Blockchain-Empowered Digital Twins Collaboration: Smart Transportation Use Case
Abstract
:1. Introduction
- Interoperability: The models and strategies of the sharing policies (i.e., internal and external data) need to define the DTs data schema and the collaboration requirements.
- Authentication: In some scenarios in distributed manufacturing systems, the deployed DTs are owned by independent entities that want to collaborate. Therefore, securing a digital distributed manufacturing system needs efficient technology to acquire secure real-time data exchange and analysis across multiple participants.
- Distributed machine learning: A large-scale input data size from multiple participants needs to be analyzed to obtain accurate predictions about the potential risks within the distributed manufacturing system.
- Distributed decision-making: Centralizing suffers from single failure data, while decentralization suffers from lacking global data, so the decision-making consensus is required.
- Scalability and robustness: a system needs to accommodate a large number of DTs which represent multiple participants, e.g., objects, devices, machines, nodes, people, workstations, etc., within manufacturing systems. The distributed manufacturing system also needs to deal with multiple deployed DTs and simultaneously maintain the robustness at a required level, especially with hacked nodes and malfunctioning.
1.1. Contribution
- We explore how blockchain employing in DTs collaboration with highlighting the benefits of the combination.
- We propose the conceptual framework of the data driving-based DTs collaboration with the help of blockchain technology. The proposed framework consists of two components:
- 1
- The data-driven ledger-based predictive model is used to predict the potential risks using DT-driven operational data. The DLT performs intelligent and secure interoperability, including real-time operational data exchange, querying the real-time operational database, and dynamic interactions among the deployed DTs. At the same time, the distributed predictive model plays a vital role in developing and evaluating DT deployment locally using the DT-driven operational data.
- 2
- A distributed consensus algorithm to improve the decentralized DTs collaboration. The distributed decision-making algorithm develops based on the essence of the consensus mechanism and the dynamic prediction, which uses real-time DT-driven operational data. The developed distributed consensus algorithm can make most nodes agree on the potential risks and notify the decision-makers within the distributed manufacturing systems.
- We describe how the conceptual framework can be applied in smart transportation systems, i.e., smart logistics and railway predictive maintenance.
1.2. Paper Organization
2. Comparison with Other Existing Solutions of Blockchain Empowering Digital Twins Collaboration
2.1. Digital Twins Collaboration
2.2. Digital Twins and Blockchain
2.3. Digital Twins and Predictive Data Analysis
3. Proposed Conceptual Framework of Data-Driven Blockchain-Based Collaborative Digital Twins
3.1. High-Level Requirements for Digital Twins Collaboration
3.2. Components of the Proposed Framework
3.2.1. Data-Driven Ledger-Based Collaborative DTs for Predictive Analytics
Ledger-Based DTs Model
Data-Driven DTs Based Predictive Analytics
3.2.2. Consensus-Based Decision Making
4. Smart Transportation Use Case
4.1. Overview of Smart Transportation Industry
4.2. Selective Use Cases for Smart Transportation Industry
- 1
- Why do we have to use collaborative DTs in this use case?
- 2
- What are the data schema and requirements which DTs will represent?
- 3
- How could a DLT be used for data sharing to support collaborative DTs?
- 4
- How can the DTs-based operational data intelligence help gain insight to enhance the prediction about the potential risks?
- 5
- How could a distributed consensus algorithm be used to ensure a consensus of the decision-making based on the predicted potential risks?
4.2.1. Smart Logistics
Overview
DTs Collaboration in Logistics
Data Schema and Requirements for DTs
DLT and Logistics Operational Data Sharing
Data-Driven DTs Based Predictive Analytics
Consensus-Based Decision Making
4.2.2. Railway Predictive Maintenance
Overview
DTs Collaboration in Railway
Data Schema and Requirements for DTs
DLT and Railway Operational Data Sharing
Data-Driven DTs Based Predictive Analytics
Consensus-Based Decision Making
5. Discussion, Validation, and Future Direction
5.1. Validation of the Proposed Framework
Validation of the Smart Logistics Use Case
- The factory is responsible for transporting the loads to the suppliers. Each factory DT checks the smart contact to meet the load requirements. Then factory DT has all the data about the loading status, certificates, suppliers’ locations, and the number of batches written into the ledgers. Once the factory sends the load to the supplier, the blockchain network is updated.
- The supplier is responsible for transporting the load to the warehouse. Each supplier DT has frequently updated data about the loaded products, warehouses locations, and shipments date. The collected data about the products and shipments are written into ledgers, and then the blockchain network is updated.
- The logistics operator is responsible for updating all necessary records, including a packing list, order number, batch number, production data, etc. Each logistics operator DT has frequently updated data about the corresponding shipment data recorded by the operator. The recorded data are written into ledgers, and then the blockchain network is updated.
