesis block also carries symbolic significance, reflecting the creator’s intent or vision for the blockchain. For instance, Bitcoin’s genesis block, mined by its creator, Satoshi Nakamoto, on 3 January 2009, contained a message from The Times, a British newspaper, which many interpret as a statement against the traditional banking system. This block marked the beginning of Bitcoin and, by extension, the cryptocurrency era. In summary, blockchain technology provides a robust framework for secure and transparent transactions through decentralization, immutability, and cryptographic security. The genesis block, as the starting point of the blockchain, ensures the integrity and continuity of the entire chain, with all blocks linked back to it.

Several key features of blockchain make it particularly well suited for securing IIoT systems:

• Decentralization: Blockchain disperses control across a peer-to-peer network, where each participant maintains a copy of the ledger. This distributed nature eliminates the need for a central authority, reducing the risk of single points of failure. Even if one node is compromised, the rest of the network remains operational, enhancing resilience against cyber threats [28].
• Immutability: Once data is recorded on the blockchain, it cannot be deleted or altered without the consensus of the network. In environments where accuracy is paramount [29], this unchangeability ensures the integrity of critical industrial data, such as sensor readings or manufacturing performance metrics.
• Transparency: Blockchain provides a transparent and auditable record of all transactions visible to all participants. This feature is crucial for IIoT systems, where real-time data tracking and operational monitoring are essential for maintaining efficiency and security [30].
• Cryptographic Security: Blockchain utilizes advanced cryptographic algorithms to safeguard data, rendering unauthorized access or tampering virtually impossible. Hash functions, digital signatures, and consensus algorithms ensure secure device communication and prevent data breaches [31].
• Smart Contracts: These self-executing agreements are embedded in the blockchain and automatically trigger actions when predefined conditions are met. For example, smart contracts can automate device maintenance or registration, enhance security, and optimize operational efficiency [32].

The decentralized, secure, and transparent nature of blockchain offers significant potential for addressing the security challenges IIoT systems face [33]. By integrating blockchain technology, industries can enhance data integrity, prevent unauthorized access, and automate secure processes, leading to safer and more resilient IIoT operations [34,35].

4. Blockchain-Based Architecture and System Design

The growing deployment of Industrial Internet of Things (IIoT) systems in sectors such as manufacturing, energy, and logistics has introduced critical challenges related to data integrity, device authentication, and system resilience. Traditional centralized architectures are increasingly inadequate in addressing these concerns due to their vulnerability to single points of failure, cyberattacks, and scalability limitations—particularly when managing real-time, high-volume data from resource-constrained devices. To overcome these issues, blockchain technology offers a decentralized, tamper-proof, and trustless framework that enhances security and transparency in IIoT environments.

This section presents a comprehensive blockchain-based architectural framework specifically designed for Industrial Internet of Things (IIoT) systems. The proposed design integrates edge computing, smart contracts, and distributed ledger technology to ensure secure, scalable, and efficient operations. It is structured into multiple layers—from physical devices to application-level interfaces—each contributing to secure data acquisition, intelligent processing, trusted storage, and fine-grained access control. Additionally, the architecture introduces a collaborative DDoS mitigation mechanism that combines deep learning models, edge node filtering and blockchain-based smart contracts to ensure continuous and trustworthy system operation.

By leveraging edge computing for localized processing, smart contracts for automated policy enforcement, and blockchain for immutable logging and auditability, the framework addresses key IIoT security and performance requirements. The following subsections outline the layered architecture, system components, controller modules, blockchain structure, and integrated defense mechanisms that collectively enable a robust and intelligent Industrial Internet of Things (IIoT) ecosystem.

4.1. Architecture Overview

The proposed architecture is structured into five distinct layers, each meticulously designed to ensure secure, scalable, and efficient IIoT operations:

• Physical Layer: This foundational layer comprises IIoT sensors, actuators, and edge computing devices responsible for real-time data collection and processing. Edge computing is pivotal in reducing latency by performing localized computations and ensuring timely responses in industrial settings where delays can lead to significant operational and financial losses [34].
• Network Layer: The network layer facilitates reliable communication between devices using standardized protocols such as MQTT (Message Queuing Telemetry Transport), HTTP (Hypertext Transfer Protocol), and OPC-UA (Open Platform Communications Unified Architecture). By enabling seamless data exchange across local and remote components, this layer ensures efficient coordination in distributed IIoT systems, laying the groundwork for robust interoperability [35].
• Storage and Cloud Layer: This layer manages data storage, quality control, and ontology modeling. Leveraging cloud-based tools, it processes large volumes of IIoT data to extract actionable insights, driving informed decision making and operational optimization [33,34,35].
• Blockchain Layer: At the core of the architecture lies the blockchain layer, which provides decentralized security and data integrity through distributed ledger technology (DLT). This layer prevents unauthorized access and data tampering by employing consensus algorithms, cryptographic encryption, and identity management. It mitigates Distributed Denial of Service (DDoS) attacks by distributing computational tasks across edge nodes and blockchain miners, enhancing system resilience [36,37,38,39].
• Application Layer: The application layer oversees system operations, including monitoring, control, and logging. Real-time interaction tools notify operators of failures or anomalies, ensuring continuous system functionality and minimizing downtime. This layer is the interface between the system and end users, fostering trust through transparent and tamper-proof data transactions [37].

