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.

The study looks into the intersection of Artificial Intelligence (AI) with the Internet of Things (IoT), especially the legal, regulatory, and cybersecurity integration challenges within the context of UAE and Jordan's financial sectors. The objective of the study was to assess the relative impact of the cybersecurity challenges, legal infrastructures, and e-governance maturity on the cyber threats and trust of clientele. The study utilized a quantitative research design, gathering data through a survey distributed to employees and managers within a number of financial institutions. With a data sample of 400 employees, the survey data were analyzed through a variety of methods, such as descriptive statistics, reliability, Pearson correlations, and Structural Equation Modelling (SEM). The study established that the risks posed by inadequate cybersecurity infrastructures substantially increase the threats. Also, the risks posed by inadequate legal regulations and low e-governance maturity do not appear to increase the challenges. Legal adequacy positively impacts trust. Exposure to cyber threats with unmitigated risks and poor legal regulations and low e-governance maturity do not appear to increase the challenges. The study relies on the trust of cyber clientele to validate and uphold the proposed theoretical framework suggesting the need for an integrated approach consisting of high-quality legal regulations, comprehensive governance, and secure advanced cybersecurity to ensure the safe merging of AI and IoT. In addition, the study sheds light on the perspectives of policymakers, regulators, and financial institutions aiming to build safe and reliable digital financial systems in the UAE and Jordan.

Big data has become more important in practically all businesses throughout the world in the present era of information technology. Big data as a part of the internet of things, creative accounting practices regarding the meaning, methods and motives and the role of big data as a part of the internet of things on the increase of creative accounting practices. The researchers concluded that big data leads to an increase in the percentage of creative accounting practices in the business environment, due to the fact that big data impacts the auditing process and the detection of creative accounting practices such as income smoothing. Despite the fact that Big Data is most commonly used in creative accounting techniques and its relevance cannot be overstated, research and analyses are insufficient. Given the relevance of big data across all industries, this study attempts to undertake a comprehensive literature analysis on the topic of big data and innovative accounting methods across all industries. As a result, the study will add to the body of knowledge by opening up new avenues for empirical research in big data and creative accounting.

The rapid digitalization in recent years has opened up many technological possibilities, gradually transforming various sectors and society as a whole. This digital shift has enabled advancements in a number of fields, leading to improved resource efficiency, systems and processes. The Internet of Things (IoT) refers to a system of interconnected devices that share information that exchange information with one another via the internet. IoT devices are now everywhere, found in applications ranging from unmanned aerial vehicles to smart home environments, from the Industrial Internet of Things to the Internet of Medical Things. The core concept of IoT revolves around establishing a seamless and intelligent communication ecosystem, facilitating interactions between devices over the internet. This is anticipated to create new opportunities for enhancing services in various societal sectors, such as transportation, farming and smart cities. However, IoT-based networks face limitations and challenges that hinder the realization of their full potential. This paper outlines these challenges and proposes solutions, emphasizing the importance of collaboration and innovation. The paper also anticipates future trends in IoT, particularly the integration of 5G connectivity, cloud computing and AI, and identifies areas for future research to address current challenges and explore new applications.

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.