A verifiable efficient federated learning method based on adaptive Boltzmann selection for data processing in the internet of things

Robotics involves design, construction, operation, and use of intelligent machines that possess the ability to sense, compute, manipulate, and navigate environments to monitor events and execute an appropriate course of action. Internet of Things (IoT) on the other hand is a fast-developing novel technology consisting of group of uniquely addressable heterogeneous smart objects or tiny devices (things) interconnected via the Internet to share and process data from different sources. IoT is designed with the goal to “connect everything and everyone everywhere to everything and everyone else.” The two technologies, IoT and robotics, have evolved into Internet of Robotic Things (IoRT) by the creation of a synergy between the two. IoRT aims at enhancing the current IoT with active sensing and actuation from robotics. This idea opened a novel opportunity for collaboration between IoT and robotics applications and research communities. However, most application domains of IoT and robotics have not fully explored the use of IoRT. This chapter discusses the (potential) applications of IoT-aided robotics in different domains, explaining how robots can extend the capabilities of existing IoT architectures to make them more knowledgeable and smarter; discuss some of the challenges in the full realization and application of IoRT; and lastly proposes an IoRT architecture for smart library management, an area that has not received much attention in the research community.

Preeclampsia detection research has started exploring some methods to diagnose and predict preeclampsia. Machine learning (ML) methods and the Internet of Things (IoT) have been successfully implemented in medical research to improve the diagnosis and prevention of complex diseases and syndromes. The goal of this work is to undertake a review of the most recent work on preeclampsia detection. The research focused on articles related to the keywords 'machine learning, 'Internet of Things, 'IoT', 'medical', and preeclampsia in five main databases, namely IEEEXplore, ScienceDirect, SpringerLink, ResearchGate, and ACM Digital Library, etc. We selected and reviewed 90 articles in the end. The final discussion highlights research gaps that remain to be investigated in the cognitive approach to IoT. The study found that preeclampsia detection based on the internet of Medical things (IoMT) was not found, so it became a big opportunity to develop this research in the future.

Usually, IoT devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with the emerging unexpected conditions. The Internet of Robotic Things is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and to decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. The Internet of Robotic Things is a better Internet of Things (IoT) solution due to its ability to bridge the gap between IT and real operations. The IoRT may face several challenges in power consumption and network bandwidth limitation due to the growth in the information dissemination over the Robotic network. The main objective of this paper is to introduce an overview of the concepts and challenges in the Internet of Robotic Things based on Fog Computing technique. Further, a framework of Fog based IoRT is introduced.

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

The Internet of Things (IoT) has revolutionized the way people interact with the digital world by connecting a vast array of devices, ranging from smart home appliances to industrial sensors. While this interconnected ecosystem brings unprecedented convenience and efficiency, it also introduces significant privacy and security concerns. The continuous collection, transmission, and processing of data in IoT systems create potential vulnerabilities and privacy risks for users and organizations alike. This work presents an in-depth analysis of the key challenges surrounding IoT privacy and security. The study explores the various threats and risks that IoT devices and networks face, including data breaches, unauthorized access, and distributed denial-of-service (DDoS) attacks. Additionally, the privacy implications of the extensive data collection and profiling inherent in IoT applications are delved into. The importance of adopting a privacy by design approach is highlighted, embedding privacy and security measures throughout the entire lifecycle of IoT devices and services. The significance of data minimization, consent management, and strong encryption protocols to protect user data and uphold privacy rights is discussed. Furthermore, an overview of existing IoT privacy and security regulations and standards is provided, emphasizing the need for compliance and collaboration between industry stakeholders and policymakers. The role of AI-driven solutions, blockchain technology, and biometric security in enhancing IoT security measures for the future is explored. Through a comprehensive examination of real-world case studies, the potential consequences of inadequate IoT privacy and security measures are illustrated. These incidents underscore the urgency of addressing IoT security vulnerabilities and the importance of regular security audits and updates. Finally, a call to action is proposed for industry stakeholders, policymakers, and users to prioritize privacy and security in the IoT ecosystem. Collaborative efforts, research, and innovation are crucial in building a secure and privacy-respecting IoT environment, ensuring that the full potential of this transformative technology is realized without compromising user data and privacy rights.

