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      Cyber Physical Systems Dependability Using CPS-IOT Monitoring

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          Abstract

          Recently, vast investments have been made worldwide in developing Cyber-Physical Systems (CPS) as solutions to key socio-economic challenges. The Internet-of-Things (IoT) has also enjoyed widespread adoption, mostly for its ability to add “sensing” and “actuation” capabilities to existing CPS infrastructures. However, attention must be paid to the impact of IoT protocols on the dependability of CPS infrastructures. We address the issues of CPS dependability by using an epidemic model of the underlying dynamics within the CPS’ IoT subsystem (CPS-IoT) and an interference-aware routing reconfiguration. These help to efficiently monitor CPS infrastructure—avoiding routing oscillation, while improving its safety. The contributions of this paper are threefold. Firstly, a CPS orchestration model is proposed that relies upon: (i) Inbound surveillance and outbound actuation to improve dependability and (ii) a novel information diffusion model that uses epidemic states and diffusion sets to produce diffusion patterns across the CPS-IoT. Secondly, the proposed CPS orchestration model is numerically analysed to show its dependability for both sensitive and non-sensitive applications. Finally, a novel interference-aware clustering protocol called “INMP”, which enables network reconfiguration through migration of nodes across clusters, is proposed. It is then bench-marked against prominent IoT protocols to assess its impact on the dependability of the CPS.

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          A Contribution to the Mathematical Theory of Epidemics

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            Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy

            In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.
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              Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions

              Background The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                14 April 2021
                April 2021
                : 21
                : 8
                : 2761
                Affiliations
                Department of Computer Science, University of the Western Cape, Bellville, Cape Town 7535, South Africa; ooajayi@ 123456uwc.ac.za (O.A.); 3219279@ 123456myuwc.ac.za (H.M.)
                Author notes
                [* ]Correspondence: abagula@ 123456uwc.ac.za
                Author information
                https://orcid.org/0000-0003-0774-5251
                https://orcid.org/0000-0001-6583-3749
                https://orcid.org/0000-0001-8972-0167
                Article
                sensors-21-02761
                10.3390/s21082761
                8070778
                33919791
                47918eef-f18d-4fd4-a9d6-9e666a75d230
                © 2021 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
                : 16 February 2021
                : 16 March 2021
                Categories
                Article

                Biomedical engineering
                collection tree protocols,cyber physical systems,dependability,epidemic modelling,fourth industrial revolution (4ir),internet-of-things (iot)

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