Bluetooth IoT infrastructure network with immersive experience

The rapid proliferation of the Internet of Things (IoT) has ushered in a new era of technological advancement, with profound implications for industries and businesses across the globe. IoT, characterized by the interconnection of everyday objects and devices through the internet, has revolutionized the way data is collected, analyzed, and utilized, thereby offering unprecedented opportunities for enhanced operational efficiency and strategic decision-making. In the realm of industry, IoT has become a linchpin for the concept of Industry or fourth industrial revolution. The seamless sensors integration, actuators, and smart devices into industrial processes has enabled the creation of "smart factories”. IoT-driven insights have empowered manufacturers to adopt a more customer-centric approach by tailoring products andservices to individual preferences, fostering customer loyalty and opening up new revenue streams. In the business sector, IoT has transcended beyond mere operational enhancements and has catalyzed fundamental business model transformations. Retailers, for instance, harness IoT- generated data to personalize customer experiences, optimize inventory management, and refine marketing strategies. The integration of IoT with artificial intelligence has led to the emergence of predictive analytics, enabling businesses to forecast demand patterns, preempt disruptions, and make proactive strategic choices. In conclusion, the Internet of Things is reshaping industries and businesses by infusing them with data-driven intelligence and connectivity. As IoT continues to evolve, stakeholders must collaborate to overcome challenges and ensure its ethical, secure, and inclusive deployment.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

The Internet of Things (IoT) environment is constantly evolving. Many IoT services have emerged, improving living conditions. Smart homes were among the first developments, and smart buildings, smart factories, and smart cities are attracting increasing attention. Smart cities represent the ultimate convergence of the IoT, the Cloud, big data, and mobile technology. Smart homes, buildings, and factories create smart cities. In addition, the IoT finds applications in traffic control, public safety, and medical services, permitting group-based communication. As the scale of service grows, the number of things (devices) constituting the service also increases. However, security vulnerabilities arise in group-based communication environments. A device may require authentication when entering a gateway; to secure environments with large numbers of devices (such as those featuring IoT smart metering), the gateways bear heavy loads. Therefore, efficient authentication of group leaders and devices is essential. Here, we develop a dynamic group authentication and key exchange scheme for group-based IoT smart metering environments which enables efficient communication among secure IoT services. Our group authentication scheme increases the computational efficiency of the group leader and the participating devices, based on a threshold secret sharing technique.

Interacting with the Web of Things

The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model

The Industry 4.0 (I4.0) adoption comprises the change of traditional factories into smart using the ICTs. The goal is to monitor processes, objects, machinery, and workers in order to have real‐time knowledge about what is going on in the factory and for achieving an efficient data collection, management, and decision‐making that help improve the businesses in terms of product quality, productivity, and efficiency. Internet of Things (IoT) will have an important role in the I4.0 adoption because future smart factories are expected to rely on IoT infrastructures composed of constellations of hundreds or thousands of sensor devices spread all over the industrial facilities. However, some problems could arise in the massive IoT deployment in a medium‐high factory: thousands of IoT devices to cope from different technologies and vendors could mean dozens of vendor tools and user interfaces to manage them. Moreover, the heterogeneity of IoT devices could entail different communication protocols, languages, and data formats, which can result in lack of interoperability. On the other hand, conventional IT networks and industrial machinery are expected to be managed together with the IoT infrastructure, maybe using a tool or a set of tools, for orchestrating the whole smart factory. This work meets these challenges presenting an open‐source software architecture solution based on OpenDaylight (ODL), a Software Defined Network (SDN) controller, for orchestrating an industrial IoT scenario. This work is addressed by shedding light on critical aspects from the SDN controller architectural choices, to specific IoT interfaces and the difficulties for covering the wide range of communication protocols, popular in industrial contexts. Such a global view of the process gives light to practical difficulties appearing in introducing SDN in industrial contexts, providing an open‐source architecture solution that guarantees devices and networks interoperability and scalability, breaking the vendor lock‐in barriers and providing a vendor‐agnostic solution for orchestrating all actor of an I4.0 smart factory.

In recent years, the Internet of Things (IoT) has constituted a driving force of modern technological advancement, and it has become increasingly common as its impacts are seen in a variety of application domains, including healthcare. IoT is characterized by the interconnectivity of smart sensors, objects, devices, data, and applications. With the unprecedented use of IoT in industrial, commercial and domestic, it becomes very imperative to harness the benefits and functionalities associated with the IoT technology in (re)assessing the provision and positioning of healthcare to ensure efficient and improved healthcare delivery. In this research, we are focusing on two important services in healthcare systems, which are cancer care services and business analytics/cloud services. These services incorporate the implementation of an IoT that provides solution and framework for analyzing health data gathered from IoT through various sensor networks and other smart devices in order to improve healthcare delivery and to help health care providers in their decision-making process for enhanced and efficient cancer treatment. In addition, we discuss the wireless sensor network (WSN), WSN routing and data transmission in the healthcare environment. Finally, some operational challenges and security issues with IoT-based healthcare system are discussed.