By integrating blockchain technology [40,41,42,43,44], the architecture ensures transparent and immutable data transactions, fostering stakeholder confidence while enabling secure device identity management. Smart contracts enhance system resilience by automating access control and enforcing security policies, thereby improving overall system security and reliability. As illustrated in Figure 3, this additional security layer promotes robust access control and data protection.

4.2. System Entities and Interactions

The proposed blockchain-integrated Industrial Internet of Things (IIoT) system is designed to ensure secure, scalable, and efficient operations by integrating several core entities. These entities interact across multiple architectural layers—physical, network, edge, cloud, and application—to support real-time data processing, the decentralized enforcement of security policies, and automated threat response. The seamless integration of IIoT devices with blockchain-based infrastructure enhances trust, traceability, and operational resilience in industrial environments.

IoT devices form the foundation of the system, serving as primary sources of environmental and operational data. These include low-power sensors, actuators, and embedded systems deployed throughout industrial settings to monitor parameters such as temperature, pressure, humidity, and machine status. Typically based on microcontroller units like ESP32 or STM32, these devices operate within constraints related to computational power, memory, and battery life. To facilitate secure communication, they employ lightweight protocols such as MQTT, CoAP, or LoRaWAN for energy-efficient and low-bandwidth data transmission. Basic cryptographic capabilities, such as AES-128, are incorporated to secure data at the source before transmission. Due to their resource-constrained nature, these devices offload complex tasks such as protocol translation and identity verification to secure hubs, ensuring secure upstream communication and preventing unauthorized access.

Edge computing nodes act as intermediaries between constrained IoT devices and centralized cloud infrastructure. These nodes perform preprocessing, filtering, and authentication of sensor data before forwarding it to controllers or cloud services. Typically implemented using single-board computers, such as the Raspberry Pi, edge nodes run lightweight operating systems optimized for real-time performance, like Ubuntu Core. They support containerized services like Docker, allowing the modular deployment of applications for anomaly detection, data aggregation, and local decision making. Edge nodes perform cryptographic operations, such as SHA-256 hashing and digital signature generation, to verify data integrity before uploading it to the blockchain. They also implement access control policies and perform the initial validation of both device identities and data authenticity, contributing to system resilience by mitigating DDoS attacks and reducing latency.

Controllers serve as central coordination points within the architecture, managing data flow, enforcing security policies, and interfacing with the blockchain layer. Implemented as high-performance embedded systems or virtual machines hosted in private clouds or fog nodes, controllers utilize middleware platforms such as Node-RED and Apache Kafka to process streaming data and handle event-driven logic. They communicate with both edge nodes and blockchain components via RESTful APIs or gRPC-based interfaces, enabling seamless integration across the different layers of the system. Controllers execute smart contracts to automate access control, transaction verification, and policy enforcement, ensuring that only authenticated and validated transactions are recorded on the blockchain. They maintain a local cache of frequently accessed data for fast retrieval, synchronizing this information periodically with the immutable blockchain ledger. As critical components for maintaining data consistency, controllers play a crucial role in preserving the integrity and reliability of the entire system.

A secure and indexed database serves as a repository for validated sensor data, supporting long-term storage and analytics. Unlike traditional databases, this component is tightly governed by the controller module to ensure data integrity, traceability, and auditability. Implemented using time series databases like InfluxDB, data is hashed using algorithms such as SHA-256 before storage, with each hash linked to its corresponding blockchain transactions. Access control mechanisms based on token authentication, such as JSON Web Token (JWT), enforce strict user permissions. To enhance availability and fault tolerance, the database is replicated across multiple trusted nodes, ensuring continuity even in the face of hardware failures or network disruptions. This component bridges the gap between operational technology (OT) and information technology (IT), enabling seamless integration with enterprise-level analytics tools and dashboards used for strategic decision making.

The blockchain layer provides the foundation for decentralized trust, immutability, and tamper-proof logging of all system transactions. It plays a central role in secure identity management, creating audit trails, and executing smart contracts. Built on permissioned blockchain platforms like Ethereum, utilizing Proof-of-Stake (PoS) consensus, this layer stores the cryptographic hashes of sensor data, timestamps, and device identifiers rather than raw data, thereby preserving privacy and scalability. Smart contracts written in Solidity enforce the rules governing data access, device registration, and mitigation strategies for cyber threats such as DDoS attacks. Nodes within the blockchain layer are categorized into miners, transaction nodes, and light nodes, depending on their computational capabilities and assigned roles within the network. By leveraging the immutability of blockchain records, the system ensures the complete traceability of events, which is essential for forensic analysis, compliance reporting, and audit purposes.

The end users represent the human operators or enterprise applications that interact with the system to retrieve data, monitor performance, and make informed decisions. These users access the system through web dashboards developed using modern front-end frameworks such as Vue.js. Through REST APIs exposed by the controller module, they query data from the database or blockchain, enabling real-time visibility into system operations. Authentication is enforced using multi-factor methods or biometric verification to prevent unauthorized access. The users receive alerts and notifications via email and push notifications triggered by predefined thresholds or anomalies detected through deep learning models analyzing network behavior. The design of user interfaces emphasizes real-time insights and historical trend analysis, supporting data-driven decision making and rapid response to system events.

To address the high transaction throughput requirements of IIoT environments, the proposed system leverages edge computing to boost the processing capacity for blockchain mining tasks. By utilizing edge computing resources, the system can efficiently handle large transactions without compromising integrity or real-time performance. This approach ensures network security, device identity management, and safe, transparent data exchanges.