The Internet of Things (IoT) is rapidly becoming one of the most talked-about and essential components of any digitization process. The IoT is comprised of several key necessary components, the most important of which are sensors, communication (the internet), and user interfaces for data processing. IoTs are currently finding applications in virtually every industry, including healthcare, where they are known as the internet of medical things (IoMT), industry, where they are known as the industrial internet of things (IIoT), and interconnection between people, where they are known as the internet of everything (IoE). The challenge is to leverage the Internet of Things (IoT), technology, and data to create smarter and more sustainable cities that enhance the quality of life for residents. Therefore, in this article; we have demonstrated the use of the IoT in a variety of applications for smart communities. These applications include smart transportation, smart water management, smart garbage management, smart house illumination, smart parking, smart infrastructure, etc. This research also includes an explanation of the flow process of implementing the IoT in different applications of smart communities, as well as their characteristics and particular applications. Along with their flow illustration, the stages involved in the implementation of smart city applications and the components they consist of are also displayed here. We have also taken into consideration the instances of particular cases and their implementation utilizing IoT. Some of these cases include the automated water collection methods of smart water management systems as well as the condition of the water. Based on the findings of the research, we came to the conclusion that IoT devices play an essential role in each and every one of the smart city project implementations.

Edge AI for Internet of Medical Things: A literature review

In deploying the Internet of Things (IoT) and Internet of Medical Things (IoMT)-based applications and infrastructures, the researchers faced many sensors and their output’s values, which have transferred between service requesters and servers. Some case studies addressed the different methods and technologies, including machine learning algorithms, deep learning accelerators, Processing-In-Memory (PIM), and neuromorphic computing (NC) approaches to support the data processing complexity and communication between IoMT nodes. With inspiring human brain structure, some researchers tackled the challenges of rising IoT- and IoMT-based applications and neural structures’ simulation. A defective device has destructive effects on the performance and cost of the applications, and their detection is challenging for a communication infrastructure with many devices. We inspired astrocyte cells to map the flow (AFM) of the Internet of Medical Things onto mesh network processing elements (PEs), and detect the defective devices based on a phagocytosis model. This study focuses on an astrocyte’s cholesterol distribution into neurons and presents an algorithm that utilizes its pattern to distribute IoMT’s dataflow and detect the defective devices. We researched Alzheimer’s symptoms to understand astrocyte and phagocytosis functions against the disease and employ the vaccination COVID-19 dataset to define a set of task graphs. The study improves total runtime and energy by approximately 60.85% and 52.38% after implementing AFM, compared with before astrocyte-flow mapping, which helps IoMT’s infrastructure developers to provide healthcare services to the requesters with minimal cost and high accuracy.

The new era of computing technology is what many are calling the Internet of Things (IoT). The first Coca Cola vending machine was an Internet-connected appliance. The IoT focuses on 5 As (anytime, anywhere, anyone, anyhow, anything) and 3 Is (interconnect, intelligent, interaction). Machine to machine, machine to infrastructure, machine to environment, the Internet of Everything, the Internet of Intelligent Things or Devices, intelligent systems—call it whatever you want, but it’s happening, and its potential is huge. The IoT is seen as billions of smart, connected “intelligent things” (a sort of “universal global neural network” in the cloud) that encompass every aspect of human life, and its foundation is the intelligence that embedded processing provides. The IoT consists of smart machines interacting and communicating with other machines, objects, environments, and infrastructures. As a result, huge volumes of data are being generated, and that data is being processed into useful actions that can “command and control” things to make human lives much easier and safer – and to reduce human impact on the environment. The creativity of this new era is boundless, with amazing potential to improve human lives. The chapter is an extensive reference to the different use cases that have actually worked with multiple variations, utility, and applications, as well as the evolution of the IoT. The chapter will provide multiple case studies, like solar tracker, robot track follower, soil moisture-level detection, control based on temperature, and the most important parameter, “cloud” connected to things for huge data collection and processing on temperature. All these are explained using actual hardware, sensors, devices, etc., used to implement the use cases.

