Metaheuristic game theory for fuzzy weight consolidation in Internet of Things (IoT) adoption decisions for construction safety

The Internet of Things (IoT) and Cloud Computing both are developing technologies. Cloud Computing blows up to provide support to IoT by working as a sort of front-end and it is based on the concept of permitting users to do computing tasks using services delivered with internet. The cloud computing empower an appropriate, on-demand, and scalable network access to a shared pool of configurable computing resources. The cloud-based IoT architecture includes features of cloud-based IoT platform and its interaction with three main cloud computing models: IaaS (infrastructure as a service), Paas (platform as a service), and SaaS (software as a service). The cloud and IoT integration empowers new scenarios, for smart services and applications, as Sensing as a Service (SaaS), DataBase as a Service (DBaaS), Video Surveillance as a Service (VSaaS), and many more. Various live company products, research projects, and projects with freely available source code in various areas of Cloud Computing and IoT integration are Nimbits, ThingSpeak, Paraimpu, Device Cloud, Sensor Cloud. REpresentational State Transfer (REST) architectural style web services and Constrained Application Protocol (COAP), Message Queue Telemetry Transport (MQTT), web transfer protocols are used for communication for the IoT resource-constrained things. Networking protocols like IPv6 over Low power Wireless Personal Area Network (6LoWPAN) and IPv6 over Bluetooth Low Energy are used for constrained networks in IoT and cloud integration. The data link layer protocols for IoT devices like IEEE 802.15.4, IEEE 802.11ah, Z-Wave, WirelessHART, Bluetooth, Zigbee are used for short range communication for IoT things. The applications of integrated cloud and IoT include agriculture, video surveillance, healthcare, smart city, smart home and smart metering, etc. IoT and cloud integration involves several challenges and issues as standardization of machine to machine (M2M) communication and interoperability, power and energy efficiency of devices for data transmission and processing, big data generated by several devices, security and privacy, integration methodology, pricing and billing, network communications, storage, etc. In this chapter, the introduction of cloud and IoT, their integration architecture, integration applications, and challenges and issues involved are discussed.

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

This study quantitatively examines the relationship between Internet of Things (IoT) integration and reductions in urban infrastructure vulnerabilities across global smart cities. Drawing on a multi-sector, multi-city panel dataset (2018–2024), the research evaluates how technological and governance dimensions of IoT maturity influence service reliability, outage duration, response lag, and failure rate reduction. IoT integration is conceptualized through five measurable dimensions—sensor coverage, data latency, interoperability, automation level, and data governance maturity—while vulnerability indicators capture operational stability and recovery capacity across energy, transportation, water, and emergency systems. Employing multilevel regression, difference-in-differences estimation, and structural equation modeling, the study finds that higher IoT integration significantly enhances infrastructure resilience, with interoperability and data governance maturity emerging as the strongest predictors of performance improvement. Automation and sensor coverage demonstrate complementary effects by reducing detection lag and restoration time, whereas high data latency negatively impacts operational efficiency. Mediation and moderation analyses reveal that response efficiency mediates the link between automation and reliability, while policy capacity and urban density moderate the effects of IoT maturity on vulnerability reduction. Developed cities display greater IoT integration and lower vulnerability levels, though developing cities achieve larger marginal gains per unit of technological advancement. Sectoral analysis confirms that energy and transportation infrastructures benefit most from IoT integration, while water and emergency sectors exhibit lower yet positive effects. The findings substantiate that IoT integration—supported by effective governance, data standardization, and automation—constitutes a statistically verifiable mechanism for enhancing urban resilience. This study provides empirical evidence for policymakers and urban engineers to design scalable, data-driven strategies for strengthening infrastructure reliability and adaptive capacity in smart cities worldwide.

