Intelligent Edge Computing in Pervasive Environments

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https://hdl.handle.net/10125/102622

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    Pervasive and Connected Digital Twins for Edge Computing Enabled Industrial Applications
    ( 2023-01-03) Sanfilippo, Filippo ; Langås, Even Falkenberg ; Bukhari, Halima ; Robstad, Stian
    A digital twin (DT) is a digital representation of a physical asset that serves as its counterpart — or twin. DTs differ from static, three-dimensional models in that they are continuously updated with data from numerous sources. In one continually changing world of pervasive computing, where computational and human intelligence are expanding everywhere, DTs can be regarded as the backbone for addressing the synergy of software, devices, movable objects, networks, and people. In this paper, we present a novel perspective for designing, prototyping and testing pervasive and connected DTs for edge computing enabled industrial applications. The provided paradigm allows for the creation of computational models for cloud computing as well as the transmission of data and computational intelligence through analytic platforms. A case study is presented to demonstrate the possibilities of the suggested framework. According to the outlined findings, the proposed architecture contributes to effective maintenance and management of infrastructures and facilities.
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    Creating Intelligent Computational Edge through Semantic Mediation
    ( 2023-01-03) Juric, Radmila ; Ronchieri, Elisabetta ; Suh, Sang
    This research proposes semantic mediation based on reasoning and the first order logic for mediating the best possible configuration of Computational Edge, relevant for software applications which may benefit for running computations with proximity to their data sources. The mediation considers the context in which these applications exist and exploits the semantic of that context for decision making on where computational elements should reside and which data they should use. The application of semantic mediation could address the initiative to accommodate algorithms from predictive and learning technologies, push AI towards computational edges and potentially contribute towards creating a computing continuum.