The Internet of Things (IoT) is a constantly growing environment for connecting hardware, software, physical things, and communicating devices altogether or individually over a network with or without human interventions. It collects, computes, and exchanges data together. This quality of IoT makes the platform flexible for the variety of applications that work on this technology to get incredible benefits, including healthcare domains. In the olden days, the medical system does not have any digital file system, clinical data monitoring, and cyber-physical systems. Cyber Physical System (CPS) is the nucleus of IoT. The smart parameters of IoT create an interesting thing on the Internet of Healthcare System (IoHT) and keep the best place in revenue. Apart from smart homes, cities, and smart devices, IoT established a vital position in IoT. The security and the privacy of Healthcare data should be managed properly. Thus, making a new concept into it called, “Blockchain.” This not only marks its step in cryptocurrencies, but it also tights the security and privacy of all types of data, and it supports heterogeneous applications, especially IoT-based applications. This article describes the Blockchain-IoT-based healthcare applications, security of the Internet of Medical Things (IoMT), and the future direction in the healthcare system based on Blockchain.

The long-term impact on global production of the shift from traditional to intelligent manufacturing is intriguing. The concept of “Industry 4.0” has been put up as a means of expanding production to achieve cost-effectively shorter product life cycles and extreme mass customization. The goal of a smart factory is to improve production efficiency, quality, control, visibility, and management by combining physical and cyber technology. For Industry 4.0 to work, manufacturers will need a smart factory equipped with cutting-edge technologies such as the Internet of Things (IoT), cloud computing, and machine intelligence (ML). As a result, we outline and illustrate how ML can be used in a cloud-assisted factory to increase its performance (CaSF). An entirely new four-tier CaSF architecture with vertical connections has been demonstrated in this study for the first time. In addition, the CaSF’s core ML technologies are categorized and defined by the architecture hierarchy’s logical relationships. It is now time to discuss some of the most pressing concerns and potential solutions related to ML technologies in CaSF systems. Industry 4.0 rollout has been aided to some degree by the incorporation of machine learning (ML) in smart factories.

Internet of Things (IoT) is a collection of networks, objects, devices that are connected using the Internet. It interacts with the environment both internally and externally. IoT senses the environment and acts accordingly. It gives the environment with advanced techniques and thus improves the quality of mankind. IoT allows the devices to communicate with each other either physically or virtually. With the help of IoT, the environment becomes smarter and can be connected with any device at any time. Nowadays people want to communicate with all the non-living things via the Internet. IoT is used to collect and analyze the data from various actuators and sensors then it is passed it over smartphones or computers through a wireless connection. There evolve the applications that use IoT and in each of the applications IoT plays a vital role in enhancing the living standards which includes home appliances, Smart energy, environment, and industries, etc. and makes a friendly environment. To make it more professional, IoT is used in the supply chain, transportation, logistics, automation, and remote monitoring. IoT delivers a change in the quality of individual life and it is widely distributed among the network of devices and provides a new ecosystem for the development of applications by understanding the development of the market in the early stages. IoT has the capabilities of delivering the services and demands of the people in this fast-moving world. This paper presents the survey of IoT smart systems applications and usages of the IoT environment.