Figure 4 illustrates the interaction and data flow across various components of the blockchain-integrated IIoT system architecture. The data flow begins with IoT devices collecting and transmitting encrypted data to secure hubs. These hubs forward the data to edge computing nodes, which perform preprocessing, filtering, and authentication. The validated data is then sent to the controllers, which manage the data flow, enforce security policies, and write transactions to the blockchain. The blockchain records all transactions, ensuring traceability and accountability. Finally, the end users interact with the system to access and analyze the validated data stored in the database.

Each entity within the system performs a distinct and technologically defined role, contributing to the overall efficiency, scalability, and security of the blockchain-enhanced Industrial Internet of Things (IIoT) architecture. This layered distribution of responsibilities ensures robustness even under high transaction loads and potential cyber threats, making the proposed framework well suited for deployment in mission-critical industrial environments.

4.3. System Controller and Modules

The system controller is the central component, ensuring data security, integrity, and availability. It consists of four key modules:

• Blockchain Management Module: This module generates transactions by encapsulating data, SHA-256 (Secure Hash Algorithm) hashes, device IDs, and database identifiers. These transactions are added to the blockchain, ensuring immutability and traceability.
• Database Management Module: Responsible for calculating the SHA256 hashes of transmitted data, this module securely stores the data in an indexed database. It ensures that the data is well organized and readily accessible to authorized users.
• Edge-Allowing Hub Module: This module manages communication between controllers and hubs, enforcing data transmission schedules and timing policies. Filtering noise and correcting errors ensure the validity and reliability of the data.
• Access Control Module: This module authenticates users through token-based mechanisms, preventing unauthorized access. It ensures that only authorized entities interact with the system, maintaining security and integrity.

4.4. Authentication and Blockchain Nodes

The system classifies blockchain nodes into three categories based on their computational capabilities:

• Miner Nodes: These high-performance nodes generate new blocks and verify transactions. They handle resource-intensive tasks and serve as gateways for transactions and light nodes [7,8,9].
• Transaction Nodes: Operating on lightweight systems like Alpine Linux, these nodes relay transactions to miner nodes for verification and processing. While they do not participate in mining, they play a crucial role in maintaining network efficiency [7,8,9].
• Light Nodes: These simple IoT devices with minimal computational power are primarily used for sensing tasks. Light nodes rely on miner nodes for data aggregation and fusion, ensuring efficient data processing [42].

The proposed architecture employs a Proof-of-Stake (PoS) consensus algorithm on the Ethereum blockchain to optimize performance and sustainability. The Proof-of-Stake (PoS) consensus algorithm significantly reduces computational overhead and energy consumption compared with the Proof-of-Work (PoW) algorithm, making it ideal for real-time Industrial Internet of Things (IIoT) environments [43]. Algorithm 1 provides a simplified outline of how the Blockchain Management Module interacts with the controller to secure IIoT data and ensure its traceability through the blockchain. This modular design ensures that the system remains secure, scalable, and efficient, addressing the unique challenges of IIoT environments while leveraging the strengths of blockchain technology.

Algorithm 1. Pseudocode for Blockchain Management Module in the Blockchain-Integrated IIoT System

module BlockchainManagement(Controller):     # Iterate over each data batch received from the hubs     for each data_batch in received_data_batches:         # Step 1: Calculate SHA256 hash of the data         data_hash = SHA256(data_batch)         # Step 2: Generate a transaction         transaction = {              ‘data_hash’: data_hash,             ‘device_id’: data_batch.device_id,             ‘database_id’: data_batch.database_id         }         # Step 3: Append the transaction to the blockchain         append_to_blockchain(transaction)     # End of data batch processing end module

4.5. Block Data Structure

The block data structure [7,8,9] of the proposed blockchain-integrated IIoT system is meticulously designed to ensure secure, scalable, and efficient operations, with each block consisting of two main components: the block header and the block body. The block header contains essential metadata that ensures integrity, security, and traceability. The block header includes the previous block hash, which cryptographically links blocks together, ensuring immutability; the Merkle root hash, which represents all transactions in the block via a Merkle tree for efficient verification; the edge root hash, which integrates IoT device data from edge devices or edge nodes; the timestamp, providing chronological context; the nonce, used in mining processes; and the block hash, a unique identifier generated by hashing the header. The block data structure of the proposed blockchain-integrated IIoT system is depicted in Figure 5. For instance, a mined block might have a hash like 000026d2118c4a35be647d1f3040839bef66fead8096556d63d6cd6278b78bff, with fields such as a previous hash of 00000000… (for the genesis block) and a timestamp of 1743053969.0619528.

The block body contains the actual data stored in the blockchain, including transactions (e.g., sensor readings, such as Tx1: 25.4 °C) and edge device data (e.g., light node or edge node data), both of which are hashed and incorporated into the Merkle and edge root hashes.

Table 1. Example block data.

