Purpose
The purpose of this paper is to discuss how the rush of technological change will consolidate the worldwide reach of the internet with more capacity, specifically to control the physical world, including the machines, industrial facilities and frameworks that characterize cutting-edge technology.
Design/methodology/approach
The data were collected from 203 respondents predominantly from emerging economies, specifically India and SEA. Most of the participants are working professionals. Structural equation modelling was used to analyze data, as it is a popular statistical technique because of its ability to model selected independent variables and take into account all possible forms of measurement error to test an entire theory.
Findings
The Industrial Internet of Things (IIOT) platform comprises four fundamental capabilities: connectivity, big data, advanced analytics and application development. The IIOT has the potential to provide a high level of synergies between the 4 Ms of manufacturing, namely, man, machine, material and method.
Research limitations/implications
The collected data are predominately from India and SEA (close to 75 per cent), while contributions from other regions are comparatively less, so the findings cannot be generalized to the global context.
Practical implications
It is in the interest of service providers to collaborate and provide a universal solution to retain legacy systems to minimize the investment and reduce the security threat, which could boost IIOT adoption while ensuring that manufacturers are able to leverage this new technology efficiently.
Originality/value
The framework obtained has good quality of validity and reliability indicators. Thus, an alternative framework has been added to customer expectation which is currently a popular topic in the technological changes.
In the modern era of technologies, the internet grows in the advancement of our day-to-day life like automation devices. The devices to set up industries with integrated cyber-physical systems and industrial IoT applications. Generative adversarial networks (GAN) can generate Cognitive feedback analysis with various data for both generator and discriminator in a supervised model. Neural networks are used for artificial intelligence algorithms, but in adversarial networks, feedback analytics is analyzed with the significance of data. The modern age of intelligent manufacturing will indeed be ushered in by Cyber-Physical Production Systems (CPPS). However, because of the connections between the virtual and physical worlds, CPPS would be subject to cross-domain assaults. Against Denial-of-Service (DoS) threats, this paper concentrates on complex performance feedback management of Cyber-Physical Systems (CPS). To begin, a swapping system modelling approach for the complex response feedback CPS is provided by analyzing the distinct effects of DoS assaults on the sensor-controller (S-C) and controller-to-actuator (C-A) channels, accordingly. Given the difference in bandwidth between the dual channels and the accused's energy cap, it is reasonable to conclude that an offender can only jam a single communication stream at a point and also that the possible number of successive DoS attacks is limited. Second, using a packet-based transfer scheme, a nested switching paradigm is built on the foundation of the switching mechanism, considering both the spatial heterogeneity and the temporal durability of DoS attacks. The probability of discriminator gets analyzed feedback data to check whether actual data or fake data is sampled, and it is generated. Cognitive feedback supports genetic algorithms to sample the feedback data in a system for advanced technologies.
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