Technology Trade-offs for IIoT Systems and Applications from a Developing Country Perspective: Case of Egypt

Adly, Aya Sedky

doi:10.1007/978-3-030-24892-5_13

Cited by 11 publications

(5 citation statements)

References 28 publications

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“…Additionally, big data may be useful to optimize resource use when making informed decisions based on the availability of data related to COVID-19 cases. AI, machine learning, and the internet of things (IoT) could contribute significantly to this process [ 49 - 51 ].…”

Section: Resultsmentioning

confidence: 99%

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Adly¹,

Adly²,

Adly³

2020

J Med Internet Res

Self Cite

128

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Background Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic. With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases. Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease. Objective The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas. This may be of great help to many health care administrators, computer scientists, and policy makers worldwide. Methods We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19. Results Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19. These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine. Conclusions We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration. Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies. Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed.

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Section: Resultsmentioning

confidence: 99%

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Adly¹,

Adly²,

Adly³

2020

J Med Internet Res

Self Cite

128

View full text Add to dashboard Cite

show abstract

“…For the purpose of optimization, an Adaptive Moment Estimation (adam) is used and the number of an epoch is set to 10. Again in this, the Rectified Linear Unit (ReLU) is used as an activation function [20] [22], [33], [47], [55], [61] and this ReLU uses the Softmax activation function. Fourthly, we have applied the Flatten layer.…”

Section: Algorithmmentioning

confidence: 99%

Encryption Techniques for Intelligent Transportation Systems via Deep Learning for IOV in Smart Cities

Choudhary¹,

Pahuja²

2021

Preprint

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Now-a-days, there is exponential growth in the field of Wireless Sensor Networks. A connected car is an ssential element of the Internet of Vehicles(IoV) vision that is a highly attractive application of the Internet of Things (IoT).The underlying technologies include Internet of Everything (IoE), artificial intelligence,machine learning, neural networks, sensor technologies, and cloud/edgecomputing. The connectivity between vehicles is through inter communicationbetween sensors and smart devices inside the vehicles, as well as smart systems inthe environment as part of the Intelligent Transportation Systems (ITS).In WSN’s security is a major concern, since most of communication happen through a wireless media hence probability of attacks increases drastically. Intrusion detection as well as prevention measures should be taken for secure communication, hence observations of intrusion detection and prevention techniques have taken immense precedence in the research field.With the help of intrusion detection and prevention systems, we can categorize the activities of user in two categories namely normal activities and suspicious activities.There is a need to design effective intrusion detection and prevention system by exploring deep learning for wireless sensor networks. This research aims to deal with proposing algorithms and techniques for intrusion prevention system using deep packet inspection based on deep learning. In this, we have proposed a deep learning model using a convolutional Neural Network classifier. The proposed model consists of two stages like intrusion detection and intrusion prevention. The proposed model learn useful feature representations from a large amount of labeled data and then classifies them. In this work, a Convolutional Neural Network is used to prevent intrusion for wireless sensor networks. To evaluate and test the effectiveness of the proposed system, a WSN-DS dataset is used, and experiments are conducted on the dataset. The experimental results show that proposed system achieves 97% accuracy and performs substantially better than the existing system. The proposed work can be used as a future benchmark for deep learning and intrusion prevention research communitiesin the smart cities now a days.

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“…Most analyses and investigations in the implementation of IIoT focus on the industrial world and have neglected the serious efforts to adopt the new technology [5]. One dominant focus of the IIoT research emphasizes the plethora of challenges that manufacturers face in implementing IIoT.…”

Section: Critical Review Of Iiot Literature Approachmentioning

confidence: 99%

“…Such barriers are not static and differ based on the availability of economic, social, cultural, and human capital. This makes each application of IIoT in manufacturing unique since it faces a different set of individual, institutional and structural barriers [5]. On another front, analyses of challenges of IIoT often concentrate on one level of analysis, technological systems-related factors, and ignore other equally important ones including individual and institutional [6].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Level Analysis of the Implementation of Industrial Internet of Things: Challenges and Future Prospects

2020

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Industrial Internet of Things (IIoT) is still a new research area. The main emphasis of the IIoT literature is on identifying the challenges involved in implementation of the IIoT. This paper summarizes the literature on the barriers faced by any stakeholder aiming to adopt IIoT anywhere. After reviewing 31 empirical studies, three domains of factors have been identified, individual (skills, abilities and knowledge), institutional (organizational/management-related), and structural (technical and economic infrastructures). A total of eleven factors across the three dimensions have been extracted. The most important factors were the absence of human capital (limited individual soft and technical knowledge, skills and abilities), low information security experience leading to a high probability data leaks and high management resistance from employees and leaders. To strengthen information for successful IIoT implementation, this paper proposes the mandate of Security, Education, Training, and Awareness (SETA) initiatives for any stakeholder interested in IIoT adoption. A Causal loop diagram for the IIoT implementation is also developed and discussed in this work.

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Technology Trade-offs for IIoT Systems and Applications from a Developing Country Perspective: Case of Egypt

Cited by 11 publications

References 28 publications

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Encryption Techniques for Intelligent Transportation Systems via Deep Learning for IOV in Smart Cities

A Multi-Level Analysis of the Implementation of Industrial Internet of Things: Challenges and Future Prospects

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