A survey on digital twin for industrial internet of things: Applications, technologies and tools
Digital twin for the industrial Internet of Things (DT-IIoT) creates a high-fidelity, fine-grained,
low-cost digital replica of the cyber-physical integrated Internet for industry. Powered by …
low-cost digital replica of the cyber-physical integrated Internet for industry. Powered by …
[HTML][HTML] Next-generation energy systems for sustainable smart cities: Roles of transfer learning
Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …
improving grid stability and meeting service demand. This is possible by adopting next …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Edge computing for internet of everything: A survey
In this era of the Internet of Everything (IoE), edge computing has emerged as the critical
enabling technology to solve a series of issues caused by an increasing amount of …
enabling technology to solve a series of issues caused by an increasing amount of …
[HTML][HTML] Deep and transfer learning for building occupancy detection: A review and comparative analysis
The building internet of things (BIoT) is quite a promising concept for curtailing energy
consumption, reducing costs, and promoting building transformation. Besides, integrating …
consumption, reducing costs, and promoting building transformation. Besides, integrating …
Physical security and safety of IoT equipment: A survey of recent advances and opportunities
The connectivity and intelligence of Internet of Things (IoT) equipment offer improved
services, but several technical challenges have emerged in recent years that hinder the …
services, but several technical challenges have emerged in recent years that hinder the …
[HTML][HTML] Ares: Adaptive resource-aware split learning for internet of things
Abstract Distributed training of Machine Learning models in edge Internet of Things (IoT)
environments is challenging because of three main points. First, resource-constrained …
environments is challenging because of three main points. First, resource-constrained …
Passive thermography based bearing fault diagnosis using transfer learning with varying working conditions
Bearing is one of the core components of any rotating machine, and its failure is widespread.
This reason drives continuous monitoring and detecting bearing faults during machine …
This reason drives continuous monitoring and detecting bearing faults during machine …
IoT network traffic classification using machine learning algorithms: An experimental analysis
Internet of Things (IoT) refers to a wide variety of embedded devices connected to the
Internet, enabling them to transmit and share information in smart environments with each …
Internet, enabling them to transmit and share information in smart environments with each …
Reliable Internet of Things: Challenges and future trends
The Internet of Things (IoT) is a vital component of many future industries. By intelligent
integration of sensors, wireless communications, computing techniques, and data analytics …
integration of sensors, wireless communications, computing techniques, and data analytics …