- The long haul carrier is used to carry the heavy shipment and transport them to the warehouses. Each carrier DT checks the smart contact to meet the rules of shipment transportation. As a result, the carrier DT frequently has the updated bill of loading, shipment details, the destination warehouses location, and diver details. The shipment data are written into ledgers, and then the blockchain network is updated about the shipment movement track.
- The warehouse is used to store the shipments. Each warehouse DT has the data about the stored products, including location, temperature, humidity, product items, etc. These updated warehouse DT data are used to check the product storing conditions concerning the smart contract rules to avoid product damages. The warehouse DT data, including the product quantity, are also used to check the smart contract for new orders. The stored product data are written into ledgers, and then the blockchain network is updated about the stored products.
- The delivery process is responsible for delivering the orders to the customers. Each delivery DT has updated data about the warehouse location, customer address, routing instruction, packing details, driver details, bills, etc. The delivering product data are written into ledgers, and then the blockchain network is updated about the products being delivered.
- A customer is a person who orders the product and who receives it. The customer DT has the data about the customer delivery address, customer ID, etc. The smart contract will check the delivery data based on the customer data, and then the delivering information is recorded into the ledgers. Once the delivery process is successfully completed, all the blockchain network participants are updated about completing the delivery process.
- The decision-making unit is responsible for deciding in case of potential risk for the products during the loading, storing, and delivering processes.
- Tracking. Blockchain network allows efficient tracking of the changes along the logistics process. Using the combination of emerging technologies like the blockchain, AI, and DTs can improve the productivity of the logistics process with effective tracking. The proposed framework can incorporate blockchain with DTs to get accurate data about every step in the shipping process. Once the products are loaded and shipped, the logistics participants’ DTs collaborate to exchange the logistics data. The logistics data is stored along with the information on the product movement [65]. Therefore, the blockchain network can provide the participants with the logistics by-product data like showing the person handling the product at that time. For example, the logistics system can track the product damage using the ledger-based logistics records in case of product damage. The smart contracts are settled, the logistics data will be stored in the public ledgers. All logistics records are stored to track the changes (i.e., what is the change, why it is done, and who made the changes). Furthermore, sharing information about logistics tracking with the customers across the blockchain network can increase the transparency of the logistics system.
- Delivery. To assess the potential risks within the decentralized logistics manufacturing delivery process (e.g., whether it is harmful to the products such as medicines and frozen food), a prediction is needed to estimate how long a refrigerated truck will require to arrive at one or more processing plants. The proposed framework can incorporate blockchain with AI and DTs to predict the potential risks in advance and then take the appropriate action like routing redirection [64]. On the other hand, the proposed framework can improve the secure delivery process by reducing fraud and theft issues. To do this, the smart contracts check the detailed rules, such as requiring government-approved photo IDs to access the goods for pickup or delivery.
- Performance monitoring. Based on the components of the proposed framework (see Section 3.2), the predictive data analysis component is used to monitor the performance of the logistics process by analyzing the product data which are collected from logistics participants DTs including factory DT, supplier DT, long haul carrier DT, logistics operator DT, warehouse DT, and delivery DT. These DTs are collaborating and interacting to feed the learning models to predict the potential risk of the product, such as harmful product, damage, theft, and so on. The decision is making based on the consensus to avoid the risk such as logistics process delay.