Block Hash	000026d2118c4a35be647d1f3040839bef66fead8096556d63d6cd6278b78bff
Block Data	Index: 0, Previous Hash: 00000000…, Merkle Root: 9d89ed10…, Edge Root: 7f19b63e…, Timestamp: 1743053969.0619528, Nonce: 329640, Block Hash: 000026d2…

provides an example of the block header and body data. This structure ensures immutability and integrity through cryptographic hashing, efficient Merkle tree verification, and the seamless integration of IoT and edge computing data. The system achieves scalability, real-time performance, and robust protection against cyber threats, such as DDoS (Distributed Denial of Service) attacks, by leveraging edge computing for distributed processing and smart contracts for automated threat responses. These features align with the proposed IIoT architecture, emphasizing edge computing for latency reduction, smart contracts for tamper-proof actions, and collaborative mechanisms involving deep learning, edge nodes, and blockchain for transparent and accountable DDoS mitigation.

4.6. Collaborative DDoS Mitigation

The proposed system integrates deep learning, edge computing, and smart contracts into a multi-layered Distributed Denial of Service (DDoS) defense mechanism. This collaborative approach enhances the system’s ability to detect, mitigate, and respond to DDoS attacks in real time, ensuring minimal disruption to operations [44,45,46].

• Deep Learning for Threat Detection: The system utilizes advanced deep learning models, such as Long Short-Term Memory (LSTM) networks, to analyze real-time network traffic patterns. These models are trained to identify anomalies and flag potential DDoS attacks, enabling early detection and rapid response [45]. By continuously monitoring traffic behavior, the system distinguishes between legitimate and malicious activity, significantly reducing false positives while maintaining high detection accuracy.
• Edge Computing for Localized Mitigation: Edge computing plays a crucial role in mitigating DDoS attacks by processing and filtering malicious traffic at the network’s edge. By offloading computational tasks to edge nodes, the system reduces the load on central servers, ensuring an uninterrupted flow of legitimate traffic. This localized approach minimizes latency and enhances the system’s resilience against large-scale attacks. Edge nodes act as the first line of defense, filtering out malicious requests before they reach critical infrastructure, thereby preventing network congestion and maintaining operational continuity.
• Smart Contracts for Automated Response: Smart contracts embedded within the blockchain automate the response to detected threats. When a DDoS attack is identified, smart contracts trigger predefined actions, such as blocking malicious IP addresses or stopping suspicious traffic that causes rate-limiting. These actions are recorded on the blockchain, creating a transparent and tamper-proof audit trail that ensures accountability and traceability [44]. Using smart contracts eliminates manual intervention, enabling faster and more reliable responses to cyber threats.
• Integrated Defense Mechanism: By combining deep learning, edge computing, and smart contracts, the system creates a collaborative defense mechanism that is both proactive and adaptive. This integrated approach improves the system’s ability to withstand DDoS attacks, ensuring continuous operation and data integrity even under adverse conditions. The decentralized nature of this solution further enhances its robustness, making it well suited for the dynamic and interconnected environments of Industrial Internet of Things (IIoT) systems.

Algorithm 2 demonstrates the collaborative role of deep learning, edge computing, smart contracts, and blockchain in achieving effective DDoS mitigation, thereby enhancing security in Industrial Internet of Things (IIoT) networks.

Key contributions of collaborative DDoS mitigation:

• Early Detection: Deep learning models identify anomalies in real-time traffic, enabling proactive threat identification.
• Localized Mitigation: Edge computing filters malicious traffic at the edge, reducing latency and server load.
• Automated Response: Smart contracts execute predefined actions, ensuring rapid and tamper-proof responses to threats.
• Decentralized Resilience: Blockchain integration ensures transparency, accountability, and resistance to single points of failure.

This multi-layered approach mitigates DDoS attacks effectively and strengthens the overall security posture of IIoT systems, ensuring uninterrupted operations in industrial environments.

Algorithm 2. Pseudocode for DDoS Detection and Mitigation Using Fog-Cloud and Blockchain

BEGIN     // Step 1: Start monitoring network traffic     START:     WHILE (system_running) DO         traffic_data = MonitorNetworkTraffic()         // Step 2: Detect and filter suspicious traffic         filtered_traffic = DetectAndFilter(traffic_data)         // Step 3: Use LSTM-based Deep Learning model to detect DDoS         attack_detected = DetectDDoSUsingLSTM(filtered_traffic)         IF attack_detected THEN             // Step 4: Identify malicious traffic             malicious_traffic = IdentifyMaliciousTraffic(filtered_traffic)             // Step 5: Forward to Fog Nodes for processing             SendToFogNodes(malicious_traffic)             // Step 6: Fog nodes filter malicious traffic             clean_traffic = FogNodeFiltering(malicious_traffic)             // Step 7: Forward cleaned traffic to the Cloud Controller             SendToCloudController(clean_traffic)             // Step 8: Cloud controller validates traffic             validation_result = ValidateTrafficInCloud(clean_traffic)             IF validation_result == “malicious” THEN                 // Step 9: Execute Smart Contract                 defense_actions = ExecuteSmartContract(validation_result)                 // Step 10: Initiate smart contract for mitigation                 InitiateMitigation(defense_actions)                 // Step 11: Record actions on the blockchain                 LogOnBlockchain(defense_actions)                 // Step 12: Collaborative defense by other participants                 TriggerCollaborativeDefense()                 // Step 13: Ensure transparency and accountability                 EnsureTransparencyAndAccountability()                 // Step 14: Attack mitigated, maintain normal traffic                 MaintainNormalTraffic()             ELSE                 // Step 15: Allow normal traffic                 AllowTraffic(clean_traffic)             END IF         ELSE             // Step 16: No attack detected, allow traffic             AllowTraffic(filtered_traffic)         END IF     END WHILE     // Step 17: End     END

5. Experiment and Results

This section evaluates the performance of our blockchain-integrated Industrial Internet of Things (IIoT) system under real-world conditions, focusing on three critical dimensions: data integrity, security, and efficiency. These aspects are essential for ensuring reliable operations in dynamic IIoT environments, where challenges such as data tampering, cyberattacks, and resource constraints can significantly impact system performance. The findings demonstrate the system’s ability to maintain data immutability, respond rapidly to threats, and operate efficiently even under adverse conditions.

