Smart library environments: IoT and automation for next-generation services

The idea of the Internet of Things (IoT) is quickly developing and influencing new advancements in a variety of application domains, including the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), and the Internet of Robotic Things (IoRT), among others. The IoT influence presents new design and implementation challenges in various fields, including seamless platform integration, context-based cognitive network integration, new mobile sensor/actuator network paradigms, and architectural domains for smart farming, infrastructure, healthcare, agriculture, business, and commerce. Applications for automation in the IoRT are numerous and are developing quickly. IoRT blends the strength of robots and the IoT, resulting in creative solutions for a range of sectors. As we ensure the authenticity of the content in this introduction, we shall investigate the wide range of IoRT automation applications. IoRT automation refers to a broad range of endeavors that use connected gadgets, sensors, and autonomous machinery to improve production, efficiency, and safety across various industries. These regions are general categories into which these programs can be placed; Industry 4.0 and manufacturing, IoRT enables automated manufacturing where robots and IoT gadgets work together without issues. While sensors keep an eye on the condition of the equipment and improve manufacturing processes, robots can carry out activities like assembly, quality checking, and material handling. Medical field: IoRT automates procedures, including surgery, patient surveillance, and drug delivery, increasing accuracy and lowering human error. Robotic prostheses and exoskeletons improve rehabilitation and mobility. Agriculture: IoRT supports precision farming by using robots and drones that can operate autonomously to check crop health, administer pesticides, and harvest crops. Decisions are made more accessible by real-time sensor data on weather and soil conditions. Logistics and warehousing: By autonomously moving items and improving inventory management, Automated Guided Vehicles (AGVs) and robots optimize warehouse operations. IoRT helps to manage traffic, monitor air quality, and enhance public safety in smart cities by utilizing autonomous cars and advanced infrastructure. Retail: IoRT improves customer experiences in the retail industry with autonomous grocery carts, robotic inventory managers, and data-driven targeted marketing. Environmental Tracking: IoRT devices gather information in hazardous or remote locations, such as monitoring ocean pollution levels or harshly weathering infrastructure inspections. Power and utilities: IoRT uses robotic inspections and proactive maintenance to help maintain electric grids, pipelines, and projects involving renewable energy. Education and Exploration: IoRT is helpful for research and education since it enables scientists and students to experiment remotely with robotic systems and learn more about automation technology. Home automation: IoRT equipment is increasingly integrated into smart homes to provide security and convenience through linked appliances, security cameras, and personal assistants. As technology develops, the range of IoRT automated applications also keeps growing. It can transform industries, enhance the standard of life, and tackle challenging problems. This overview offers a glimpse into the intriguing and diverse world of IoRT automation.

With the popularization of the Internet of Things(IoT), as the IoT devices carry a large amount of sensitive information, more and more attention is paid to the privacy and security of the IoT. According to legal requirements, vendors are obliged to provide a privacy policy to inform users of their privacy practices and to ensure that actual behavior complies with the published privacy policy. However, we found that the reality is not the case. In this paper, we design an IoT privacy policy consistency detection framework that can automatically extract sensitive information in the request packets sent by IoT apps, and can detect whether the privacy policy declared by the vendor is consistent with the actual collection and sharing behavior. We create a sensitive word list and a sensitive field mapping dictionary in the IoT wearable scene. The sensitive word list contains almost all sensitive words representing sensitive information in the IoT wearable scene. The sensitive field mapping dictionary can realize the IoT wearable in the scenario, the special fields in the data packets sent by the APP to the cloud are mapped to the words in the sensitive word list. We tested 6 IoT platforms, including 9 devices and 14 APPs, and extracted a total of 245 items of sensitive information, of which 57 items of sensitive information were not claimed in their corresponding privacy policies. The test results showed that some representative IoT platforms (e.g. Huawei, Amazfit) have violated their user privacy policies to collect and share actual sensitive information, which prove that the inconsistence between platforms' privacy statement and actual behaviors is prevalent.

Data practices of internet of medical things: A look from privacy policy perspectives

In today’s business environments, data-driven decision-making has played a crucial role in bringing real-time sustainability to organizational behavior. Furthermore, the Internet of Things (IoT) has been widely adopted recently, but studies investigating its impact on organizational dynamics and sustainability are limited. This study explores the IoT’s potential to improve the real-time decision-making and sustainability of organizations. A cross-sectional study was conducted on 250 respondents belonging to a diverse range of industries. Five variables were analyzed in the study—IoT implementation, real-time data analytics, decision-making, organizational behavior, and organizational performance. These variables were scored on a 5-point Likert-type scale, with responses ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The data were collected with the help of a structured questionnaire. The data were analyzed using descriptive statistics, Pearson correlations, and structural equation modeling (SEM) to test the relationships among the study variables. The findings indicate that enhanced organizational behavior (r = 0.1101), decision-making (r = 0.269), and real-time data analytics (r = 0.1888) are all strongly associated with the IoT. Applying structural equation modeling further reveals a direct connection between IoT adoption and company performance. Moreover, it is observed that the coefficients for organizational behavior (β = 0.0707, p < 0.01) and real-time data analytics (β = 0.0851, p < 0.001) are statistically significant. This study demonstrates how the IoT can bring real-time sustainability to organizational behavior by improving decision-making and business operations. Although decision-making is enhanced by the IoT, organizations still need to optimize their resource usage and reduce waste to enhance sustainability. This study bridges a substantial gap in the existing literature by carefully investigating the relationship between IoT technology, organizational behavior, and decision-making techniques. Our findings conclude that leveraging the IoT changes the nature of digital innovation and brings real-time sustainability to organizational behavior.

