Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology
Abstract
:1. Introduction
- An enhanced framework providing flexibility to accommodate different data sources and digital twin, as well as an integrated mechanism to disseminate the desired content while limiting exposition of the related IT systems. This creates a common understanding of interoperability.
- Integration of ontologies with the data sets, enabling data fusion techniques and a higher degree of flexibility for data manipulation, enabling automation in machine learning applications.
- Integration of data dissemination in an encrypted way which is immutable and isolated from other IT areas of the company, through the usage of Distributed Ledger Technology (DLT) solutions. Such automation accounts for the transparency principle. Obviously, its contribution must be understood as not being attached to the any particular DLT ledgers, but as a convenient data management approach to show its capabilities.
- Validation of the proposal through Proof of Concept, as deployed in an industrial case involving a real manufacturing company.
2. Literature review
- Data silos. At present, data produced by IIoT deployment devices and wearable devices are controlled or owned by different device manufacturers. Most of the existing value proposals for the IIoT deployment or wearable devices include one platform to manage the physical devices in an integrated way, as well as collecting the data from them by placing it into a private cloud for processing [32,33]. Benefiting from Radio-frequency identification (RFID) and sensor network technologies, common physical objects can be connected and are able to be monitored and managed by a single system [34]. However, as increasing types of IIoT and wearable devices from different vendors become available, more data silos are generated. The existence of such data silos not only jeopardize potential data-driven services and applications; the limited and restricted features provided also dramatically hinder the learning and inference capability derived from the higher requirements of data integration. Most importantly, all related stakeholders in I4.0 are looking forward to a transparent and shared information platform including production and operator data, which is difficult to realize due to the security constraints when passing between various data silos.
- Data ownership. It is an additional challenge for data producers (normally referred to as operators) to reuse (e.g., monetize) their data outside the data provider’s environments. The data producer cannot benefit from the data they generate, as the data is locked inside of the internal data environment of the enterprise. They lose their data ownership and the business opportunities stemming from those data are outside of the data owner’s control. Under this perspective, when referring to data related to humans, the EU General Data Protection Regulation (GDPR) is as effective tool to add significant trust to this dimension, as users are now able to understand their rights and privacy.
- Privacy. When data are related to people, such as devices which are linked to apps by bluetooth, the apps are mostly designed to present the data to users and upload summaries to the cloud manufacturer [35]. However, data integration (especially when related to people) has limitations related to privacy and misuse [36,37]. Cyber security is vulnerable, as wearable device manufacturers have reduced their safety protocols and safety stack layers to enable cheap products, as they have been understood as only serving the end user in a local context. Therefore, there is a clear demand regarding the concept of building and embedding security and privacy controls into connected products, as well as the infrastructure itself. This is one of the implementations of the Privacy by Design (PbD) concept [38,39].
- Interoperability. IIoT devices, including wearable devices, are highly heterogeneous in terms of the underlying communication protocols, data formats, and technologies from different vendors. Such heterogeneous infrastructures, devices, and configurations have becomes a strong limitation for data integration and interoperability. By 2021, 25 billion sensor-enabled objects are expected to be connected to the IIoT, as reported by Gartner [40].
3. Proposed Architecture
- Public. This is the already-described mechanism, where anyone knowing the address of the receiver has the right to freely access to the content.
- Restricted. This is the case where sensitive information needs to be shared among only a limited number of stakeholders. To prevent unauthorized entities from reading the data, the message content is obfuscated by encrypting it before uploading it to the Tangle. DLT access at message level, including the encryption/decryption process, is demonstrated in Figure 3.
- Private. In this case, due to the specific content of the data, to prevent unauthorized entities from reading the data and to respect the GDPR enforcement rules, the message content is obfuscated by encrypting it before uploading it to the Tangle by using a private certificate, making it Private by Design (PbD).
- The DLT address, representing the interested entity or device;
- The DLT tag, representing the interested data set;
- The public key, for the user requesting the access to the DLT stored data;
- Selection criteria for the required data, such as from [DateTime] to [DateTime]; and
- Proof of worth for accessing the data.
4. Proof of Concept: An Industrial Scenario
- Production related information from ERP/MES/PLM system;
- Ultra wide band (UWB) indoor positioning system (IPS) to track crane position and crane operator movements, to better define the location for rebar bundles; and
- Smart band to monitor crane operator’s heart rate and blood pressure.
5. Discussion
- Data consistency and the use of different sources of information and/or different time periods or geographical positions;
- Data storage: although properly scrambled, masked, or blurred, the related persons probably are not aware such pieces of information do exist related to them; and
- Problems related to EU citizens when requesting products or services outside of the EU.
