
handle: 10630/36352
Beyond fifth-generation (B5G) communication networks and computation paradigms in the edge are expected to be integrated into power grids infrastructures over the next years. In this sense, AI technologies will play a fundamental role to efficiently manage dynamic information flows of future applications, which impacts the authorization policies applied in such a complex scenario. This article studies how Digital Twins can evolve their context-awareness capabilities and simulations technologies to anticipate faults or to detect cyber-security issues in real time, and update access control policies accordingly. Our study analyzes the evolution of monitoring platforms and architecture decentralization, including the application of machine learning and blockchain technologies in the Smart Grid, towards the goal of implementing autonomous and self-learning agents in the medium and long term. We conclude this study with future challenges on applying Digital Twins to B5G-based Smart Grid deployments.
blockchain, Autorizaciones, Intelligence, Fog Computing, intelligence, 004, Digital Twin, Blockchain, edge computing, digital twin, Authorization, authorization, Edge Computing, fog computing, Smart Grid, smart grid,, 5G
blockchain, Autorizaciones, Intelligence, Fog Computing, intelligence, 004, Digital Twin, Blockchain, edge computing, digital twin, Authorization, authorization, Edge Computing, fog computing, Smart Grid, smart grid,, 5G
| selected citations These citations are derived from selected sources. 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). | 59 | |
| 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 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