Recently, the internet of things (IoT) has evolved into a breakthrough for creating intelligent settings. Any technology's reliance on the IoT model is seen as having major security and privacy issues. The various conceivable attacks carried out by intruders give rise to privacy and security considerations. Therefore, creating an intrusion detection system is crucial for identifying attacks and anomalies in the IoT system. In this work, a deep belief network (DBN) algorithm model for the intrusion detection system has been proposed. The CICIDS 2017 dataset is used for the performance analysis of the current IDS model in terms of assaults and anomaly detection. Accuracy, recall, precision, F1-score, detection rate, and other characteristics were all improved by the proposed method. IoT technology has revolutionized how healthcare is provided to patients. Medical institutions are very concerned about IoT security because of the network enabled IoT devices' integration with healthcare network infrastructure.

In the past few years, Internet of Things (IoT) devices have emerged and spread everywhere. Many researchers have been motivated to study the security issues of IoT devices due to the sensitive information they carry about their owners. Privacy is not simply about encryption and access authorization, but also about what kind of information is transmitted, how it used and to whom it will be shared with. Thus, IoT manufacturers should be compelled to issue Privacy Policy Agreements for their respective devices as well as ensure that the actual behavior of the IoT device complies with the issued privacy policy. In this paper, we implement a test bed for ensuring compliance of Internet of Things data disclosure to the corresponding privacy policy. The fundamental approach used in the test bed is to capture the data traffic between the IoT device and the cloud, between the IoT device and its application on the smart-phone, and between the IoT application and the cloud and analyze those packets for various features. We test 11 IoT manufacturers and the results reveal that half of those IoT manufacturers do not have an adequate privacy policy specifically for their IoT devices. In addition, we prove that the action of two IoT devices does not comply with what they stated in their privacy policy agreement.

Companies are obliged to have privacy policies in place to notify the users about their privacy practices. However, privacy policies are not generally efficient and they fail to help users make informed decisions. The privacy policy challenges get worse when dealing with Internet of Things (IoT) as these devices have access to very sensitive data about users. Much research have addressed these problems using different approaches to make privacy policies more understandable. With the increase in the use of IoT, there is a large demand to improve privacy policies and provide better notices. In this paper, we propose a method to extract notice and choice statements from privacy policies for IoT users to decide about their privacy. We explain our approach by considering Google Home and Amazon Alexa as case studies. We show how our method creates notice and choices and evaluate our approach by conducting a survey among users. Our result shows that our proposed method is useful for the users to make decision and understand privacy notices.

Precision aquaculture is revolutionizing fish farming through the integration of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and remote sensing. These innovations enhance real-time monitoring, automation, and data-driven decision-making, thereby improving efficiency, sustainability, and productivity. IoT-enabled sensors facilitate continuous tracking of critical environmental parameters, while AI-driven analytics optimize feed management, disease detection, and operational processes. Remote sensing complements these technologies by providing large-scale monitoring and environmental assessments, ensuring compliance with ecological and regulatory standards. Despite these advancements, the widespread adoption of precision aquaculture faces challenges, including technical limitations, regulatory complexities, and high initial investment costs. However, emerging technologies such as edge computing and blockchain offer promising solutions to enhance system efficiency, transparency, and data security. Economic analyses highlight the long-term viability of these innovations, particularly in resource-limited settings where sustainable aquaculture can contribute to food security and economic growth. This paper explores the synergy between IoT, AI, and remote sensing in precision aquaculture, analyzing case studies that demonstrate successful integration. It further examines policy and economic considerations essential for facilitating technology adoption and ensuring industry scalability. By addressing current challenges and leveraging future innovations, precision aquaculture has the potential to transform global seafood production, promoting sustainability while meeting the increasing demand for aquatic food resources.