The integration of the Internet of Things (IoT) with Machine Learning (ML) is a transformative advancement that is revolutionizing the way data-driven decision-making occurs across various industries. IoT systems comprise interconnected devices that collect and transmit vast amounts of real-time data from sensors, machines, and appliances. However, merely collecting data is not sufficient; the real value lies in the analysis and interpretation of this data to generate actionable insights. This is where ML comes into play. ML techniques allow systems to learn from the data generated by IoT devices, enabling predictive analysis, automation, and enhanced decision-making processes. This integration of IoT and ML is paving the way for smarter, more efficient systems that can be applied in a wide array of fields such as healthcare, manufacturing, transportation, home automation, and smart cities. For instance, in healthcare, wearable IoT devices track vital health statistics like heart rate and blood pressure, while ML algorithms process these data in real-time to detect anomalies, predict potential health risks, and provide healthcare professionals with alerts for timely interventions. Similarly, in manufacturing, IoT devices collect sensor data from machines, which is analyzed by ML algorithms to predict maintenance needs, preventing costly breakdowns and improving operational efficiency. The sheer scale and complexity of data produced by IoT devices pose significant challenges for traditional data processing methods. ML algorithms are essential for managing and extracting value from this data, as they can handle large datasets, identify patterns, and make predictions in a scalable manner. By utilizing ML models such as deep learning, reinforcement learning, and clustering techniques, IoT systems are capable of adapting to changing environments, learning from their surroundings, and making intelligent decisions without human intervention. This paper will review the various ways ML can be leveraged within IoT systems to provide scalable, intelligent decision-making processes for analyzing the vast amounts of data produced by IoT devices. It will examine key use cases across different sectors where the integration of ML and IoT has shown significant promise. Specific case studies will be highlighted, including healthcare, where ML models enhance the monitoring and prediction of patient health; industrial IoT (IIoT), where predictive maintenance and anomaly detection improve operational efficiency; and smart cities, where ML-optimized IoT systems are used to manage traffic flow, energy consumption, and public services. By exploring these case studies, this paper aims to demonstrate the immense potential of integrating IoT with ML. It will also examine the challenges that arise in implementing such systems, including issues of scalability, data privacy, and security, and discuss potential solutions to these challenges. The paper will conclude with insights into the future of IoT-ML integration and how these technologies can continue to evolve to create even more intelligent, autonomous, and efficient systems across a broad range of industries.

Since its inception, Blockchain has proven itself as an emerging technology that revolutionizes diverse industries. Among others, Internet of Things (IoT) is one of the application domains that reaps large benefits from Blockchain. The Blockchain’s potential to overcome different challenges of IoT services has shifted the research interests of many scientists towards addressing the integration of two disruptive technologies, i.e., IoT and Blockchain. This resulted in publishing more research papers in this emerging field. Thus, there is a need to conduct research studies through which a broad overview of research contributions in this field could be investigated. To respond to this need, a number of review papers have been published recently, each of which has considered the integration of IoT and Blockchain from a different perspective. Nonetheless, none of them has reported a bibliometric analysis of the state-of-the-art in the integration of IoT and Blockchain. This gap stimulated us to investigate a thorough analysis of the current body of knowledge in this field, through a bibliometric study. In this paper, we conducted a bibliometric analysis on the Scopus database to assess all scientific papers that addressed the integration of IoT and Blockchain. We have analyzed those collected papers against four criteria including annual publication and citation patterns, most-cited papers, most frequently used keywords, and most popular publication venues. The results disseminate invaluable insights to the researchers before establishing a research project on IoT and Blockchain integration.

The integration of the Internet of Things (IoT) in supply chain management and logistics has emerged as a promising approach to enhance operational efficiency, transparency, and agility. A comprehensive data analysis and interpretation of the integration of Internet of Things (IoT) technology in supply chain management and logistics. The study aims to assess the current level of IoT adoption, identify challenges faced during implementation, examine the relationship between IoT adoption and supply chain efficiency, explore potential benefits, and evaluate the impact of IoT integration on organizational performance and customer satisfaction. The data analysis is based on survey responses from diverse organizations operating in various industries. The survey investigated the extent of IoT adoption in different supply chain activities, such as inventory management, transportation, warehousing, and order fulfilment. The findings reveal that 75% of the surveyed organizations have partially adopted IoT in their supply chain operations, with varying degrees of implementation across different activities. Furthermore, the research identifies key challenges faced by organizations in implementing IoT, with the cost of implementation, integration with existing systems, and data security and privacy concerns being the most prominent hurdles. The study also explores the relationship between IoT adoption and supply chain efficiency. The data analysis demonstrates a positive correlation between IoT adoption and supply chain performance, with improvements observed in on-time delivery, order accuracy, and inventory turnover among the organizations that adopted IoT. The potential benefits of IoT adoption in supply chain management are investigated. The data analysis highlights the expected advantages of real-time tracking and visibility, predictive maintenance, and enhanced demand forecasting. Finally, the impact of IoT integration on overall organizational performance and customer satisfaction is evaluated. The data analysis indicates that a significant majority of organizations reported an overall improvement in performance after IoT integration, and a positive impact on customer experience and loyalty. In conclusion, this research provides valuable insights into the adoption and implications of IoT in supply chain management and logistics. The findings underscore the potential for IoT to drive efficiency and enhance customer satisfaction in the supply chain context. However, challenges related to cost, integration, and data security need to be addressed for successful implementation. The research contributes to a deeper understanding of leveraging IoT for smarter supply chain management and logistics, paving the way for further advancements and future perspectives in this rapidly evolving domain

