The Industrial Internet of Things (IIoT) and Digital Twins (DTs) are changing how digital models and physical products interact. IIoT connects to intelligence in the physical world, and DTs virtually represent their physical counterparts. As a result, DTs will be invaluable for testing and...

Executing complex and time-sensitive operations has become difficult due to the increased acceptance of Internet of Things (IoT) devices and IoT-generated big data, which can result in problems with power consumption and lag time. The fog computing layer is a distributed computing option that...

The Internet of Things (IoT), cloud computing, and machine learning opened an opportunity for new smart systems. These technologies have triggered huge traffic and delay by continuously transmitting telemetry data to the cloud. IoT edge choice made decision-making closer to the environment,...

The fog computing paradigm is promising for deploying various delay-sensitive Internet of Things (IoT) applications. The resource-constrained fog devices restrict the number of application deployments due to a lack of efficient resource estimation and discovery mechanisms for various emergent...

The importance of waste management, including collection, separation, recovery, and recycling, increases with the growing amount of waste. Technological innovations such as smart connected products, the Internet of Things, and digital twins are driving the development of smart management...

Internet of Things (IoT) enabled networks are highly vulnerable to cyber threats due to insecure wireless communication, resource constraint architecture, different types of IoT devices, and a high volume of sensor data being transported across the network. Therefore, IoT-compatible...

The growing demand for data-driven decision-making and the emergence of new business models has led to a rise in digital technologies. As a result, businesses have become more aware of the impact of their activities on the environment, including the production of digital technologies....

This study proposes a novel entropy function for the intuitionistic fuzzy sets to improve the existing fuzzy entropy measure. We introduce an attitude-based entropy measure by methodically calculating the experts' attitude values through variance approach. We discuss several important properties...

The generation of huge data with high velocity creates alteration in the distribution of the stream data, which is defined as the concept of drifts. The concept drifts negatively influence the classification accuracy and the stability of the data streams. Numerous machine learning-based models...

The principle of how hazardous pollutants are annulled by rain, dew or precipitations has been a subject of interest in addressing environmental challenges and climate change. This scavenging process when studied generates nonlinear dynamic models that require robust techniques for obtaining...