In the manufacturing industry, technologies of The Fourth Industrial Revolution have been integrated into Smart Factory and is evolving into an intelligent factory. In other words, smart factories create an optimal economic production environment by exchanging information through the Internet of Things and by providing real-time monitoring, analysis of all processes. The recent decline in birthrate, aging population and rising labor costs require automated production plants. At the same time, the more diversi- fied demands of consumers become, the shorter the life cycle of products becomes and the capability of customized mass production has emerged as the core of competitiveness. Therefore, smart factories are an essential product of innovation to adapt to these socioeconomic changes. In addition, smart factories provide breakthrough results such as high quality, reduced defect rate and improved productivity. In this study, we will first explore the cases of the operations of German automated factories, the place of origin of the Fourth Industrial Revolution and the world’s strongest manufacturer, to derive elements that can be benchmarked to Korean small and medium-sized enterprises (SME’s). Based on this, the ultimate purpose of this study is to suggest implications for the governmental projects supporting compa- nies that want to build, maintain and spread smart factories in Korea. In this study, prior literary studies on domestic and foreign papers and research reports and empirical case studies through corporate visits were used as research methodologies. While the existing research is an indirect study through domestic and international literature or information on the Internet, this study is differentiated in that it is an empirical case study of visiting demonstration. The implication is that in terms of corporate strategy, smart factories should first commence with manufacturing processes that require automation and then spread them sequentially in line with the corporate management environment (financial, market competition structure, and growth potential). Furthermore, various information and new technologies in the smart manufacturing process should be actually integrated to create optimal synergy, and various internal and external management resources such as human resources and environmental energy should be planned and implemented in an integrated manner. In terms of policy support, the focus should be on customized modular support that companies need. At the same time, it is also necessary to standardize the process of smart manufacturing through joint cooperative support for smart factories among the same companies and induce mutual utilization of generated data, minimization of use costs and maximum synergy efficiency. In particular, it is necessary to seek ways to utilize idle smart factories when ordering small or excessive amounts of orders. At the same time, expansion of smart factory supporting platforms and managemental aid are also required through a consortium between professional companies that supply new technologies to smart factories (e.g. Robots, Sense, AI, Total solution SW, VR etc.). Environment-friendly smart manu- facturing and optimal utilization of energy and materials should also be considered comprehensively. To this end, it is necessary to have a smart management organization, or a research advisory group, that studies human and environment-friendly smart manufacturing within the “Smart Manufacturing Innovation Promotion Group” to induce social innovation. It will be necessary to minimize the waste of policy support by thoroughly managing and supervising the implementation of the beneficiary entities. Finally, support for the introduction and application of smart factories for SME’s and Midsize Business should be strengthened to promote the smart manufacturing of SME’s to increase their competitiveness.

Thanks to the advent of the Internet of Things (IoT) concept, several areas have been extended to offer a new category of "smart" services, such as smart home, smart cities, smart grid or smart factory. The IoT is based on communicating devices (sensors, actuators, RFID tags) that are remotely manageable by business applications. In this new context, several traditional challenges have to be reconsidered, among them the quality of service (QoS) issue to meet the application requirements. As a part of our approach for an autonomic QoS management at the Middleware level within the IoT, we first propose an analytical model based on queueing theory of a Middleware platform, aimed at guiding the monitoring and planning steps of the proposed autonomic management. This model is then instantiated for an IoT entity (here a Gateway). We finally show the benefits induced by the use of this model to guide the activation of two adaptation actions aimed at satisfying QoS constraints of critical IoT application still optimizing energy consumption of the involved IoT entity.

SummaryRecently, the deployment of novel smart network concepts, such as the Internet of things (IoT) or machine‐to‐machine communication, has gained more attention owing to its role in providing communication among various smart devices. The IoT involves a set of IoT devices (IoTDs) such as actuators and sensors that communicate with IoT applications via IoT gateways without human intervention. The IoTDs have different traffic types with various delay requirements, and we can classify them into two main groups: critical and massive IoTDs. The fundamental promising technology in the IoT is the advanced long‐term evolution (LTE‐A). In the future, the number of IoTDs attempting to access an LTE‐A network in a short period will increase rapidly and, thus, significantly reduce the performance of the LTE‐A network and affect the QoS required by variant IoT traffic. Therefore, efficient resource allocation is required.In this paper, we propose a priority‐based allocation scheme for multiclass service in IoT to efficiently share resources between critical and massive IoTD traffic based on their specific characteristics while protecting the critical IoTDs, which have a higher priority over the massive IoTDs. The performance of the proposed scheme is analyzed using the Geo/G/1 queuing system focusing on QoS guarantees and resource utilization of both critical and massive IoTDs. The distribution of service time of the proposed system is determined and, thus, the average waiting and service times are derived. The results indicate that the performance of the massive IoTDs depends on the data traffic characteristics of the critical IoTDs. Furthermore, the results emphasize the importance of the system delay analysis and demonstrate its effects on IoT configurations.

Today "Smart Factories" has paved way for the new industrial revolution called "Industry 4.0 or the Internet of Things (IIOT)". By the implementation of this standard, industrial devices communicate with each other as well as operators, automate processes with little or no human effort, and facilitates real-time manufacturing operation. This intelligent, networked system uses a continuous exchange of data for connected operations and production. To establish a smart manufacturing platform that can enhance the accomplishment of smart factories is vital and desirable for modern manufacturing industries. At the moment, most conventional factories are faced with the challenge of migrating into smart factories, due to their inability to converge information and automation system to perform a real-time operation. This paper studied various articles related to Smart Manufacturing and will discuss some of the challenges with this transformation and propose a 5 stage approach for the transformation of conventional factories to smart factories.

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.