The exponential expansion of the Internet of Things (IoT) has fundamentally transformed the way people, machines, and gadgets communicate, resulting in unparalleled levels of interconnectedness. Nevertheless, the growth of IoT has also brought up notable security obstacles, requiring the creation of strong intrusion detection systems to safeguard IoT networks against hostile actions. This study investigates the utilization of fog computing architectures in conjunction with machine learning approaches to improve the security of the IoT. The UNSW-NB15 dataset, containing an extensive range of network traffic characteristics, is used as the basis for training and assessing the machine learning models. The study specifically applies and evaluates the performance of various models, including linear regression, Ridge classifier, SGD classifier, and ensemble learning. Furthermore, the findings indicate that these models are capable of accurately identifying intrusions, with success rates of 94%, 97%, 96.60%, and 96.50%, respectively. The Ridge Classifier demonstrates exceptional accuracy, highlighting its potential for effective implementation in IoT security frameworks. The results emphasize the efficacy of combining machine learning with fog computing to tackle the distinct security obstacles faced by IoT networks. In the future, our work will prioritize optimizing these models for real-time applications, improving their scalability, and investigating more advanced ensemble strategies to enhance detection accuracy. The project intends to enhance these areas to create a comprehensive and scalable intrusion detection system that can offer strong security solutions for the growing IoT environment. This will guarantee the integrity and dependability of linked devices and systems.

The Internet of Things (IoT) has become essential for business. The adoption rate of IoT has dropped recently and this could be due to security, privacy, and trust issues. Blockchain (BC) has the potential to mitigate the risk of security, privacy, and trust. However, few studies examined the integration between IoT and BC in the context of developing countries. The purpose of this study is to examine the predictors of IoT adoption by telecommunication companies in the Gulf Cooperation Council (GCC). In addition, the study aims to examine the moderating role of BC as well as the effect of using IoT and BC on the competitive advantage of companies. Based on technology acceptance model, social exchange theory, and resource-based view, the study proposed that security, privacy, trust, communication quality, perceived ease of use (PEOU), and perceived usefulness (PU) affect positively the adoption of IoT. BC is proposed as a moderating variable and expected with IoT to affect the competitive advantage of companies. The population includes all the telecommunication companies in GCC. Data was collected using purposive sampling from IT professionals. The results of data analysis using SmartPLS showed that security, privacy, trust, PU, and PEOU positively affected the adoption of IoT. BC and IoT adoption have a positive effect on competitive advantage. Further, BC moderated only the effect of security and privacy on the adoption of IoT. Services providers must enhance the security, privacy, and trust of IoT services by deploying BC technology. Effective integration of IoT and BC will lead to the achievement of competitive advantages.

Many people are aware of taking care of the global environment, so they demand environmentally friendly business activities. The government also has responded to the commotion by requiring companies to produce friendly and safe products or services to their customers. Hotel industries react to it by implementing the concept of green hotels. The purpose of this study is to examine whether the Internet of Things, strategic green purchasing and green operation have impacted green employee behavior in star-hotels in East Java. Eighty-two (82) questionnaires were distributed, but only 62 questionnaires were valid, with a response rate of 75.60 %. SEM-PLS (Structural Equation Modelling Partial Least Square) was used to analyze the data. The results show that the Internet of Things (IoT) has a significant impact on green hotel operation, with the T-statistic value of 0.378. green purchasing has a significant impact on green hotel operation, with the T-statistic value of 0.545, and green employee behavior, with the T-statistic value of 0.346. The Internet of things (IoT) has no significant impact on green employee behavior directly but through green hotel operation. The use of energy efficiency and the existence of good waste management as indicators of green hotel operation has an impact on green employee behavior of 0.346.

Implementing sustainable and smart supply chain practices have a great impact on the performance of an organization. In today’s globalized and highly industrialized world, sustainability is recognized as one of the highest priorities of all organizations. Evolution of internet-based technologies, digital platforms and big data analytics have paved the way for redesigning supply chains to be smart, agile, and resilient. Therefore, the implementation of practices related to these two concepts is found to improve the supply chain related organizational performance. This research aims to investigate empirically the impact of these two practices on improving the supply chain organizational performance in the Small and Medium Enterprises (SMEs) of India. This research considered the dimensions and the variables related to sustainable supply chain and smart supply chain practices in SMEs in India which were not considered in research contributions prior to this. Therefore, this research becomes a unique contribution to the existing body of knowledge. Empirical analysis was carried out on data from 92 SMEs from Telangana State in India, collected using a questionnaire. The directory of SMEs of Government of Telanagana, India was used to select the cluster sample of SMEs as respondents, based on a criterion using exploratory research methodology. SPSS software was used to test the model. Regression and ANOVA were used for this purpose. Findings of this research reveal significant influence of sustainable and smart supply chain (SC) practices on improving SC organizational performance. Additionally, individually each of these practices also have a direct influence on the performance of SMEs. Obtaining responses from the representatives of SMEs was a challenge and limitation of this research while expanding the scope of this research to different geographical regions and clusters will be a topic for further research. The outcomes and results of this research provide significant contribution to the existing body of knowledge by filling the gaps and value-adding to the researchers, academicians, students, policy makers and industry practitioners.