5.2. Future Directions
5.2.1. Security and Privacy
5.2.2. Connectivity
5.2.3. Global Logistic Networks
5.2.4. Timing, Speed, and Response
5.2.5. Packaging and Containers in Transportation
5.2.6. Decision-Making Process
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Ref | Highlighted | Blockchain | DT | Collaboration | Data Analysis | I4.0 | Transp |
---|---|---|---|---|---|---|---|
[11] (2020) | ManuChain model is proposed based on the incorporation of blockchain into DT on a decentralized manufacturing | ✓ | ✓ | X | X | ✓ | X |
[38] (2019) | Makerchain model based on DTs is proposed to handle the cyber-credit of social manufacturing among various makers | ✓ | ✓ | X | X | ✓ | X |
[40] (2020) | ExplorerChain is a reference for researchers to implement and deploy blockchain technology in the healthcare/genomic domain using online machine learning | ✓ | X | X | ✓ | ✓ | X |
[39] (2020) | MBCoT architecture for the configuration of a secure, traceable, and decentralized manufacturing | ✓ | X | X | X | ✓ | X |
[12] (2019) | A protocol based on DLT to guarantee the transfer of values between DTs in economic systems | ✓ | ✓ | X | X | ✓ | X |
[29] (2020) | The homogeneous and heterogeneous of multi-robot collaboration to perform their complex task in decentralized fashion with blockchain technology | ✓ | X | ✓ | X | X | X |
[45] (2019) | Aledger-based DT reference model for predictive maintenance | ✓ | ✓ | X | ✓ | ✓ | X |
[41] (2019) | A framework for secure DT data sharing based on DLT to track events and provenance information | ✓ | ✓ | X | X | ✓ | X |
[42] (2020 ) | A predictive DTs methodology based on ML using UAV case study | X | ✓ | X | ✓ | ✓ | X |
[43] (2020) | A smart city DT architecture using reasoning and ML to automated decision-making | X | ✓ | X | ✓ | ✓ | X |
[19] (2019) | Machine learning technique for multi-robot collaboration based on keeping connectivity, maintaining the quality of services, and improving mobility during tasks performances | X | X | ✓ | ✓ | X | X |
[22] (2019) | Drones and IoT devices collaborate to improving greener and smarter cities | X | X | ✓ | X | X | ✓ |
[49] (2018) | DT monitoring that for monitoring and development of wind farms. | X | ✓ | X | ✓ | X | X |
[50] (2017) | An approach for identifying the network physical vulnerabilities in industry 4.0 systems. | X | X | ✓ | X | ✓ | X |
[51] (2014) | AI-based supervisory control and data acquisition method for prediction and fault diagnosis of wind turbines | X | X | X | ✓ | X | X |
[33] (2019) | A conceptual architecture and model for smart manufacturing relying on service-based DTs | X | ✓ | X | X | ✓ | X |
[48] (2019) | Advanced physics-based modeling approach for predictive maintenance using DTs | X | ✓ | X | ✓ | ✓ | X |
[46] (2016) | A model-based machine predictive maintenance based on DTs and a simulation platform | X | ✓ | X | ✓ | ✓ | X |
[47] (2018) | A modular-based corrective maintenance methodology using DTs to automate decision making in complex systems | X | ✓ | X | ✓ | ✓ | X |
[34] (2018) | Discussion of the DT and big data in smart manufacturing in terms of applications, production, manufacturing, maintenance prediction | X | ✓ | X | ✓ | ✓ | X |
[37] (2019) | A tool and technologies for DT in smart manufacturing | X | ✓ | X | X | ✓ | X |
[36] (2020) | A DT-driven sustainable technique smart manufacturing | X | ✓ | X | X | ✓ | X |
[35] (2020) | Focusing on DTs with Industry 4.0 and data analytics | X | ✓ | X | ✓ | ✓ | X |
[52] (2018) | Investigation wind farm and power consumption for smart manufacturing using IoT and DTs to perform wind turbines maintenance. | X | ✓ | X | ✓ | ✓ | X |
[23] (2020) | Collaboration of drone and IoT to enhance smartness of smart cities applications | X | X | ✓ | X | X | X |
[18] (2021) | A conceptual framework for DTs collaboration to provide an auto-detection of erratic operational data by utilizing the intelligence of operational data in the manufacturing systems. | X | ✓ | ✓ | ✓ | ✓ | X |
Our work | A conceptual framework for blockchain based DTs collaboration to improve DTs collaboration in transportation systems and focuses on real-time operational data analytics. | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Req. No | Requirement | Reason |
---|---|---|
R1 | Data collection | supporting data-driven decision making |
R2 | Data update frequency | providing realtime update of the physical twin |
R3 | Data analysis | enabling advanced predictions of the potential risks |
R4 | Simulation capabilities | enabling virtual visibility of the products |
R5 | Data exchange | allowing internal and external data sharing |
R6 | Authentication | maintaining trust among network peers |
R7 | Transparency | allowing traceability across the entire network |
R8 | Distributed decision making capabilities | providing insightful consensus-based decision making process |
R9 | Decentralization | delivering efficient and reliable solutions |
Ref | Highlighted | Blockchain | DT | Collaboration | Data Analysis | IoT | Selected Use Cases Logistics | Railway |
---|---|---|---|---|---|---|---|---|
[61] (2021) | MVDF technique for handling asynchronous generated from the connected vehicles in STS | X | X | X | ✓ | ✓ | X | X |
