Decentralized Internet of Things

Alzubi, Jafar A.; Selvakumar, J.; Alzubi, Omar A.; Ramesh, Manikandan

doi:10.5958/0976-5506.2019.00295.x

Cited by 17 publications

(8 citation statements)

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“…Governments of all countries must speed up the digital upgrade of traditional industries, and integrate the digital and the real economies. Especially after the outbreak of COVID-19, the new economy, represented by cloud shopping, online medical care, and distance education, will not only improve the daily lives of residents but also reduce the negative impacts of potential supply chain disruptions [ 13 ]. The digital economy, with digital industrialization and industrial digitization at its core, has become a new major change in the world following the industrial and the information revolutions [ 14 ].…”

Section: Literature Reviewmentioning

confidence: 99%

The road to change: Broadband China strategy and enterprise digitization

Wang

Zhang

2022

PLoS ONE

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The digitization of a company necessitates not only the effort of the company but also state backing of network infrastructure. In this study, we applied the difference-in-differences method to examine the impact of the Broadband China Strategy on corporate digitalization and its heterogeneity using the data from Chinese listed firms from 2010 to 2020. The results show that the improvement in network infrastructure plays a vital role in promoting company digitization; this improvement is extremely varied due to variances in market demand and endowments. Non-state-owned firms, businesses in the eastern area, and technology-intensive businesses have profited the most. Among the five types of digitization, artificial intelligence and cloud computing are top priorities for enterprises. Our findings add to the literature on the spillover effects of broadband construction and the factors affecting enterprise digitalization.

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Section: Literature Reviewmentioning

confidence: 99%

The road to change: Broadband China strategy and enterprise digitization

Wang

Zhang

2022

PLoS ONE

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“…11,12 In this manner, IoT technology has changed the traditional architectures of monitoring procedures. [13][14][15][16][17] In addition to this, time-sensitive statistic compilation has extended the scope of real-time monitoring of an individual under observation. In this manner, IoT-assisted sensing devices have made it possible to observe both macroscopic and microscopic health and environmental events.…”

Section: Research Domainmentioning

confidence: 99%

IoT‐assisted irregular environmental event determination for health analysis of pregnant females

Manocha

2021

Trans Emerging Tel Tech

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Indoor environment pollution is one of the significant concerns related to the individual's health of every category. As a majority of the population is spending their time indoors, the research on environment monitoring is realized on priority. In this research, the correlation between the indoor environment and health is determined to predict the health severity of pregnant females in real-time. The proposed solution employs IoT-enabled smart sensors for data acquisition. The accumulated data is processed over the fog-cloud platform for data quantification. To classify the irregular events from the processed data, the machine learning-assisted Weighted-Naïve Bayes (W-NB) classification technique is deployed on the fog layer. Furthermore, a deep learning-inspired multilayered convolutional neural network-long short-term memory (M-CNN-LSTM) model is proposed to determine the scale of health severity. Moreover, a two-phased alert generation mechanism is proposed to deal with the case of a medical emergency by generating notifications to the concerned medical specialists. The performance of the system is validated by performing several experimental simulations over the environmental and health datasets comprised of nearly 38 356 instances. The performance of the proposed solution is evaluated in terms of event classification, severity prediction, temporal delay efficiency, and overall performance stability. The calculated outcomes registered considerable performance over the several state-of-the-art decision-making approaches.

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“…IoT is referred to as the network of physical objects that are embedded with software and are connected through the internet for providing security during exchange of a big volume of data. However, the purpose of connecting to the IoT devices for exchanging the data through internet faces security challenges for industries [1]. As a result, variety of malware variants and threats are newly emerging at a faster pace.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Particle Swarm and Gray Wolf Optimization Algorithm for IoT Intrusion Detection System

Krishna¹,

Thangavelu²

2021

IJIES

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Internet of Things (IoT) is a network that provides security for physical objects such as smart home appliance, smart machines and many more. The physical objects are assigned to a unique Internet address known as Internet Protocol (IP) that is used for data communication with the external entities of the network through the internet. The IoT devices are facing security issues due to the rapid increase in attacks that are launched by the intruders during data sharing through the internet. The detection of attacks is essential to provide a strong security mechanism for such threatening attacks. The proposed hybrid optimization algorithm utilizes the combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in this research. The PSO is known for its fast computation speed and has found extensive utility in data training as well as data estimation. The GWO is developed as an intrusion detection approach to classify data and to efficiently detect several of intrusions. The proposed hybrid GWO-PSO uses NSL-KDD data set with binary and multi class problem respectively for showing the effectiveness of the present work. The results obtained better accuracy value of 99.97 % when compared to the existing LSTM-RNN that achieved 97.72% of accuracy, whereas the multi class SVM obtained 98 % and modified rank-based information gain feature selection method showed 99.8 % of accuracy.

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Decentralized Internet of Things

Cited by 17 publications

References 0 publications

The road to change: Broadband China strategy and enterprise digitization

The road to change: Broadband China strategy and enterprise digitization

IoT‐assisted irregular environmental event determination for health analysis of pregnant females

Hybrid Particle Swarm and Gray Wolf Optimization Algorithm for IoT Intrusion Detection System

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