Opportunistic Internet of Things (OIoT): Elucidating the Active Opportunities of Opportunistic Networks on the Way to IoT

Malik, Ayasha; Bhushan, Bharat; Kumar, Anuj; Chaganti, Raj

doi:10.1007/978-3-030-99329-0_14

Cited by 9 publications

(2 citation statements)

References 59 publications

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“…In 2022, Malik et al 24 introduced the suggested opportunistic network based on IoT's qualities (opportunistic‐IoT) for discovering essential connections among individuals and smart devices. Here, this paper has implemented the links and evaluated the functionality with the realistic model.…”

Section: Literature Surveymentioning

confidence: 99%

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A design of predictive manufacturing system in IoT‐assisted Industry 4.0 using heuristic‐derived deep learning

Murugiah

Muthuramalingam

Anandamurugan³

2023

Int J Communication

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The predictive maintenance function is ensured with the earlier detection of errors and faults in the machinery before reaching its critical stages. On the other hand, the challenges faced by Internet of things (IoT) devices are the security problem because they can be easily attacked by comparing the other devices such as computers or portable devices. It cannot solve the highdimensional issues and imbalanced data. The computation cost is very expensive when using the modern sampling method. In addition, the conventional methods for predictive maintenance are incorporated with a single method. So, the maintenance and prognostic tasks are very hard to address simultaneously. Thus, a new predictive manufacturing system in Industry 4.0 for examining the machines is proposed. In the initial stage, the data are collected from IoT industry sensors. Considerably, the data cleaning is carried out, and deep features are extracted through the "Multi-Scale Dilation Attention Convolutional Neural Network (MSDA-CNN)." Further, the deep, weighted features are extracted, where the weight is optimized using the hybrid algorithm named Probabilistic Beetle Swarm-Butterfly Optimization (PBS-BO). In the end, the weighted features are given to the Optimized Hybrid Fault Detection (OHFD) that is performed by the "Deep Neural Network (DNN) and Deep Belief Network (DBN)." Finally, if any faults in machines are predicted, then the system sends alerts to the industrialists for suitable decision-making. The efficiency of the suggested model is evaluated on a set of real measurements in Industry 4.0.

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Section: Literature Surveymentioning

confidence: 99%

“…The "true positive and true negative values are shown as aa and an, respectively, and false positive and false negative values are given as bb and bn, respectively." The Precision prs is "the fraction of relevant instances among the retrieved instances," as in Equation (24).…”

Section: Ohfdmentioning

confidence: 99%