The traditional supply chain system included smart objects to enhance intelligence, automation capabilities, and intelligent decision-making. Internet of Things (IoT) technologies are providing unprecedented opportunities to enhance efficiency and reduce the cost of the existing system of the supply chain. This article aims to study the prevailing supply chain system and explore the benefits obtained after smart objects and embedded networks of IoT are implanted. Short-range communication technologies, radio frequency identification (RFID), middleware, and cloud computing are extensively comprehended to conceptualize the smart supply chain management system. Moreover, manufacturers are achieving maximum benefits in terms of safety, cost, intelligent management of inventory, and decision-making. This study also offers concepts of smart carriage, loading/unloading, transportation, warehousing, and packaging for the secure distribution of products. Furthermore, the tracking of customers to convince them to make more purchases and the modification of shops with the assistance of the Internet of Things are thoroughly idealized.

The internet of things (IoT) is one of the growing platforms of the current era that has encircled a large population into its domain, and life appears to be useless without adopting this technology. A significant amount of data is generated from an immense number of smart devices and their allied applications that are constructively utilized to automate our daily life activities. This big data requires fast processing, storage, and safe passage through secure channels to safeguard it from any malicious attacks. In such a situation, security is considered crucial to protect the technological resources from unauthorized access or any interruption to disrupt the seamless and ubiquitous connectivity of the IoT from the perception layer to cloud computers. Motivated by this, this article demonstrates a general overview about the technology and layered architecture of the IoT followed by critical applications with a particular focus on key features of smart homes, smart agriculture, smart transportation, and smart healthcare. Next, security threats and vulnerabilities included with attacks on each layer of the IoT are explicitly elaborated. The classification of security challenges such as confidentiality, integrity, privacy, availability, authentication, non-repudiation, and key management is thoroughly reviewed. Finally, future research directions for security concerns are identified and presented.

The growing energy demand is attracting a lot of interest in heavy crude oil. While various conventional methods have been employed to reduce the viscosity of crude oil, the challenges associated with these techniques have driven researchers to seek economical and environmentally sustainable alternatives. In this context, combining traditional methods, such as solvent treatment, with ultrasonic radiation presents a promising, yet complex solution. In this study, we develop a machine learning-based algorithm to rigorously predict the synergistic effects of ultrasonication and solvation on crude oil viscosity. This research is divided into two parts. First, we conducted experimental measurements of crude oil viscosity following treatment with ultrasound and/or n-heptane solvent. It was found that the optimum irradiation period for samples containing 0% to 16% n-heptane was 8 min. Notably, after 8 min of treatment, the viscosity decreased by over 34% for samples with 16% n-heptane and by more than 47% for those with 0% n-heptane. When the n-heptane concentration was increased to 22% and 30%, the required sonication time increased by 2 to 10 min. In the former case, viscosity was reduced by more than 50%, while in the latter, it decreased by over 48%. In the second part of the study, a Machine Learning (ML) model was developed using the experimental data. In particular, a Random Forest Regressor (RFR) model was applied, with results demonstrating high reliability based on RMSE and R² values for training (3.3395, 0.9764), validation (3.0166, 0.9602), and testing (2.4778, 0.9557). Considering the significance of features, n-Heptane (0.54) and irradiation time (0.45) were the key predictors, with n-heptane showing slightly greater impact on error reduction. The application of this model to additional datasets from other oil fields shows significant promise for future research and practical implementation.