Enhancing Industrial IoT Network Security through Blockchain Integration
Abstract
:1. Introduction
2. Related Work
3. Key Components of the Proposed System
3.1. Sensor Nodes
3.2. Blockchain
3.3. Certificate Authority
3.4. Interplanetary File System (IPFS)
3.5. Zero Knowledge Proof
3.6. Proof of Authority (PoA)
3.7. Lightweight Compression
4. Proposed System
4.1. Phase-1: Sensor Nodes to Private Blockchain Gateway
Algorithm 1 Sensor Nodes to Private Blockchain Gateway |
Input: Data from sensor nodes, local aggregator, CA certificate cache Output: Encrypted and compressed data for gateway
|
4.1.1. Data Collection and Lightweight Compression
4.1.2. Transmission of Data to Local Aggregators
4.1.3. Certificate Requests with Caching
4.1.4. Data Encryption at Aggregators
4.1.5. Parallel Processing for Certificate Verification by Distributed Nodes
4.1.6. Data Decompression in Parallel
4.1.7. Priority-Based Data Transmission to Gateway
4.2. Phase-2: Private Blockchain Gateway Processing
Algorithm 2 Private Blockchain Gateway Processing |
Input: Encrypted and compressed data from distributed nodes Output: Data stored in IPFS and blockchain updated with IPFS index and hash
|
4.2.1. Data Decryption at the Private Blockchain Gateway
4.2.2. Data Decompression
4.2.3. Intelligent Sharding and Adaptive Rate Limiting
4.2.4. Data Classification Based on Confidentiality
4.2.5. For Less Confidential Data
4.2.6. For Confidential Data
4.3. Phase-3: Consensus Mechanism
Algorithm 3 Consensus Mechanism |
Input: Data, metadata, verification nodes Output: New block added to blockchain
|
4.3.1. Distributed Verification by Verification Nodes
4.3.2. Data Authenticity Check
4.3.3. Proof of Authority (PoA) Consensus
4.3.4. Block Creation
4.4. Phase-4: IoT Device Access on Private Blockchain
Algorithm 4 IoT Device Access on Private Blockchain |
Input: Data request from IoT device, gateway, ZKP challenge Output: Requested data transmitted to IoT device
|
4.4.1. IoT Device Initiates Request to Gateway
4.4.2. Gateway Processes the Request
4.4.3. ZKP Challenge Generation and Caching
4.4.4. IoT Device Responds to the Challenge
4.4.5. Gateway Verifies the ZKP Response
4.4.6. Data Retrieval and Transmission
4.4.7. Rate Limiting and Caching
4.4.8. Intelligent Data Access and Caching for IoT Devices
4.5. Underlying Mathematical Principles
4.5.1. Hash Function
4.5.2. Elliptic Curve Cryptography (ECC)
4.5.3. Zero Knowledge Proof (ZKP)
4.5.4. Consensus Mechanism—Proof of Authority (PoA)
4.5.5. Sharding
4.5.6. Lightweight Compression
4.5.7. Rate Limiting
5. Discussion
5.1. Features of the Proposed System
5.2. Limitations
5.3. Practical Implications and Use Cases
5.4. Results
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Authors | Title | Main Contributions | Challenges Identified | Proposed Solutions | Application Domain |
---|---|---|---|---|---|
Christidis et al. [4] | Blockchains and Smart Contracts for the Internet of Things | Discusses the potential of blockchain and smart contracts in revolutionizing IoT through decentralized interactions and automated processes. | Challenges like scalability, privacy, and legal enforceability. | Suggests solutions like dual integration for legal robustness and privacy-preserving techniques. | Internet of Things |
Alia Al Sadawi et al. [5] | A Survey on the Integration of Blockchain With IoT to Enhance Performance and Eliminate Challenges | Provides a nuanced analysis of IoT and blockchain convergence, proposing a novel three-tier architecture integrating dew and cloudlet computing. | Addresses challenges in scalability, efficiency, and latency in IoT–blockchain systems. | Employs Practical Byzantine Fault Tolerance (PBFT) for consensus, enhancing system performance and data integrity. Recognizes PBFT’s susceptibility to Sybil attacks and suggests sharding as a countermeasure. | IoT and Blockchain Integration |
Ouaddah et al. [6] | Fair Access: a new Blockchain-based access control framework for the Internet of Things | Presents Fair Access, an innovative blockchain-based access control framework for IoT, demonstrated through a smart security camera system. | Challenges in real-time processing and blockchain scalability in IoT. | Proposes custom blockchain development and future extensions including secure storage layer and a billing model to incentivize data sharing. | IoT Security |