5.1. Experiment Setup

The experimental environment was designed to mimic a smart factory equipped with IoT sensors, local processors, and blockchain technology. This setup allowed us to evaluate the system’s performance in a realistic industrial context, where real-time monitoring and secure data processing are paramount. A network of 20 Raspberry Pi IoT devices (Cambridge, UK) was deployed in a controlled laboratory setup at the AT@Lab of KMU University in Daegu, South Korea. Each Raspberry Pi was equipped with a suite of environmental sensors, including temperature sensors (DS18B20), humidity sensors (DHT22), and pressure sensors (BMP280). Functioning as digital “noses,” these devices continuously monitored environmental parameters, providing real-time data collection and analysis for various research applications. For example, a temperature sensor in factory A recorded 25.4 °C every 10 s, highlighting the importance of real-time monitoring in detecting anomalies such as overheating motors or abnormal humidity levels, which could lead to equipment failures or production delays.

Table 2. Sensor readings from factory A and B.

Factory	Sensor Type	Value	Timestamp	Normal Range	Anomaly Detected
Factory A	Temperature	25.4 °C	2023-04-08T12:00:00	23–27 °C	No
	Temperature	28.0 °C	2023-04-09T03:00:00	23–27 °C	Yes (Z > 3) *
Factory B	Humidity	65%	2023-04-08T12:00:05	50–70%	No

* Z-Score was used to measure unusual data points. Threshold: Z > 3 flagged as abnormal.

consolidates sensor readings from multiple factories, including temperature and humidity data.

Five Raspberry Pi mini computers served as edge nodes to preprocess and secure data before transmitting it to central servers, acting as “local traffic cops.” These edge nodes generated unique digital fingerprints (SHA256) for each data packet, akin to barcodes that uniquely identify products, and secured data using AES-256 (Advanced Encryption Standard with 256-bit key length) encryption, providing bank-grade security to prevent unauthorized access. By performing localized computations, edge nodes reduce latency and alleviate the computational burden on central servers, ensuring efficient data flow.

Figure 6 illustrates a decentralized IIoT architecture, where factory A and factory B utilize IoT sensors and dedicated edge nodes to collect and secure data locally. Processed data is sent to a central controller and recorded on an Ethereum blockchain with 100 validating nodes, ensuring data integrity and traceability. The system supports miners, transaction nodes, and light nodes, with data stored in a centralized database for end user access and analysis.

The system utilized Ethereum’s energy-efficient Proof-of-Stake (PoS) consensus mechanism, which contrasts sharply with the computationally intensive and energy-heavy Proof-of-Work (PoW) method. Validators in the PoS model are selected based on their “stake”, which is akin to a security deposit, resulting in a 76% reduction in energy consumption compared with the PoW model. This adoption of PoS ensured faster transaction processing while aligning with sustainability goals. An HTTP flood attack was simulated to evaluate the system’s resilience, generating 50,000 requests per second over a 10 min period. Under normal conditions, the system handled 1200 transactions per second, like a busy highway with manageable traffic flow. However, during the attack, traffic surged to 50,000 requests per second, representing a 40-fold increase that caused significant congestion.

5.2. Data Collection and Blockchain Integration

Data collection and blockchain integration were central to the experiment. For instance, a humidity sensor in factory B recorded 65% at 12:00:05 p.m., highlighting the importance of continuous data collection for proactive monitoring and the early detection of potential issues. Each data packet was assigned a unique SHA256 hash at the edge nodes, ensuring tamper-proof identification. Any alteration in the data would change the hash, exposing fraud, while AES-256 encryption safeguarded the data from unauthorized access. The processed data was then stored on the blockchain, with each transaction encapsulating details such as the device ID, sensor data, timestamp, and data hash.

Table 3. Translation structure.

{ “device_id”: “device_001”, “sensor_data”: {“sensor_type”: “temperature”, “value”: 25.4}, “timestamp”: “2023-04-08T12:00:00”, “data_hash”: “a1b2c3d4…” }

provides an example of a typical transaction structure. In contrast,

Table 4. Transaction examples from blockchain integration.

Device ID	Sensor Type	Value	Timestamp	Data Hash
device_001	Temperature	25.4 °C	2023-04-08T12:00:00	a1b2c3d4…
device_002	Humidity	65%	2023-04-08T12:00:05	e5f6g7h8…
device_003	Pressure	101.3 kPa	2023-04-08T12:01:00	i9j0k1l2…

expands on the blockchain transaction structure by providing additional examples of how data packets are stored.

5.3. Data Analysis and Insights

Data stored on the blockchain is immutable and distributed across thousands of nodes, making it nearly impossible to alter—akin to writing in stone. The system demonstrated impressive performance, handling 980 transactions per second, which is comparable to processing approximately 1000 credit card payments every second. Over 12 h, the system achieved a 100% match between raw sensor data and blockchain hashes, showcasing impeccable data integrity.

