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handle: 10251/213001
[EN] Digital Twins are software technologies that enable the modelling of real-world phenomena in digitised environments, representing and monitoring the reality of various processes, including IoT deployments. Since 2017, the use of Digital Twins has been increasing. However, in the road transport and logistics realm, the adoption rate remains low, primarily due to the costs of processing and validating data in centralised scenarios, among other factors. On the other hand, Blockchain technologies were created to provide immutable and decentralised data storage in diverse scenarios, adding a layer of isolation and reliability in heterogeneous solutions. This paper presents a case study proposing a Digital Twin based on open-source Blockchain technologies, such as FIWARE Canis Major. The primary goal is to design and implement a robust and efficient open-source architecture that allows for the control and optimisation of vehicle fleet allocations in logistics/transport companies within supply chain management. This case study aims to showcase the practical application of the proposed solution in a real-world context, providing insights into its eco-friendly and low-cost attributes while opening the door to a large number of additional applications.
This research has been partially funded by the European Commission, under the Horizon 2020 project OPTIMAI, grant number 958264, and the Spanish Regional Program Agencia Valenciana Innovacio with grant number INNEST/2021/164.
Ethereum, Blockchain, INGENIERÍA TELEMÁTICA, Canis major, Digital twin, FIWARE, Smart logistics
Ethereum, Blockchain, INGENIERÍA TELEMÁTICA, Canis major, Digital twin, FIWARE, Smart logistics
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 7 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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