PurposeIn data grids, replication has been regarded as a crucial optimization strategy. Computing tasks are performed on IoT gateways at the cloud edges to obtain a prompt response. So, investigating the data replication mechanisms in the IoT is necessary. Henceforth, a systematic survey of data replication strategies in IoT techniques is presented in this paper, and some suggestions are offered for the upcoming works. In two key classifications, various parameters dependent on the analysis of the prevalent approaches are considered. The pros and cons associated with chosen strategies have been explored, and the essential problems of them have been presented to boost the future of more effective data replication strategies. We have also discovered gaps in papers and provided solutions for them.Design/methodology/approachProgress in Information Technology (IT) growth has brought the Internet of Things (IoT) into life to take a vital role in our everyday lifestyles. Big IoT-generated data brings tremendous data processing challenges. One of the most challenging problems is data replication to improve fault-tolerance, reliability, and accessibility. In this way, if the primary data source fails, a replica can be swapped in immediately. There is a significant influence on the IoT created by data replication techniques, but no extensive and systematic research exists in this area. There is still no systematic and full way to address the relevant methods and evaluate them. Hence, in the present investigation, a literature review is indicated on the IoT-based data replication from papers published until 2021. Based on the given guidelines, chosen papers are reviewed. After establishing exclusion and inclusion criteria, an independent systematic search in Google Scholar, ACM, Scopus, Eric, Science Direct, Springer link, Emerald, Global ProQuest, and IEEE for relevant studies has been performed, and 21(6 paper analyzed in section 1 and 15 paper analyzed in section 3) papers have been analyzed.FindingsThe results showed that data replication mechanisms in the IoT algorithms outperform other algorithms regarding impressive network utilization, job implementation time, hit ratio, total replication number, and the portion of utilized storage in percentage. Although a few ideas have been suggested that fix different facets of IoT data management, we predict that there is still space for development and more study. Thus, in order to design innovative and more effective methods for future IoT-based structures, we explored open research directions in the domain of efficient data processing.Research limitations/implicationsThe present investigation encountered some drawbacks. First of all, only certain papers published in English were included. It is evident that some papers exist on data replication processes in the IoT written in other languages, but they were not included in our research. Next, the current report has only analyzed the mined based on data replication processes and IoT keyword discovery. The methods for data replication in the IoT would not be printed with keywords specified. In this review, the papers presented in national conferences and journals are neglected. In order to achieve the highest ability, this analysis contains papers from major global academic journals.Practical implicationsTo appreciate the significance and accuracy of the data often produced by different entities, the article illustrates that data provenance is essential. The results contribute to providing strong suggestions for future IoT studies. To be able to view the data, administrators have to modify novel abilities. The current analysis will deal with the speed of publications and suggest the findings of research and experience as a future path for IoT data replication decision-makers.Social implicationsIn general, the rise in the knowledge degree of scientists, academics, and managers will enhance administrators' positive and consciously behavioral actions in handling IoT environments. We anticipate that the consequences of the present report could lead investigators to produce more efficient data replication methods in IoT regarding the data type and data volume.Originality/valueThis report provides a detailed literature review on data replication strategies relying on IoT. The lack of such papers increases the importance of this paper. Utilizing the responses to the study queries, data replication's primary purpose, current problems, study concepts, and processes in IoT are summarized exclusively. This approach will allow investigators to establish a more reliable IoT technique for data replication in the future. To the best of our understanding, our research is the first to provide a thorough overview and evaluation of the current solutions by categorizing them into static/dynamic replication and distributed replication subcategories. By outlining possible future study paths, we conclude the article.

The Internet of Things (IoT) is a concept that is transforming our everyday life. Because of its capacity to change people's lives, it has become a vital element of our lives. IoT devices are being used by an increasing number of businesses because they provide new opportunities for wearable devices, home appliances, and healthcare. With all of these possibilities, the risks associated with IoT security are increasing. One of the most challenging aspects of any IoT application is device identification. Things in the IoT share and process data without the need for human interaction. As a result of their total authority, these entities must authenticate and recognize one another effectively. Failure to legitimately authenticating the IoT devices can make them vulnerable to a number of assaults such as DDoS and replay attacks. It is nearly hard to develop an effective authentication system due to the size and other characteristics of IoT. Although a lot of work has been done on this issue, the majority of these solutions rely on a centralized system. These centralized systems are segregated and incompatible with one another, making information exchange between them impossible. In addition, once a centralized authority is attacked, the user's privacy can be exposed easily. Proof of security, decentralization, and anonymity characteristics of blockchain can help address these issues. With blockchain technology applied to IoT systems, obstacles to IoT architecture development and security can be overcome. This paper, therefore, focuses on covering current advancements made in the application of blockchain for authentication in IoT. There is a lack of survey papers on the use of blockchain for authentication in IoT this paper will serve as an aide. This paper will also add to the knowledge of researchers who are interested in IoT security.

Research on Fusion Application of Mobile Internet and Internet of Things in Digital Campus

Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occurrences. The detection of lung regions in images is a classic medical challenge. Studies show that computational methods contribute significantly to the medical diagnosis of lung pathologies by Computerized Tomography (CT), as well as through Internet of Things (IoT) methods based in the context on the health of things. The present work proposes a new model based on IoT for classification and segmentation of pulmonary CT images, applying the transfer learning technique in deep learning methods combined with Parzen’s probability density. The proposed model uses an Application Programming Interface (API) based on the Internet of Medical Things to classify lung images. The approach was very effective, with results above 98% accuracy for classification in pulmonary images. Then the model proceeds to the lung segmentation stage using the Mask R-CNN network to create a pulmonary map and use fine-tuning to find the pulmonary borders on the CT image. The experiment was a success, the proposed method performed better than other works in the literature, reaching high segmentation metrics values such as accuracy of 98.34%. Besides reaching 5.43 s in segmentation time and overcoming other transfer learning models, our methodology stands out among the others because it is fully automatic. The proposed approach has simplified the segmentation process using transfer learning. It has introduced a faster and more effective method for better-performing lung segmentation, making our model fully automatic and robust.