Data analysis revealed valuable insights into system performance. In factory A, temperature trends were monitored within a normal range of 23–27 °C, which was considered safe for machinery operation. However, twelve flagged events were detected over 72 h, including a spike to 28 °C at 3:00 a.m., which was traced back to a misconfigured sensor. Statistical methods, such as the Z-Score, were used to measure how “unusual” a data point was relative to the mean. For example, if the average human height is 5′6′′, a person measuring 7′0′′ would have a high Z-score. Any Z-score greater than three was flagged as abnormal, allowing for the early detection of potential issues. The temperature trends and anomalies are summarized in Table 2.

5.4. DDoS Attack Mitigation

To evaluate the system’s security capabilities, a DDoS attack simulation was conducted, in which a hacker launched an HTTP flood attack, sending 50,000 requests per second to disrupt the network. Such an attack could shut down machinery or halt production, resulting in substantial financial losses. The system responded swiftly, with edge nodes detecting the surge in network traffic within 12 s, which is significantly faster than traditional tools, which typically take up to 120 s to respond. The AI algorithms flagged the anomaly, triggering automatic IP blocking through blockchain-based smart contracts. Within 28 s, 95% of malicious traffic was blocked, and the system was restored to normal operation. The edge nodes handled 78% of the CPU (central processing unit) load during the attack, compared with central servers, which would have reached 98% utilization and likely crashed.

Table 5. Anomalies detected in factory A (72-h period).

Timestamp	Temperature (°C)	Z-Score	Anomaly Flagged
2023-04-08T03:00:00	28.0 °C	3.2	Yes
2023-04-08T15:30:00	27.8 °C	3.1	Yes
2023-04-09T02:45:00	28.1 °C	3.5	Yes
… (12 total events)	…	…	…

lists all anomalies detected in factory A over a 72 h period, including timestamps, temperature values, and Z-scores. It highlights how statistical methods, such as the Z-Score (standard score), identify unusual data points.

Table 6. Detailed DDoS attack simulation metrics.

Time (s)	Network Traffic (req/s)	Action Taken	Outcome
0	50,000	Edge nodes detect surges in network traffic.	Attack identified.
12	50,000	AI flags anomaly; blockchain triggers IP blocking.	Malicious IPs flagged for blocking.
28	2500 (95% blocked)	The system was restored to normal operation.	95% of malicious traffic mitigated.

provides a granular view of the DDoS attack simulation, detailing network traffic levels, system actions, and outcomes at specific time intervals.

5.5. Performance and Scalability

Performance metrics highlighted the system’s scalability and efficiency. Under normal conditions, the system achieved a throughput of 1200 transactions per second with a latency of 120 ms and an energy consumption of 0.8 kWh/h. During the attack, throughput dropped by 20% to 960 transactions per second, latency increased by 275% to 450 ms, and energy consumption rose to 1.2 kWh per hour. Post attack, the system recovered to 1150 transactions per second (−4.2%) with a latency of 180 ms (+50%) and energy consumption of 0.9 kWh/h. Edge computing played a critical role, reducing cloud load by 40%, akin to diverting traffic from a congested highway to local roads.

Additionally, the PoS consensus mechanism consumed 76% less energy than traditional blockchain methods, aligning with sustainability goals.

Table 7. Comparative performance metrics during DDoS attack.

Scenario	Throughput (tx/s)	Latency (ms)	Energy Use (kWh/h)	CPU Load (%)
Normal	1200	120	0.8	50
During Attack	960 (−20%)	450 (+275%)	1.2	78 (Edge Nodes)
Post Attack	1150 (−4.2%)	180 (+50%)	0.9	60 (Edge Nodes)

compares the system’s performance metrics under three scenarios: normal conditions, during the DDoS attack, and after the attack recovery. It quantifies the attack’s impact on throughput, latency, energy consumption, and CPU load.

Table 8. Energy savings with Proof-of-Stake (PoS).

Consensus Mechanism	Energy Consumption (kWh/h)	Energy Savings (%)
Proof-of-Work (PoW)	3.0	-
Proof-of-Stake (PoS)	0.8	76%

quantifies the energy savings achieved by adopting the Proof-of-Stake (PoS) consensus mechanism compared with the Proof-of-Work (PoW) mechanism.

The key achievements included achieving 100% data integrity across 1 million transactions, ensuring reliability and trustworthiness. The system demonstrated rapid threat response, with a Mean Time to Detect (MTTD) of 12 s and a Mean Time to Mitigate (MTTM) of 28 s. The cost savings were significant, with energy consumption reduced by 62% compared with traditional blockchain systems and latency improved by 40% through edge computing. The system enabled predictive maintenance for factories, allowing operators to detect overheating or other issues before machinery failures occurred. For security teams, automated defense mechanisms minimize downtime and operational disruptions.

5.6. General Results

The results presented in the tables demonstrate the effectiveness of the proposed blockchain-based IIoT system in addressing the key challenges, including data integrity, security, and efficiency. The system achieved 100% data integrity across 1 million transactions, ensuring tamper-proof and reliable data storage. It also demonstrated rapid threat response capabilities, with a Mean Time to Detect (MTTD) of 12 s and a Mean Time to Mitigate (MTTM) of 28 s during a simulated DDoS attack. Additionally, the integration of edge computing reduced energy consumption by 62% and improved latency by 40%, highlighting the system’s scalability and sustainability.