Life is pleasant simpler and easier in practically every way as automation technology advances. A wireless automation system that uses the internet to monitor health, functionality and features from anywhere in the world is known as a smart health automation. With the use of sophisticated data retrieval and classification models, disease may potentially be studied or even unusual health problems could be predicted. Employing Internet of Things (IoT) technology to observe human daily life, which includes activities, physiological characteristics, stress, and vital signs. There are numerous uses for the Internet of Things (IoT), including in manufacturing, healthcare, agriculture, and other industries. Wearable technology has become widely used in the health monitoring system in recent years, which has encouraged the development of the Internet of Medical Things (IoMT). The IoMT can significantly lower the mortality rate by aiding in early disease detection. Because of their various applications to users, smart healthcare systems have gained importance. It can be monitored and controlled remotely. This chapter includes a roadmap on IoT, automation technology and various recommendations into smart healthcare automation system sensors

The advancement of Internet of Things (IoT) technologies has ushered in a new era of smart homes, promising convenience and automation. However, alongside these advancements, concerns regarding the security and privacy of Internet of Things smart homes have garnered significant attention. The study embarked on a user-centric analysis, delving into the intricacies of security and privacy concerns in the adoption of Internet of Things smart homes. The primary purpose of this research was to investigate the security and privacy concerns that users harbour when adopting Internet of Things smart home technologies. We used SMART-PLS version (4.0.9.6) as the data analysis tool, to examine the concerns and to gain a comprehensive understanding of their impact on adoption. The analysis was, rooted in quantitative research design and based on data gathered through an online questionnaire distributed to the target population of 325 participants. The research response rate was 92%. The hypotheses examined unveiled statistically significant relationships, culminating in results indicating an R<sup>2</sup> of 0.762. This implies that approximately 76.2% of the rationale behind individuals' decisions to either adopt or refrain from using IoT smart home devices, with a focus on security and privacy considerations, can be elucidated by our proposed Structural Equation Model. his model served as a comprehensive lens through which we dissected the intricate interplay of variables shaping user attitudes and behaviors. The study sheds light on the critical concerns of security and privacy within the IoT smart home domain. By leveraging quantitative analysis and a well-crafted Structural Equation Model, we offer valuable insights into the factors influencing user adoption decisions. The research contributes to the broader discourse IoT technology adoption and serves as a foundation for future studies and policy considerations in the ever-evolving landscape of smart homes.

Digitalising food manufacturing

An empirical investigation into the enhancement of decision-making capabilities in corporate sustainability leadership through Internet of Things (IoT) integration

Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilities

Understanding the implementation of the Internet of Things (IoT) in sustainable construction projects. Implementing the IoT in sustainable construction schemes can offer many benefits, including resource management, remote control and monitoring, improving safety, optimizing building performance and data-driven decision-making. This research presents a unique effort to enhance our comprehension of IoT implementation in such projects. We analyzed 55 studies using a systematic approach to literature selection and filtration, using theoretical models for Internet of Things (IoT) implementation to study the sustainability of construction industries. The results revealed that monitoring constituted 9 (16.4%), machine/equipment control 2 (3.6%), safety in construction 5 (9.1%), water management 4 (7.3%), 4IR 2 (3.6%), BIM 5 (9.1%), green IoT 5 (9.1%), prefabricated design 2 (3.6%), IoT adoption level 6 (10.9%), and other aspects accounted for 15 (27.3%). The primary areas of IoT application in the construction industry include BIM, remote application monitoring, construction management, repair and equipment services, construction implements, and tracking equipment. Notably, there is a lack of research awareness regarding the potential integration of IoT and BIM by construction companies. Despite numerous studies on IoT adoption, limited literature addresses the challenges and awareness of how IoT tools impact implementation in the building industry. Our review proposes a framework for implementing sustainable construction projects to address this gap. However, further investigation is required to analyze potential security threats associated with IoT in a smart city setting, explore promising countermeasures, and delve into open research issues.

The application of the Internet of Things (IoT) in energy infrastructure is revolutionizing operations and maintenance practices, driving efficiencies, and enhancing sustainability across the sector. This paper explores the transformative impact of IoT technologies on energy infrastructure, focusing on how they facilitate real-time monitoring, predictive maintenance, and data-driven decision-making. By leveraging IoT devices and systems, energy operators can optimize performance, reduce operational costs, and minimize downtime, ultimately contributing to more resilient and sustainable energy systems. The discussion highlights key IoT applications within energy infrastructure, including smart grids, renewable energy systems, and conventional power plants. Smart sensors and devices enable continuous data collection, providing insights into equipment performance, energy consumption, and environmental conditions. Predictive analytics, powered by IoT data, allows operators to anticipate equipment failures and conduct maintenance proactively, thereby extending asset life and improving reliability. Additionally, the abstract addresses the challenges associated with IoT integration in energy infrastructure, such as cybersecurity risks, data management issues, and the need for interoperability among diverse systems. It emphasizes the importance of establishing robust security protocols to protect sensitive data and ensure the integrity of IoT networks. Furthermore, the role of regulatory frameworks and industry standards in promoting the safe and effective deployment of IoT technologies is discussed. The paper also examines case studies of successful IoT implementations in energy infrastructure, showcasing the lessons learned from these initiatives. These examples illustrate the potential for IoT to enhance operational efficiency, reduce greenhouse gas emissions, and support the transition to cleaner energy sources. In conclusion, the abstract underscores that the adoption of IoT technologies in energy infrastructure is not just a trend but a necessity for the future of operations and maintenance. As the energy sector continues to evolve, embracing IoT will be crucial for achieving sustainability goals and meeting the growing demands for reliable energy supply.