[62] (2021) | Enhancing the security and performance of nodes on the Internet of Vehicles. | X | X | X | X | ✓ | X | X |
[63] (2020) | Hybridized cryptographic integrated steganography algorithm for secured data sharing in IoT in cloud environment | X | X | X | X | ✓ | X | X |
[64] (2021) | IoT-assisted intelligent logistics transportation management framework to optimize logistics | X | X | X | X | ✓ | ✓ | X |
[65] (2020) | Exploring potential of using DT in synchromodal transport | X | ✓ | X | X | ✓ | X | X |
[44] (2020) | Using DTs to apply maintenance strategies in the industrial sectors. | X | ✓ | X | X | ✓ | X | X |
[1] (2020) | Identify a set of requirements to enable predictive maintenance for Industry 4.0 including railway. | X | X | X | X | ✓ | X | ✓ |
[66] (2018) | Exploring and analysis of adoption of blockchain in the railway | ✓ | X | X | X | X | X | ✓ |
[67] (2019) [68] (2020) | Proposing a DT-based maintenance model for cranes operating in control terminal. | X | ✓ | X | X | ✓ | ✓ | X |
[69] (2021) | Proposing multi-component based on DTS called Ilmatar for overhead cranes. | X | ✓ | X | X | ✓ | ✓ | X |
[70] ( 2020) | Proposing a robotic process automation solution to deliver service for the patients | X | ✓ | X | ✓ | X | ✓ | X |
[71] (2020 ) | Study the potential of using DTs to manage the COVID-19 | X | ✓ | X | ✓ | X | X | X |
[29] (2020) | A blockchain framework for heterogeneous multi-robot collaboration to combat COVID-19 | ✓ | X | ✓ | X | ✓ | X | X |
[22] (2019) | The collaboration between drone and IoT devices for improving Industry 4.0 applications such as smart city, smart healthcare | ✓ | X | ✓ | X | ✓ | X | X |
Ref | Highlighted | Technologies | Advantages | Limitations |
---|---|---|---|---|
[1] (2020) | Identify a set of requirements to enable predictive maintenance for Industry 4.0 including railway | Big data streaming technologies including -distributed queuing management, -big data stream processing, -big data storage, -streaming SQL engines | Provide a breadth-first mapping of predictive maintenance use-case requirements to the capabilities of big data streaming technologies focusing on open-source tools | Using the capabilities of blockchain and DTs Collaboration |
[74] (2016) | Discussing the possibility of applying predictive maintenance in the railway transportation industry | -RFID technologies -Business intelligence | Explore the most important positive effects of applying predictive maintenance that affect the organization, the economy, and the people in the whole system | Using the capabilities of blockchain and DTs Collaboration |
[75] (2018) | Proposing a cloud-based system for cost-effective and reliable real-time data collection, processing, and analysis from the shop floor with a multi-criteria decision-making algorithm and a condition-based maintenance strategy | -Cloud computing -Wireless sensor network -Data acquisition technologies | -Increasing awareness on both machine and shop-floor level condition -Effective and accurate maintenance of machine tools -Accurate decisions though condition-based maintenance and adaptive scheduling. -Increasing interoperability, automation and communication | Using the capabilities of blockchain and DTs Collaboration |
[76] (2018) | Reviews the overall framework to develop a DT coupled with the industrial IoT technology to advance aerospace platforms autonomy | -Industrial IoT -DTs | Discuss the role of data fusion in predictive maintenance using DT | Using the capabilities of blockchain and DTs Collaboration |
[77] (2020) | Investigating of creating an automation cell for the fan-blade reconditioning component of maintenance, repair, and overhaul services to ensure that fleets of aircraft are in airworthy conditions | -Vision sensor -DTs -Robotic technologies | Track and remove the coating material of a fan blade in a closed-loop approach | Using the capabilities of blockchain and DTs Collaboration |
Req. No | Requirement | Enabled by |
---|---|---|
R1 | Data collection | IoT technology |
R2 | Data update frequency | DT technology |
R3 | Data analysis | AI techniques |
R4 | Simulation capabilities | DT technology |
R5 | Data exchange | Blockchain DLT technology |
R6 | Authentication | Blockchain technology |
R7 | Visibility and transparency | Blockchain technology |
R8 | Distributed decision making capabilities | Consensus algorithms |
R9 | Decentralization | Blockchain DLT technology |
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Sahal, R.; Alsamhi, S.H.; Brown, K.N.; O’Shea, D.; McCarthy, C.; Guizani, M. Blockchain-Empowered Digital Twins Collaboration: Smart Transportation Use Case. Machines 2021, 9, 193. https://doi.org/10.3390/machines9090193
Sahal R, Alsamhi SH, Brown KN, O’Shea D, McCarthy C, Guizani M. Blockchain-Empowered Digital Twins Collaboration: Smart Transportation Use Case. Machines. 2021; 9(9):193. https://doi.org/10.3390/machines9090193
Chicago/Turabian StyleSahal, Radhya, Saeed H. Alsamhi, Kenneth N. Brown, Donna O’Shea, Conor McCarthy, and Mohsen Guizani. 2021. "Blockchain-Empowered Digital Twins Collaboration: Smart Transportation Use Case" Machines 9, no. 9: 193. https://doi.org/10.3390/machines9090193