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Farooq MS, Abdullah M, Riaz S, Alvi A, Rustam F, Flores MAL, Galán JC, Samad MA, Ashraf I. A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry. Sensors (Basel) 2023; 23:8958. [PMID: 37960657 PMCID: PMC10650216 DOI: 10.3390/s23218958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 10/24/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023]
Abstract
The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.
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Affiliation(s)
- Muhammad Shoaib Farooq
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan; (M.S.F.); (M.A.); (S.R.); (A.A.)
| | - Muhammad Abdullah
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan; (M.S.F.); (M.A.); (S.R.); (A.A.)
| | - Shamyla Riaz
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan; (M.S.F.); (M.A.); (S.R.); (A.A.)
| | - Atif Alvi
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan; (M.S.F.); (M.A.); (S.R.); (A.A.)
| | - Furqan Rustam
- School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland;
| | - Miguel Angel López Flores
- Research Group on Foods, Universidad Europea del Atlantico, Isabel Torres 21, 39011 Santander, Spain; (M.A.L.F.); (J.C.G.)
- Research Group on Foods, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Instituto Politécnico Nacional, UPIICSA, Ciudad de México 04510, Mexico
| | - Juan Castanedo Galán
- Research Group on Foods, Universidad Europea del Atlantico, Isabel Torres 21, 39011 Santander, Spain; (M.A.L.F.); (J.C.G.)
- Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
- Department of Projects, Universidade Internacional do Cuanza, Cuito EN250, Bie, Angola
| | - Md Abdus Samad
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Sharif N, Sharif N, Alzahrani KJ, Halawani IF, Alzahrani FM, Díez IDLT, Lipari V, Flores MAL, Parvez AK, Dey SK. Molecular epidemiology, transmission and clinical features of 2022-mpox outbreak: A systematic review. Health Sci Rep 2023; 6:e1603. [PMID: 37808926 PMCID: PMC10556267 DOI: 10.1002/hsr2.1603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 09/08/2023] [Accepted: 09/15/2023] [Indexed: 10/10/2023] Open
Abstract
Background and Aims The 2022-mpox outbreak has spread worldwide in a short time. Integrated knowledge of the epidemiology, clinical characteristics, and transmission of mpox are limited. This systematic review of peer-reviewed articles and gray literature was conducted to shed light on the epidemiology, clinical features, and transmission of 2022-mpox outbreak. Methods We identified 45 peer-reviewed manuscripts for data analysis. The standards of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement and Cochrane Collaboration were followed for conducting the study. Results The case number of mpox has increased about 100 times worldwide. About 99% of the cases in 2022 outbreak was from non-endemic regions. Men (70%-98% cases) were mostly infected with homosexual and bisexual behavior (30%-60%). The ages of the infected people ranged between 30 and 40 years. The presence of HIV and sexually transmitted infections among 30%-60% of cases were reported. Human-to-human transmission via direct contact and different body fluids were involved in the majority of the cases (90%-100%). Lesions in genitals, perianal, and anogenital areas were more prevalent. Unusually, pharyngitis (15%-40%) and proctitis (20%-40%) were more common during 2022 outbreak than pre-2022 outbreaks. Brincidofovir is approved for the treatment of smallpox by FDA (USA). Two vaccines, including JYNNEOSTM and ACAM2000®, are approved and used for pre- and post-prophylaxis in cases. About 100% of the cases in non-endemic regions were associated with isolates of IIb clade with a divergence of 0.0018-0.0035. Isolates from B.1 lineage were the most predominant followed by B.1.2 and B.1.10. Conclusion This study will add integrated knowledge of the epidemiology, clinical features, and transmission of mpox.
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Affiliation(s)
- Nadim Sharif
- Department of MicrobiologyJahangirnagar UniversitySavarDhakaBangladesh
| | - Nazmul Sharif
- Department of MathematicsRajshahi University of Engineering & TechnologyRajshahiBangladesh
| | - Khalid J. Alzahrani
- Department of Clinical Laboratories Sciences, College of Applied Medical SciencesTaif UniversityTaifSaudi Arabia
| | - Ibrahim F. Halawani
- Department of Clinical Laboratories Sciences, College of Applied Medical SciencesTaif UniversityTaifSaudi Arabia
| | - Fuad M. Alzahrani
- Department of Clinical Laboratories Sciences, College of Applied Medical SciencesTaif UniversityTaifSaudi Arabia
| | | | - Vivían Lipari
- Universidad Europea del AtlánticoSantanderSpain
- Universidad Internacional IberoamericanaAreciboPuerto RicoUSA
- Universidade Internacional do CuanzaCuitoBiéAngola
- Fundación Universitaria Internacional de ColombiaBogotáColombia
| | - Miguel Angel López Flores
- Universidad Europea del AtlánticoSantanderSpain
- Universidad Internacional IberoamericanaCampecheMéxico
- Instituto Politécnico NacionalUPIICSACiudad de MéxicoMéxico
| | - Anowar K. Parvez
- Department of MicrobiologyJahangirnagar UniversitySavarDhakaBangladesh
| | - Shuvra K. Dey
- Department of MicrobiologyJahangirnagar UniversitySavarDhakaBangladesh
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Khan A, Shafi I, Khawaja SG, de la Torre Díez I, Flores MAL, Galvlán JC, Ashraf I. Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants. Sensors (Basel) 2023; 23:7710. [PMID: 37765768 PMCID: PMC10537715 DOI: 10.3390/s23187710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/03/2023] [Accepted: 09/03/2023] [Indexed: 09/29/2023]
Abstract
Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO.
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Affiliation(s)
- Arooj Khan
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (A.K.); (I.S.); (S.G.K.)
| | - Imran Shafi
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (A.K.); (I.S.); (S.G.K.)
| | - Sajid Gul Khawaja
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (A.K.); (I.S.); (S.G.K.)
| | - Isabel de la Torre Díez
- Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Miguel Angel López Flores
- Research Group on Foods, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain; (M.A.L.F.); (J.C.G.)
- Research Group on Foods, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Instituto Politécnico Nacional, UPIICSA, Ciudad de Mexico 04510, Mexico
| | - Juan Castañedo Galvlán
- Research Group on Foods, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain; (M.A.L.F.); (J.C.G.)
- Universidad Internacional Iberoamericana Arecibo, Arecibo, PR 00613, USA
- Department of Projects, Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Iqbal F, Altaf A, Waris Z, Aray DG, Flores MAL, Díez IDLT, Ashraf I. Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction. Sensors (Basel) 2023; 23:s23115263. [PMID: 37299993 DOI: 10.3390/s23115263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/25/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply-demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data's security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users' privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user's wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes.
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Affiliation(s)
- Faiza Iqbal
- Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan
| | - Ayesha Altaf
- Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan
| | - Zeest Waris
- Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan
| | - Daniel Gavilanes Aray
- Research Group on Foods, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Research Group on Foods, Universidad Internacional Iberoamericana Arecibo, Arecibo, PR 00613, USA
- Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
| | - Miguel Angel López Flores
- Research Group on Foods, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Instituto Politécnico Nacional, UPIICSA, Ciudad de México 04510, Mexico
| | - Isabel de la Torre Díez
- Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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