The findings underscore the transformative potential of blockchain in the Industrial Internet of Things (IIoT), particularly in enhancing cybersecurity, enabling predictive maintenance, and supporting real-time decision making. By leveraging decentralized security mechanisms, smart contracts, and edge computing, the proposed framework ensures the robust protection of sensitive data while maintaining operational efficiency.

Table 9. Real-world impact of blockchain-integrated IIoT system.

Beneficiary	Impact	Example
Factories	Enabled predictive maintenance to prevent machinery failures.	Detected overheating in factory A at 3:00 a.m., preventing potential downtime.
Security Teams	Automated defense mechanisms minimize downtime during cyberattacks.	Mitigated DDoS attacks within 28 s, reducing operational disruptions.
Environmental Impact	Reduced energy consumption by 62%, aligning with sustainability goals.	Edge computing reduced cloud load by 40%, resulting in a 40% decrease in overall energy use.

highlights the practical benefits of the blockchain-integrated Industrial Internet of Things (IIoT) system for various stakeholders.

Future research will address emerging challenges, including threats from quantum computing and scalability issues associated with larger deployments. These efforts will further solidify blockchain’s role as a cornerstone technology in the evolution of Industrial Internet of Things (IIoT) ecosystems.

5.7. Comparative Analysis of Blockchain-Integrated IIoT Systems with Traditional IIoT Architectures

In conducting a comparative analysis of our blockchain-integrated Industrial Internet of Things (IIoT) system, we draw insights from the relevant literature that focuses on traditional IIoT systems [41,42]. The study [41] highlights the inefficiencies associated with the Proof-of-Work (PoW) mechanism, reporting high energy consumption and slow transaction processing, which are critical issues in IIoT environments that demand energy efficiency and speed. Our experiment, which utilized Ethereum’s Proof-of-Stake (PoS) consensus mechanism, demonstrated a significant 76% reduction in energy consumption compared with traditional PoW methods, aligning with the paper’s suggestion that optimizations to PoW could improve resource efficiency. Moreover, our results indicate that PoS not only enhances energy efficiency but also supports faster transaction processing, with our system handling 1200 transactions per second under normal conditions.

Another study [42] emphasizes the security vulnerabilities and network intrusions faced by traditional IIoT architectures. It proposes an Artificial Intelligence-based Lightweight Blockchain Security Model (AILBSM) to enhance privacy and security. Our experiment’s results underscore the robust security features of the blockchain-integrated IIoT system, with a rapid threat response capability during a simulated DDoS attack, achieving a Mean Time to Detect (MTTD) of 12 s and a Mean Time to Mitigate (MTTM) of 28 s, which surpasses the security capabilities of traditional IIoT systems discussed in the paper. The use of edge computing in our system also significantly reduced the computational burden on central servers, thereby enhancing the system’s resilience against cyberattacks.

The comparative analysis reveals that our blockchain-integrated IIoT system outperforms traditional IIoT systems in terms of energy efficiency, transaction processing speed, and security response capabilities. The adoption of the PoS consensus mechanism and the integration of edge computing have been instrumental in achieving these improvements. The findings from our experiment validate the potential of blockchain technology to address the key challenges faced by traditional Industrial Internet of Things (IIoT) systems, thereby supporting the conclusions drawn in the referenced papers regarding the need for innovative solutions to enhance the performance and security of IIoT environments.

6. Discussion

This section highlights the transformative potential of blockchain in Industrial Internet of Things (IIoT) systems while addressing the practical challenges and providing a roadmap for real-world implementation. Integrating blockchain into Industrial Internet of Things (IIoT) environments offers significant advantages. However, it presents unique challenges that must be carefully managed to ensure successful deployment.

6.1. Enhancing Security, Integrity, and Resilience

Blockchain technology offers a robust framework for securing Industrial Internet of Things (IIoT) systems, addressing vulnerabilities inherent in traditional centralized architectures. As IIoT networks grow in complexity, they face critical challenges, including data manipulation, unauthorized access, and single points of failure. Blockchain mitigates these risks through its decentralized, immutable, and transparent ledger system, ensuring secure and trustworthy operations.

The immutable ledger is a cornerstone of blockchain’s contribution to the Industrial Internet of Things (IIoT). By recording every transaction in an unalterable chain, blockchain guarantees data integrity—a crucial requirement for systems generating vast amounts of high-speed sensor data. This immutability prevents tampering and supports real-time auditing, enabling rapid detection and resolution of security breaches [43]. Furthermore, the decentralized nature of blockchain eliminates single points of failure, thereby enhancing resilience against cyberattacks, such as Distributed Denial of Service (DDoS) attacks. Even if one or more nodes are compromised, the distributed network ensures continuous operation, maintaining system reliability in industrial environments.

Smart contracts further strengthen security by automating device authentication and access control processes. These programmable agreements reduce human error, enforce consistent policies, and streamline operations. Additionally, blockchain’s transparent ledger facilitates real-time auditing and traceability, which is essential for regulatory compliance and accountability in industries with stringent standards [43,44].

