{"id":875,"date":"2025-04-27T08:16:39","date_gmt":"2025-04-27T08:16:39","guid":{"rendered":"https:\/\/els-journal.net\/wp\/about\/"},"modified":"2025-04-27T08:16:39","modified_gmt":"2025-04-27T08:16:39","slug":"29101-intelligent-iot-surveillance-and-instantaneous-management","status":"publish","type":"page","link":"https:\/\/els-journal.net\/wp\/?page_id=875","title":{"rendered":"29101 Intelligent IoT Surveillance and Instantaneous Management"},"content":{"rendered":"\n\n\n

Vol. 29, No. 1 – June 2025<\/h3>\n

Intelligent IoT Surveillance and Instantaneous Management<\/h3>\n

https:\/\/doi.org\/10.53314\/ELS2529003H<\/h5>\n
Ying Huang, Yongmei Su, and Shuanggui Lu<\/h5>\n
Abstract<\/b><\/h5>\n
Unmanned Vehicles (UVs) and the Industry Internet of Things (IoT) are two examples of new technology being incorporated <\/span>into manufacturing processes during the fourth industrial <\/span>revolution. IT networks must be machine-compatible to integrate <\/span>these technologies; this includes addressing problems with connectivity, <\/span>fog, and cloud-based computing security, lowering latency, <\/span>and improving data reliability and standard of service. Regarding <\/span>IoT, AI techniques must handle resource management, network<\/span><\/h5>
deployment, and these problems. The significant issues are unstable and high-latency communications between Industrial IoT endpoints <\/span>and the Cloud. By extending storage and computation to <\/span>the network\u2019s edge, fog computing offers a valuable tool for merging <\/span>intricately linked processing systems. Interoperability may <\/span>be addressed using fog in an IoT gateway and advanced software <\/span>distributed on the edge. However, as an IoT gateway is essential <\/span>to processing and delivering data to many systems and platforms, <\/span>selecting one is critical regarding accuracy and latency. Intelligent <\/span>IoT monitoring and real-time control based on Integrating Autonomous <\/span>Robots for Instantaneous Industrial Operations, visual recognition, <\/span>and cloud\/edge computing services are proposed to address <\/span>these challenges. By deploying Deep Learning (DL) facilities <\/span>close to customers who want them, latency and processing costs <\/span>associated with transmitting data through the Cloud may be minimized. <\/span>The suggested methods enhance platform decision-making <\/span>and industrial automation system performance by integrating <\/span>cloud-based services into an operational loop. A smart approach <\/span>that offers a trade-off between accuracy and latency is suggested to <\/span>choose the right AI for the situation under observation.<\/span><\/h5>\n
Full text: <\/a><\/h5>\n\n\n\n\n\"cited<\/a>\n\n\n\n\nGoogle Scholar Citations <\/a>\n\n\n\n\n
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