The aim of this study is to explore the impact of supply chain 4.0 technologies (e.g., big data and Internet-of-things) of the strategic outcomes of supply chain 4.0. The required data was collected via a questionnaire from a sample consisting of 211 employees from construction companies in Jordan. The results point out that big data and the Internet of things have significant impacts on the strategic outcomes of supply chain 4.0. Hence, it was concluded that supply chain technologies are pivotal drivers of supply chain strategic outcomes. Based on these results companies are called to adopt and integrate Industry 4.0 technologies into their supply chains to improve supply chain long-run performance. Scholars were also encouraged to investigate the impact of supply chain 4.0 technologies on other constructs such as supply chain resilience.

In Malaysia, manufacturing industry is a major contributor to the economic advancement. As a result, cutting-edge technology like the internet of things (IoT) is projected to have a significant impact on business operations and supply chain management (SCM). However, research into the influence of IoT deployment on supply chains and organizational performance is relatively sparse. Therefore, this study is to determine the relationship between benefits and challenges of IoT adoption and organizational performance. Furthermore, this study looks into the mediating role of supply chain performance in the relationship between IoT adoption benefits and challenges and organizational performance. The population of this study is comprised of 3019 manufacturing companies in Malaysia, while the minimum sample size needed is 43 manufacturing companies.1160 complete set of survey questionnaire were distributed through email and 63 responses received representing five per cent of response rate. Partial Least Square Structural Equation Modelling (PLS-SEM) is used to assess all of the study's hypotheses. The results of this paper support six out of the seven hypotheses tested. In conclusion, the manufacturing industry in Malaysia needs to be exposed more to the benefits of IoT rather than keep discussing its challenges. This study can be a guideline to the manufacturing companies in decision making for IoT adoption. The limitations and recommendation for future study is highlighted.

In the era of IoT gaining traction, attacks on IoT-enabled devices are the order of the day that emanates the need for more protected IoT networks. IoT's key feature deals with massive amounts of data sensed by numerous heterogeneous IoT devices. Numerous machine learning techniques are used to collect data from different types of sensors on the objects and transform them into information relevant to the application. Furthermore, business and data analytics algorithms help in event prediction based on observed behavior and information. Routing information securely over the internet with limited resources in IoT applications is a key problem. The study proposes a model for detecting network anomalies in IoT devices to enhance the security of the devices. The study employed the IoT Botnet dataset, and K-fold cross-validation tests were used for validating the values of evaluation metrics. The average values of Accuracy, Precision, Recall, and F Score was 97.4.

The Internet of Things (IoT) is an innovative technology that has the potential to help public hospitals better meet the demands of hospitalization. However, only a small portion of the research looked at patients' behavioural intentions (BI) to utilise IoT healthcare devices (IoTHD). This study intends to investigate the variables that influence the BI's use of IoTHD. The research suggests that the BI may be explained by factors of UTATU. The patients of public hospitals make up the population. A questionnaire was used to obtain the data using convenience sampling. Participants in this research totalled 161. Smart Partial Least Square results demonstrated that social influence (SI) has an impact on performance expectancy (PE). Technological complexity (TC) and playfulness (PP) had an impact on effort expectancy (EE). Additionally, the BI to adopt IoTHD was impacted by PE, EE, perceived security (PS), and perceived privacy (PV). The impact of PE and EE on BI to use IoTHD was not moderated by the facilitating conditions (FC). In order to improve patients' perceptions of IoTHD usage in public health organisations, simple process and more positive word of mouth is required.

The primary goal of this research is to examine the influence of the Internet of Things on Pharmaceutical Inventory Management in Jordan. The employees of the pharmaceutical companies in Jordan represent the population of the study. Accordingly, the study instrument was distributed to an appropriate sample of 620 employees of pharmaceutical companies in Jordan; the questionnaires used in the analysis were 432. Structural equation modeling (SEM) was used to test the study hypotheses. The study results indicated that all dimensions of the Internet of Things had an influence on inventory management. The greatest influence was for cloud computing. Based to the study findings, pharmaceutical companies in Jordan should provide high-quality Internet coverage in their offices and stores, as well as to invest in Internet of things technologies that allow the company to remotely monitor and control the physical components of the stores, attract employees with knowledge of modern Internet technologies, and provide the necessary training and development for their employees.

The purpose of this paper is to investigate the adoption of an Activity-Based Costing (ABC) system to provide relevant cost information for enterprises in the business of building and selling Internet of Things (IoT) based products. The research is constructed over the relevant themes identified in previous research about ABC. An important issue that emerges from the analysis is that cost is a function of the volume of production and this notion of production does not represent the source of value generation discussed in this work. A functioning of a smart and connected product requires a cloud-based system for operating and monitoring it throughout its life. The implication of collecting and processing data is that costs essentially changes depending on the volume of data stored and the complexity of intended analytics that would require more qualified analysts. The estimation of costs related to data analytics and products’ monitoring serve as a reference to forecast future expending during the lifetime of products. This research is the first to provide a theoretical discussion on ABC assumptions in the context of IoT business models.