Relevance. The growing integration of Internet of Things (IoT) technologies into all areas of human life – from intelligent households to smart city infrastructure – is accompanied by an exponential increase in the volume of data being collected, transmitted, and processed in real time. When combined with artificial intelligence technologies, this data becomes the foundation for making autonomous decisions, predicting user behavior, and adapting environments to the needs of specific individuals. However, it is precisely in this context that the critically important issue of personal data protection arises. Many IoT devices operate in uncontrolled environments, have limited resources for cryptographic protection, and are vulnerable to cyberattacks and unauthorized data collection. Meanwhile, artificial intelligence algorithms used to analyze this data often exhibit the “black box” problem, where it is impossible to fully explain how and why a particular decision was made based on personalized data. The lack of transparency, combined with broad access to sensitive information, threatens fundamental human rights to privacy. The relevance of this topic is driven by the need to find balanced technical solutions that enable both effective analysis of large-scale data in IoT environments and a high level of data security. In this regard, the study of modern methods for processing, analyzing, and protecting data in IoT systems, adapted to the requirements of ethical artificial intelligence and digital privacy standards, represents one of the key challenges of contemporary digital science. The object of research: the processes of data collection, processing, analysis, and protection in IoT systems, particularly those components related to the use of users' personal information and its processing through artificial intelligence methods. Purpose of the article: research of modern methods for data processing and analysis in IoT systems. The objective of the work is to identify the most effective approaches to secure data handling, characterize existing privacy threats, and assess the potential for integrating protected analytical algorithms that meet both the technical and ethical requirements of the digital environment. Research results. A comprehensive analysis of modern approaches to data collection, processing, analysis, and protection in IoT systems has been conducted, particularly in the context of the growing role of artificial intelligence. The technological foundations of IoT functionality were examined, key architectural components identified, and their role in creating digital ecosystems for monitoring, management, and decision-making across various sectors – from household systems to critical infrastructure – was investigated. Special attention was paid to data preprocessing methods, which help reduce information load, improve the quality of analysis, and adapt data flows to the requirements of intelligent algorithms. It was demonstrated that the use of edge processing and local-level aggregation enhances both system performance and security. The main types of databases for IoT – especially those optimized for time series – were analyzed, along with tools for handling large volumes of data in cloud and hybrid environments. Conclusions. Data collection methods in IoT are multilayered and closely linked to the requirements for energy efficiency, security, latency, and system scalability. The quality and reliability of the collected information form the foundation for subsequent processing, analysis, and decision-making; therefore, the selection of sensors, communication protocols, and architectural models is of strategic importance for any IoT system. Preprocessing and efficient data storage in IoT are critical stages that ensure the quality, security, and usability of information for further analysis. They determine not only the accuracy of analytics but also the stability, scalability, and compliance with regulatory standards. This creates a demand for the development of adaptive, intelligent data processing and storage systems capable of dynamically responding to changes in device operation context and user requirements. The successful implementation of secure IoT solutions requires an integrated approach that combines technical expertise, legal knowledge, and ethical responsibility.

This paper explores the transformative impact of the Internet of Medical Things (IoMT) on healthcare. By integrating medical equipment and sensors with the internet, IoMT enables real-time monitoring of patient health, remote patient care, and individualized treatment plans. IoMT significantly improves several healthcare domains, including managing chronic diseases, patient safety, and drug adherence, resulting in better patient outcomes and reduced expenses. Technologies like blockchain, Artificial Intelligence (AI), and cloud computing further boost IoMT’s capabilities in healthcare. Blockchain enhances data security and interoperability, AI analyzes massive volumes of health data to find patterns and make predictions, and cloud computing offers scalable and cost-effective data processing and storage. Therefore, this paper provides a comprehensive review of the Internet of Things (IoT) and IoMT-based edge-intelligent smart healthcare, focusing on publications published between 2018 and 2024. The review addresses numerous studies on IoT, IoMT, AI, edge and cloud computing, security, Deep Learning, and blockchain. The obstacles facing IoMT are also covered in this paper, including interoperability issues, regulatory compliance, and privacy and data security concerns. Finally, recommendations for further studies are provided.