Influence of IoT implementation on Resource management in construction

The changing business landscape over the past two decades has been marked by the accelerated adoption of digital technology, increasing environmental awareness, and the demand for sustainable business models. In this context, companies are required to not only compete effectively but also transform towards a Green Industry by leveraging renewable energy and Internet of Things (IoT) technology. Traditional management strategies such as SWOT, TOWS, and QSPM remain relevant, but their effectiveness increases significantly when combined with modern approaches such as Blue Ocean Strategy and supported by the use of real-time data from the IoT. The study showed that IoT implementation can improve operational efficiency, reduce machine downtime, strengthen supply chain management, and optimize renewable energy utilization, resulting in reduced carbon emissions and energy cost savings of up to 20%. Quantitative findings demonstrate a significant contribution of the integration of management strategies, IoT, and renewable energy to company performance with an R² value of 0.67, while qualitative interviews confirmed IoT's role as a catalyst for digital-green transformation. However, challenges such as initial investment costs, data security risks, and limited technical skills remain inhibiting factors. This study concludes that the integration of IoT with renewable energy and management strategies is a strategic foundation for increasing competitiveness while strengthening the legitimacy of corporate sustainability in the green industry era.

The research here investigates how Internet of Things (IoT) technologies can be integrated into responsive architecture in order to create sustainable and energy-efficient smart buildings. The main issue being solved is that there is no clearly defined framework for integrating IoT in responsive architectural design that makes it challenging to attain real-time adaptability, reduce energy consumption, and enhance the user experience. The research aims to evaluate existing IoT implementations in architecture by analyzing real-world case studies and developing a conceptual framework that articulates IoT's impact on building sustainability and efficiency. In an attempt to realize this goal, the research explores the following hypotheses: IoT integration greatly lowers a building's energy use. IoT-enabled buildings are more flexible and more satisfactory to users than conventional architectural buildings. An IoT-responsive architecture framework that is well designed can be employed as a scalable model for future smart city infrastructure. Through IoT-enabled building case studies, it has been found that IoT deployment results in a 30% reduction in energy consumption and substantial improvement in occupant comfort and operational efficiency. Keeping these findings in perspective, the research proposes a holistic framework with specific guidance to architects and urban planners to design more adaptive, efficient, and user-friendly buildings.

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

PurposeBuilt asset management processes require a long transition period to collect, edit and update asset conditions information from existing data sets. This paper aims to explore and explain whether and how digital technologies, including asset information model (AIM), Internet of Things (IoT) and blockchain, can enhance asset conditions assessment and lead to better asset management.Design/methodology/approachMixed methods are applied to achieve the research objective with a focus in universities. The questionnaire aims to test whether the integration of AIM, IoT and blockchain can enhance asset condition assessment (ACA). Descriptive statistical analysis was applied to the quantitative data. The mean, median, mode, standard deviation, variance, skewness and range of the data group were calculated. Semi-structured interviews were designed to answer how the integration of AIM, IoT and blockchain can enhance the ACA. Quantitative data was analysed to define and explain the essential factors for each sub-hypothesis. Meanwhile, to strengthen the evaluation of the research hypothesis, the researcher also obtained secondary data from the literature review.FindingsThe research shows that the integration of AIM, IoT and blockchain strongly influences asset conditions assessment. The integration of AIM, IoT and blockchain can improve the asset monitoring and diagnostics through its life cycle and in different aspects, including financial, physical, functional and sustainability. Moreover, the integration of AIM, IoT and blockchain can enhance cross-functional collaboration to avoid misunderstandings, various barriers and enhance trust, communication and collaboration between the team members. Finally, costs and risk could be reduced, and performance could be increased during the ACA.Practical implicationsThe contribution of this study indicated that the integration of AIM, IoT and blockchain application in asset assessment could increase the efficiency, accuracy, stability and flexibility of asset assessment to ensure the reliability of assets and lead to a high-efficiency working environment. More importantly, a key performance indicator for ACA based on the asset information, technology and people experience could be developed gradually.Originality/valueThis study can break the gap between transdisciplinary knowledge to improve the integration of people, technology (AIM, IoT and blockchain) and process value-based ACA in built asset management within universities.