Wang et al. [7] | RDIC: A blockchain-based remote data integrity checking scheme for IoT in 5G networks | Introduces a blockchain-based RDIC scheme to enhance IoT security within 5G networks, with rigorous proofs of correctness and unforgeability. | Need for trustworthy data and secure data integrity in autonomous vehicle systems and IoT within 5G networks. | Application of RDIC scheme to the Internet of Vehicles, addressing the vital need for trustworthy data in autonomous vehicle systems. | IoT Security in 5G Networks |
Rane et al. [8] | Data-driven decision making with Blockchain-IoT integrated architecture: a project resource management agility perspective of industry 4.0 | Assesses the shortcomings of traditional project resource management (PRM) tools in the EPC industry, proposing an integrated blockchain and IoT architecture for enhanced decision making and operational agility. | Challenges in manual data entry and delayed updates in traditional PRM tools in the EPC industry. | Proposes the integration of blockchain and IoT for real-time data and autonomous resource coordination, aiming to improve decision making and agility in operations. | Project Resource Management in Industry 4.0 |
Authors | Title | Main Contributions | Challenges Identified | Proposed Solutions | Application Domain |
---|---|---|---|---|---|
Ma et al. [9] | Blockchain + IoT sensor network to measure, evaluate, and incentivize personal environmental accounting and efficient energy use in indoor spaces | Advances real-time carbon accounting and energy monitoring, integrating IoT sensors with blockchain for improved data acquisition and management in energy use evaluation. | Challenges like data lags and volatility in emissions factors. | Applies predictive modeling and machine learning algorithms to optimize energy consumption patterns, promoting sustainable energy behaviors. | Sustainable Energy Management |
Farahani et al. [10] | The convergence of IoT and distributed ledger technologies (DLT): Opportunities, challenges, and solutions | Explores the synergistic integration of IoT with distributed ledger technologies (DLT), outlining opportunities and solutions for challenges in this convergence. | Identifies challenges in scalability, security, and privacy in the IoT-DLT ecosystem. | Suggests innovative approaches to address these challenges, underscoring the importance of further research and development in IoT and DLT integration. | IoT and Distributed Ledger Technologies |
Alrubei et al. [11] | A Secure Blockchain Platform for Supporting AI-Enabled IoT Applications at the Edge Layer | Develops a secure blockchain platform for AI-enabled IoT applications at the edge layer, enhancing security and decentralized operations. | Focuses on challenges related to security and integration of AI and IoT at the edge layer. | Proposes a blockchain solution to provide a secure and decentralized platform for public health surveillance and AI applications in IoT. | AI-Enabled IoT Applications |
Sun et al. [12] | Blockchain-Based IoT Access Control System: Towards Security, Lightweight, and Cross-Domain | Proposes a blockchain-based IoT access control system, integrating Hyperledger Fabric for management of local ledgers and enhanced system resilience. | Addresses the need for secure, lightweight, and cross-domain access control in IoT. | Integrates the ABAC model with blockchain, introducing MSPs for trusted cross-domain interactions and emphasizing lightweight design for performance and resource efficiency. | IoT Access Control |
Bataineh et al. [13] | Novel and Secure Blockchain Framework for Health Applications in IoT | Develops a novel and secure blockchain framework specifically tailored for health applications in IoT, focusing on enhancing data security and operational efficiency. | Challenges in data security and operational efficiency in healthcare IoT applications. | Proposes a private Ethereum network and smart contracts to create a secure, decentralized framework adhering to global EHR standards. | Healthcare IoT |
