Sensors and machine learning and AI operation-constrained process control method for sensor-aided industrial internet of things and smart factories

Muruganandam, S.; Anas, A. Salameh; Mohd, Affendi Ahmad Pozin; Manikanthan, S.V.; Padmapriya, T.

doi:10.1016/j.measen.2023.100668

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…This structured procedure will enable the company to make an informed decision to achieve a good trade-off between improving monitoring and control of equipment and minimizing sensor installation costs. Apart from maintenance optimization and performance improvements, careful monitoring leads to increased efficiency and productivity, as noted by various authors in the literature [ 64 , 65 ]. By identifying potential failures or issues early on, companies can proactively address them before they escalate into major problems that could result in downtime, loss of revenue, and even safety hazards.…”

Section: Discussionmentioning

confidence: 99%

Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry

Pellicer

Tungekar

Carpitella

2023

Sensors

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Industry 4.0 technologies offer manufacturing companies numerous tools to enhance their core processes, including monitoring and control. To optimize efficiency, it is crucial to effectively install monitoring sensors. This paper proposes a Multi-Criteria Decision-Making (MCDM) approach as a practical solution to the sensor placement problem in the food industry, having been applied to wine bottling line equipment at a real Italian winery. The approach helps decision-makers when discriminating within a set of alternatives based on multiple criteria. By evaluating the interconnections within the different equipment, the ideal locations of sensors are suggested, with the goal of improving the process’s performance. The results indicated that the system of electric pumps, corker, conveyor, and capper had the most influence on the other equipment which are then recommended for sensor control. Monitoring this equipment will result in the early discovery of failures, potentially also involving other dependant equipment, contributing to enhance the level of performance for the whole bottling line.

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Section: Discussionmentioning

confidence: 99%

Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry

Pellicer

Tungekar

Carpitella

2023

Sensors

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“…Similarly, propounded a learned approximation pass message network tailored to the intricacies of beam space millimeter-wave large MIMO systems. Echoing this theme [ 23 ] unfolded a novel approach predicated on deep denoising neural networks, orchestrating a virtuous interplay with Compressed Sensing (CS) channel estimation strategies within IRS systems [ 24 ]. in a parallel vein, engineered a DL-centric approach that endowed IRS with an acumen to judiciously interface with signals.…”

Section: Literature Reviewmentioning

confidence: 99%

Advancing reliability and efficiency of urban communication: Unmanned aerial vehicles, intelligent reflection surfaces, and deep learning techniques

Li,

Qiang

2024

Heliyon

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“…We use the Matlab simulator to carry out the test in order to assess the effectiveness of our suggested EPEGASIS technique [15] [16]. With regard to standard PEGASIS, we will contrast our suggested algorithm.…”

Section: Situational Simulationmentioning

confidence: 99%

“…Dr. S. V. Manikanthan (10), national journal (15), international conferences (15), and national conferences (10), and has participated in several workshops and FDP. He is a Board of Member in Universities like Puducherry Technical University, Sathyabama University, Madurai Kamarajar University, Dhanalakshmi Srinivasan University, and Bharath University (E-mail: baskarank@citchennai.net).…”

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confidence: 99%

Cluster-Factors of Mobile Sensor Network Technology for Security Enhanced PEGASIS

T. K. Sivakumar,

Saurabh Dhyani,

S.V. Manikanthan

et al. 2023

Int. J. Interact. Mob. Technol.

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Mobile wireless sensor networks (MWSNs) have been a hot topic of research, and numerous routing methods have been developed to increase energy efficiency and extend longevity. Nodes close to the sink often use more energy to transmit data from their neighbors to the sink, which causes them to run out of energy faster. These places are also referred to as rendezvous points, and choosing the best one is a hard task. The likelihood of choosing an ideal node as the rendezvous point will be extremely low because hierarchical algorithms only use their local information to select these places. The warm spot problem is addressed from four angles in this work using the Enhanced Power-Efficient Gathering in Sensor Information Systems (EPEGASIS) technique. In order to limit the amount of energy used during transmission, the ideal communication distance is first calculated. To balance the energy consumption among the nodes, mobile sink technology is employed once a threshold value is set to safeguard the dying nodes. The node can then modify its communication range based on how far away the sink node is from it. Thorough testing has been done to demonstrate that our suggested EPEGASIS works better in terms of longevity, drive usage, and web latency.

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Sensors and machine learning and AI operation-constrained process control method for sensor-aided industrial internet of things and smart factories

Cited by 12 publications

References 12 publications

Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry

Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry

Advancing reliability and efficiency of urban communication: Unmanned aerial vehicles, intelligent reflection surfaces, and deep learning techniques

Cluster-Factors of Mobile Sensor Network Technology for Security Enhanced PEGASIS

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