Purpose- In recent years, there has been a notable surge in the utilization of emerging technologies, notably the Internet of Things (IoT), within the realm of business operations. However, empirical evidence has underscored a disconcerting trend whereby a substantial majority, surpassing 70%, of IoT adoption initiatives falter when confronted with the rigors of real-world implementation. Given the profound implications of IoT in augmenting product quality, this study endeavors to scrutinize the extant body of knowledge concerning IoT integration within the domain of agricultural logistics operations. Furthermore, it aims to discern the pivotal determinants that exert influence over the successful assimilation of IoT within business operations, with particular emphasis on logistics. Design/Methodology/Approach- The research utilizes a thorough systematic review methodology coupled with a meta-synthesis approach. In order to identify and clarify the key factors that influence IoT implementation in logistics operations, the study is grounded in the Resource-Based View theory. It employs rigorous grounded theory coding procedures, supported by the analytical capabilities of MAXQDA software. Findings- The culmination of the meta-synthesis endeavor culminates in the conceptual representation of IoT adoption within the agricultural logistics domain. This representation is underpinned by the identification of three overarching macro categories/constructs, namely: (1) IoT Technology Adoption, encompassing facets such as IoT implementation requisites, ancillary technologies essential for IoT integration, impediments encountered in IoT implementation, and the multifaceted factors that influence IoT adoption; (2) IoT-Driven Logistics Management, encompassing IoT-based warehousing practices, governance-related considerations, and the environmental parameters entailed in IoT-enabled logistics; and (3) the Prospective Gains Encompassing IoT Deployment, incorporating the financial, economic, operational, and sociocultural ramifications ensuing from IoT integration. The findings underscore the imperative of comprehensively addressing these factors for the successful assimilation of IoT within agricultural logistics processes. Originality- The originality of this research study lies in its pioneering effort to proffer a conceptual framework that furnishes a comprehensive panorama of the determinants that underpin IoT adoption, thereby ensuring its efficacious implementation within the ambit of agricultural logistics operations. Practical Implications- The developed framework, by bestowing upon stakeholders an incisive comprehension of the multifaceted factors that steer IoT adoption, holds the potential to streamline the IoT integration process. Moreover, it affords an avenue for harnessing the full spectrum of IoT-derived benefits within the intricate milieu of agricultural logistics operations.

IoT Contributions to The Safety of Construction Sites: A Comprehensive Review of Recent Advances, Limitations, And Suggestions for Future Directions

Internet of Things (IoT) technology is something that is equipped with sensors and software so that it can send data over a network without human interaction. As a result, the Internet of Things can improve connectivity by connecting many devices via the internet, facilitating human interaction with machines. This research was conducted with the aim of increasing efficiency, reliability and innovation in the teaching and learning process. By using IoT, this research focuses on developing interactive and personalized learning media to increase students' understanding of technology. In addition, this research aims to improve teachers' abilities in using information technology to help students learn to use it. In conducting this research, researchers used quantitative methods in carrying out the research. The data obtained by the researcher was obtained through distributing questionnaires presented by the researcher via a goggle from application. The distribution of this questionnaire is carried out by researchers online, and then the results of the distribution of this questionnaire will be processed using an SPSS application. From this research, researchers can conclude that the integration of Internet of Things (IoT) technology can increase classroom management efficiency. With the help of the Internet of Things (IoT), teachers can monitor and manage classes more efficiently and intelligently by collecting data such as student activity, room temperature, and other information. Thus, IoT allows teachers to make smarter and strategic decisions about classroom management. Based on the results of this research, the impact of integrating IoT technology can provide benefits for teachers and students. Developing digital skills is one of the benefits of integrating IoT technology. By learning how to use Internet of Things technology in learning, teachers can improve their digital skills and increase their ability to use technology to improve the efficiency and quality of education.

With fastest development in communication technologies, Internet of Things (IoT) plays a key role with full maturity and its infancy. Rapidly, it has developed (growth) for large data transmission over the wireless communication. Hence, it is needed to manage system and full fill the market requirement for practical application. Many existing IoT has greatly centralized architectures that have many technical limitations. Examples of these limitations are cyber attacks. Hence, it is needed to find out new techniques for enhancement of data accessing with maintaining security as well as privacy. The solution for this problem is to make the combination of the IoT with block chain which gives a guarantee to sense data integrity. Integration of IoT and block chain resulted in immutable log, comprehensive and easy access. Here, this paper carried out the study of integration of IoT and block chain in relation with different issues, opportunities and application area.