Criteria | Proposed Methodology | Existing System | Remarks |
---|---|---|---|
Data Collection | Utilizes sensor nodes with edge computing capabilities. | Relies on centralized data collection, leading to potential bottlenecks [26]. | Edge computing enhances efficiency by distributing processing. |
Achieves efficient data collection with reduced latency. | Exhibits higher latency and inefficiency due to centralized processing. | ||
Data Compression | Employs advanced algorithms for optimal data size reduction. | Uses basic compression methods with limited effectiveness [27]. | Advanced algorithms improve transmission efficiency. |
Facilitates faster and more efficient data transmission. | Results in slower data transmission due to basic compression. | ||
Certificate Handling | Implements certificate caching to minimize validation times. | Requires frequent CA validations due to lack of caching [28]. | Caching and ML for CA validation enhance security and efficiency. |
Uses machine learning for efficient CA validation. | Depends on slower traditional CA validation processes. | ||
Encryption | Adopts ChaCha20-Poly1305 for high-security encryption. | Employs standard encryption methods with potential vulnerabilities [29]. | ChaCha20-Poly1305 ensures enhanced security. |
Ensures strong security and data integrity during transmission. | Faces challenges in maintaining data integrity and security. | ||
Verification | Enables parallel verification through distributed nodes. | Centralized verification creates scalability and security issues [30]. | Parallel verification enhances security and scalability. |
Offers a secure and scalable verification process. | Suffers from security and scalability limitations. | ||
Data Storage | Integrates IPFS for decentralized storage with blockchain indexing. | Utilizes centralized cloud storage, posing risks of single points of failure [31]. | IPFS provides secure and decentralized storage. |
Differentiates data storage based on confidentiality. | Does not differentiate, leading to potential security risks. | ||
Consensus Mechanism | Employs proof of authority for efficient consensus. | Uses proof of work, known for high energy consumption. | PoA is more energy-efficient than PoW. |
Ensures rapid validation with less energy use. | Consumes more energy and has slower validation times. | ||
IoT Device Access | Utilizes Zero Knowledge Proof for secure data access. | Lacks ZKP implementation, leading to security concerns [32]. | ZKP significantly improves data privacy and security. |
Enhances privacy and security for data access. | Exposes data access to security vulnerabilities. |
Data Size | Size After Compression | Transmission Time to Local Aggregator (ms) | Data Processing Latency at Aggregators (ms) | Transmission Time to Distributed Nodes (ms) | Data Processing Latency at Distributed Nodes (ms) | Transmission Time to Gateway (ms) |
---|---|---|---|---|---|---|
10 KB | 2.5 KB | 5.102 | 8.256 | 15.314 | 20.478 | 18.589 |
20 KB | 5 KB | 7.189 | 10.342 | 18.467 | 25.531 | 22.674 |
50 KB | 12.5 KB | 10.478 | 13.589 | 22.643 | 30.756 | 28.812 |
100 KB | 25 KB | 15.867 | 18.932 | 30.987 | 35.104 | 35.219 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Bobde, Y.; Narayanan, G.; Jati, M.; Raj, R.S.P.; Cvitić, I.; Peraković, D. Enhancing Industrial IoT Network Security through Blockchain Integration. Electronics 2024, 13, 687. https://doi.org/10.3390/electronics13040687
Bobde Y, Narayanan G, Jati M, Raj RSP, Cvitić I, Peraković D. Enhancing Industrial IoT Network Security through Blockchain Integration. Electronics. 2024; 13(4):687. https://doi.org/10.3390/electronics13040687
Chicago/Turabian StyleBobde, Yash, Gokuleshwaran Narayanan, Manas Jati, Raja Soosaimarian Peter Raj, Ivan Cvitić, and Dragan Peraković. 2024. "Enhancing Industrial IoT Network Security through Blockchain Integration" Electronics 13, no. 4: 687. https://doi.org/10.3390/electronics13040687