Despite these strengths, scalability remains a challenge for blockchain in IIoT applications due to the high volume of real-time transactions. To address this, the proposed framework utilizes edge computing to offload computational tasks to edge nodes, thereby reducing latency and enhancing efficiency. Furthermore, adopting a Proof-of-Stake (PoS) consensus mechanism reduces energy consumption while enhancing transaction processing speed. These innovations ensure that blockchain can meet the demanding performance requirements of IIoT systems without compromising security or operational efficiency [45].

6.2. Addressing Real-Time Threats

While blockchain strengthens data integrity and access control, it does not inherently prevent cyberattacks at the device or network level, where most IIoT vulnerabilities reside. The proposed system integrates complementary security mechanisms, such as Zero Trust architecture and AI-driven anomaly detection, to address the network vulnerability gap.

Under the Zero Trust model, every access request is rigorously authenticated and authorized, minimizing the risk of unauthorized access. This approach assumes that no user or device is inherently trustworthy, even if they are already inside the network. Meanwhile, AI-based anomaly detection models analyze patterns in network traffic and sensor data to identify suspicious activities in real time. These models effectively detect threats such as DDoS attacks, sensor spoofing, and firmware tampering. By combining blockchain with advanced security measures, the system achieves robust protection against evolving cyber risks [47].

6.3. Evaluating Consensus Mechanisms

The choice of consensus mechanism is a crucial factor in determining the suitability of blockchain for Industrial Internet of Things (IIoT) applications. While Proof-of-Stake (PoS) offers significant advantages over traditional Proof-of-Work (PoW) in terms of energy efficiency and scalability, it introduces challenges such as potential latency in validator selection, which may limit its ability to handle high-frequency Industrial Internet of Things (IIoT) sensor data.

Alternative consensus mechanisms and blockchain platforms are evaluated to address these concerns. For example, Solana [48] and IOTA [49] are considered for scalability, efficiency, and real-time capabilities. Solana’s high throughput and low latency make it an ideal candidate for IIoT applications requiring rapid transaction processing. Similarly, IOTA’s feeless transactions and scalable architecture offer significant advantages for large-scale industrial deployments, where cost effectiveness and performance are paramount.

6.4. Practical Deployment and Testing

Although the proposed framework demonstrates strong theoretical potential, its feasibility in real-world scenarios requires validation through experimental evaluation. This paper includes a detailed analysis using a real IIoT test bed to simulate a smart factory environment. The test bed incorporates a variety of IIoT devices, edge computing nodes, and blockchain components, enabling comprehensive performance testing under realistic conditions.

Performance benchmarks and empirical results are presented to evaluate the system’s effectiveness in key areas, including latency, transaction throughput, and security resilience. For instance, the system’s ability to process high volumes of sensor data while maintaining low latency is evaluated under normal and attack conditions. Additionally, real-world case studies demonstrate the practical applicability of the framework in industrial settings, such as predictive maintenance, supply chain tracking, and environmental monitoring [40].

6.5. Transition Feasibility

Transitioning from traditional security models to a blockchain-based approach presents several challenges, particularly in IIoT environments that rely heavily on legacy systems and centralized architecture. This paper proposes a hybrid architecture that combines blockchain with existing security mechanisms to facilitate seamless integration. This phased approach allows organizations to adopt blockchain incrementally, minimizing disruption to ongoing operations.

This paper also examines the implications of implementing blockchain in Industrial Internet of Things (IIoT) systems. While initial deployment costs may be higher due to infrastructure upgrades and training requirements, the long-term benefits—such as reduced operational costs, enhanced security, and improved efficiency—are expected to outweigh these expenses. A roadmap for gradual adoption is provided, outlining strategies for integrating blockchain into existing workflows while ensuring minimal impact on day-to-day operations.

7. Conclusions

Blockchain technology provides a powerful solution for addressing the security, integrity, and scalability challenges that modern Industrial Internet of Things (IIoT) systems face. By leveraging innovations such as edge computing, PoS consensus mechanisms, and AI-driven anomaly detection, the proposed framework demonstrates significant potential for enhancing the resilience and efficiency of industrial networks. However, the practical deployment and integration with legacy systems remain key considerations. With careful planning and incremental adoption, blockchain can pave the way for a more secure, transparent, and efficient future for Industrial Internet of Things (IIoT) applications. This study introduces a novel framework that leverages blockchain technology to enhance the security and integrity of Industrial Internet of Things (IIoT) systems. By addressing critical challenges such as scalability, interoperability, energy consumption, and latency, the framework ensures secure data storage, validation, and visualization through the blockchain’s decentralized architecture. The integration of blockchain with IIoT not only mitigates vulnerabilities such as unauthorized access and data tampering but also fosters trust and operational resilience in smart factory environments. This paper highlights the need for further research to explore scalability improvements, seamless integration with legacy systems, edge case management, and robust data protection mechanisms. Practical experimental implementations are crucial to validate the framework’s effectiveness in real-world industrial settings, ensuring it meets the demanding requirements of IIoT systems, including low latency, high throughput, and cybersecurity resilience. The proposed framework represents a promising step toward securing IIoT systems, paving the way for the full utilization of blockchain technology in the era of smart manufacturing. By combining innovations such as edge computing, Proof-of-Stake (PoS) consensus mechanisms, and AI-driven anomaly detection, this solution demonstrates significant potential in addressing the security, integrity, and scalability challenges faced by modern Industrial Internet of Things (IIoT) systems. With careful planning and incremental adoption, this approach can enable industries to achieve a more secure, transparent, and efficient future for IIoT applications.