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      Optimal Placement of Social Digital Twins in Edge IoT Networks

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          Abstract

          In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.

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          Edge Computing: Vision and Challenges

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            Digital Twin in Industry: State-of-the-Art

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              A Survey on Mobile Edge Computing: The Communication Perspective

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                30 October 2020
                November 2020
                : 20
                : 21
                : 6181
                Affiliations
                [1 ]DIIES Department, University Mediterranea of Reggio Calabria, 89100 Reggio Calabria, Italy; olga.chukhno@ 123456unirc.it (O.C.); nadezda.chukhno@ 123456unirc.it (N.C.); araniti@ 123456unirc.it (G.A.); claudia.campolo@ 123456unirc.it (C.C.)
                [2 ]Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland
                [3 ]Institute of New Imaging Technologies (INIT), Universitat Jaume I, 12071 Castelló de la Plana, Spain
                [4 ]DIMES Department, University of Calabria, 87036 Rende, Italy; antonio.iera@ 123456dimes.unical.it
                [5 ]Laboratoire des Signaux et Systémes (L2S), CentraleSupélec, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
                Author notes
                Author information
                https://orcid.org/0000-0003-4271-5744
                https://orcid.org/0000-0002-1466-5367
                https://orcid.org/0000-0001-8670-9413
                https://orcid.org/0000-0003-3281-6680
                https://orcid.org/0000-0001-5414-8252
                https://orcid.org/0000-0003-2731-300X
                Article
                sensors-20-06181
                10.3390/s20216181
                7662998
                33143038
                5f4de543-aa72-4991-bf92-0a5ee55f0636
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 02 October 2020
                : 27 October 2020
                Categories
                Article

                Biomedical engineering
                internet of things,social internet of things,edge computing,digital twin,optimization problem

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