&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;${\mathsf{ENCLOSE}}$&lt;/tex-math&gt; &lt;/inline-formula&gt;: An Enhanced Wireless Interface for Communication in Factory Automation Networks

Aijaz, Adnan

doi:10.1109/tii.2018.2817202

Cited by 16 publications

References 19 publications

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Mathematical modeling on application of wireless networks for industrial automation-factory automation

Ming

Hawari

Apandi

2021

J. Phys.: Conf. Ser.

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Since traditional automation relies on wires, the machines’ versatility will be restricted. Thus maintenance is required to prevent cable wear and breakage, which would be costly and highlighted the importance of wireless connectivity in overcoming some of the limitations of conventional factory automation. Moreover, two of the most critical considerations, reliability and latency, need to be fulfilled so that a factory automation system can perform flawlessly. However, in factory automation, 0ms is the perfect condition since many machines share the same network. Furthermore, it can only reduce latency to a certain extent but not to zero. This study focuses on wireless networks for Industrial Automation Factor Automation’s system architecture, including network devices and topology, and then demonstrates one of the wireless networks’ main technologies, retransmission. This paper develops a mathematical analysis based on Negative Acknowledgement (NACK)-based retransmission mode as default retransmission for field devices to send data frames to the gateway device periodically. The results demonstrate an automatic on-demand retransmission (AODR) scheme able to provide higher efficiency of retransmission rate.

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Mathematical modeling on application of wireless networks for industrial automation-factory automation

Ming

Hawari

Apandi

2021

J. Phys.: Conf. Ser.

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Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Zhen

Zhang

2020

IEEE Access

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Knowing number of nodes is the precondition of their locating and estimation of other parameters in wireless sensor network, actually it can be evaluated according to the signal received by the sensor array. The popular algorithms are only appropriate for the uncorrelated signals and circumstance of Gaussian white noise, the estimation precision will deteriorate at some harsh environments. Hence a new algorithm for determining coherent node number based on multiple feature extraction in wireless sensor network is presented in this paper. First, perform spatial difference smoothing to the array data. Then the eigenvalues and eigenvectors can be acquired by eigen-decomposition, consequently multiple features which are used for training are constructed. Finally, back propagation neural network and particle swarm optimization are exploited for calculating node number. Simulation results demonstrate that the algorithms has good performances under the background of colored noise and small samples.

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Sensor Automation for Industrial Applications and Prediction of Energy Level for Home Appliances Using Machine Learning Algorithm

Duraisamy

Natarajan

Jeya

et al. 2023

2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)

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<inline-formula> <tex-math notation="LaTeX">${\mathsf{ENCLOSE}}$</tex-math> </inline-formula>: An Enhanced Wireless Interface for Communication in Factory Automation Networks

Cited by 16 publications

References 19 publications

Mathematical modeling on application of wireless networks for industrial automation-factory automation

Mathematical modeling on application of wireless networks for industrial automation-factory automation

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Sensor Automation for Industrial Applications and Prediction of Energy Level for Home Appliances Using Machine Learning Algorithm

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