- Accountability regarding users; but also,
- Accountability within the organization.
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
DAG | Directed Acyclic Graph |
DL | Deep Learning |
DLT | Distributed Ledger Technology |
ERP | Enterprise Resource Planning |
GDPR | General Data Protection Regulation |
I4.0 | Industry 4.0 paradigm |
IIoT | Industrial Internet of Things |
IoT | Internet of Things |
IPS | Indoor Positioning System |
IT | Information Technology |
IWS | Industrial Wearable System |
JSON | JavaScript Object Notation |
JSON-LD | JavaScript Object Notation for Linked Data |
KPI | Key Performance Indicator |
LASFA | Lasim Smart Factory reference model |
MES | Manufacturing Execution System |
ML | Machine Learning Techniques |
OPC UA | Open Platform Communications Unified Architecture |
PbD | Privacy by Design |
PLM | Product Lifecycle Management |
UWB | Ultra Wide Band |
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Ontology List | ||
---|---|---|
IPS | Wearable | Production System |
Positioning Ontology [69,70] | MIMU-Wear Ontology [71] | MES ontology [72,73,74] |
IndoorGML [75] | SmartBAN Ontology [76] | ERP ontology [77,78] |
Navigation ontology [70,79,80] | HealthIoT Ontology [81] | PLM ontology [73] |
Indoor space ontology [82,83] | Fitbit Ontology [84] | |
Vital Sign Ontology [85] |
Semantic Modeling | |||
---|---|---|---|
Data Source | Data Storage | Ontology | JSON-LD |
MES (ISA-95) | Microsoft SQL database | MES ontology [72] | {
"@context": { "mes":"http://mesontology/schema", "gr": "http://purl.org/goodrelations/v1#", "pto": "http://www.productontology.org/id/", "xsd": "http://www.w3.org/2001/XMLSchema#", "gr:seriaNumber": { "@type": "xsd:int" }, "gr:description": { "@type": "xsd: string" }, "gr:amountOfThisGood": { "@type": "xsd:int" }, "gr:eligibleRegions": { "@type": "xsd: string" }, "gr:weight": { "@type": "xsd:float" } "mes:planificationId": "2270077239", "gr:seriaNumber": "AAAEEE3452XX", "gr:description": "bundle of rebars", "gr:amountOfThisGood": "20", "gr:eligibleRegions": "DE(Germany)", "gr:weight": "300", "gr:includes": { "@type": [ "gr:Individual", "pto:Rebar" ]} } |
Smart band | MongoDB | Vital Sign Ontology [85] | {
"@context": { "vso": "https://bioportal.bioontology.org/ ontologies/VSO", "xsd": "http://www.w3.org/2001/XMLSchema#", "vso: pulserate": { "@type": "xsd:int" }, "vso: systolicbloodpressure": { "@type": "xsd:int" }, "sumo": "http://www.adampease.org/OP/SUMO.owl", "sumo:timepoint": { "@type": "xsd:dateTime" }, "vso:pulserate" : "79", "vso:systolicbloodpressure": "111", "sumo:timepoint": "2020-04-08T11:20:00Z" } |
IPS (tracktio) | CSV file | Positioning Ontology [69] | {
"@context": { "positionpoint": "http://positioningontology/schema", "xsd": "http://www.w3.org/2001/XMLSchema#", "axis": "http://data.ign.fr/def/ignf#CoordinateSystemAxis", "axis: listofaxes": { "@type": "xsd:list" }, "sumo": "http://www.adampease.org/OP/SUMO.owl", "sumo:timepoint": { "@type": "xsd:dateTime" }, "positionpoint": { "axis: listofaxes": "7.299998 1.140002 1.499998" }, "sumo:timepoint": "2020-04-09T10:00:00Z" } } |
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Sun, S.; Zheng, X.; Villalba-Díez, J.; Ordieres-Meré, J. Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology. Sensors 2020, 20, 3046. https://doi.org/10.3390/s20113046
Sun S, Zheng X, Villalba-Díez J, Ordieres-Meré J. Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology. Sensors. 2020; 20(11):3046. https://doi.org/10.3390/s20113046
Chicago/Turabian StyleSun, Shengjing, Xiaochen Zheng, Javier Villalba-Díez, and Joaquín Ordieres-Meré. 2020. "Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology" Sensors 20, no. 11: 3046. https://doi.org/10.3390/s20113046
APA StyleSun, S., Zheng, X., Villalba-Díez, J., & Ordieres-Meré, J. (2020). Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology. Sensors, 20(11), 3046. https://doi.org/10.3390/s20113046