Identity Authentication Under Internet of Everything Based on Edge Computing

This research aims to investigate the possibility and viability of open-source Internet of Things (IoT) and edge-compatible equipment in the field of health monitoring. The focus is on exploring various IoT health monitoring environments used in e-health applications, taking into account crucial aspects such as sensor integration, data collection methods, communication protocols, security measures, scalability, and regulatory requirements. The research begins by examining existing IoT health monitoring environments to gain a comprehensive understanding of their strengths and limitations. This analysis helps identify the gaps and challenges that open-source IoT and edge devices can address in the context of health monitoring. Building upon this groundwork, a novel IoT-edge-powered deep learning system will be developed specifically for a targeted health monitoring environment. The system will leverage the capabilities of IoT devices, integrate various sensors to capture relevant health data, harness edge computing techniques to process data locally, and utilize deep learning algorithms for advanced analysis and inference. Special emphasis will be placed on optimizing data preprocessing, feature extraction, model training, and overall system performance. To assess the effectiveness of the proposed IoT-edge-deep learning environment, a comparison with currently implemented solutions will be done. Key indicators such as precision, accuracy, and efficiency will be the focus of the assessment, aiming to highlight the advantages and improvements offered by the developed system. The results of this research project are anticipated to make major contributions to the implementation of IoT and edge computing in health monitoring. By exploring the feasibility of open-source devices, the research will demonstrate their potential for democratizing access to health monitoring technology. Additionally, the exploration of e-health monitoring environments will provide valuable insights into best practices, challenges, and regulatory considerations. Finally, the introduction of an innovative deep learning system will enhance health monitoring capabilities, enabling more accurate and timely detection of health-related conditions. This research holds promise for advancing the field of health monitoring by combining IoT, edge computing, and deep learning, ultimately raising the standard of healthcare services and patient outcomes.

The basics of Internet of Things (IoT) is the attribute of Internet of Objects, expected as a flexible strategy for giving several facilities. Condensed clever units create a critical phase of IoT. They vary extensively in practice, volume, service facility, and calculation strength. However, the incorporation of these clever matters in the widespread internetworking proposed IoT of safety threat due to the act the most internetwork technologies along with verbal exchange manners that had not been now designed to guide IoT. Furthermore, the trade of IoT has produced public safety matter, such as nonpublic secrecy issues, risk of cyber threats, and trained crime. Have guidelines or suggestions on the analysis of IoT security and contribute to improvement. This control offers full information of the open penetration and retrieval as opposed to the IoT edge side layer, which are done in three phases: edge nodes, transmission, and edge computing. The method to reach this target, first, we quickly define those popular IoT recommendation models and outline protection in the IoT's framework. We also talk about the feasible purposes of IoT and the inspiration of the intruder and set new goals in this new model. With such rise in IoT and with the advent of ongoing digital applications, huge amount of data is produced each and every day, leading to the emergence of the term big data. In this chapter, we will be collaborating the detailed security study in case of IoT and big data.

A blockchain-enabled privacy-preserving authentication management protocol for Internet of Medical Things

Investigation on big data evaluation and visualization of internet of things based on edge computing

Industry 4.0 is an aggregate of recent technologies including artificial intelligence, big data, edge computing, and the Internet of Things (IoT) to enhance efficiency and real-time decision-making. Industry 4.0 data analytics demands a privacy-focused approach, and federated learning offers a viable solution for such scenarios. It allows each edge device to train the model locally using its own collected data and shares only the model updates with the server without the need to share real collected data. However, communication and computational costs for sharing model updates and performance are major bottlenecks for resource-constrained edge devices. This study introduces a representative-based parameter-sharing framework that aims to enhance the efficiency of federated learning in the Industry 4.0 environment. The framework begins with a server by distributing an initial model to edge devices, which then train it locally and send updated parameters back to the server for aggregation. To reduce communication and computational costs, the framework identifies groups of devices with similar parameter distributions and only sends updates from the resourceful and better-performing device, termed the cluster head, to the server. A backup cluster head is also elected to ensure reliability. Clustering is performed based on the device’s parameter distributions and data characteristics. Moreover, the server incorporates randomly selected past aggregated parameters into the current aggregation process through weighted averaging where more recent parameters are given greater weight to enhance model performance. Comparative experimental evaluation with the state of the art using a testbed dataset demonstrates promising results by minimizing computational cost while preserving prediction performance, which ultimately enhances data analytics on edge devices in industrial environments.

Flood disasters affect millions of people across the world by causing severe loss of life and colossal damage to property. Internet of Things (IoT) has been applied in areas such as flood prediction, flood monitoring, and flood detection. Although IoT technologies cannot stop the occurrence of flood disasters, they are an exceptionally valuable apparatus for conveyance of catastrophe readiness data. Advances have been made in flood prediction using artificial neural networks (ANN). Despite the various advancements in flood prediction systems, there has been less focus on the utilisation of edge computing for improved efficiency and reliability of such systems. In this paper, we present a system for short-term flood prediction that uses IoT and ANN, where the prediction computation is carried out on a low power edge device. The system monitors real-time rainfall and water level sensor data and uses the temporal correlative information for ahead-of-time prediction of flood water levels using long short-term memory. The system can be deployed on battery-powered IoT devices. The results of evaluating a prototype of the system indicate that our model is suitable for real-time flood prediction. Furthermore, we obtain a low response time for running the ANN prediction analysis on the low-power edge device. The application of ANN with edge computing will help improve the efficiency of real-time flood early warning systems by bringing the prediction computation close to where data is collected.

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...

Edge computing has gradually availed itself as one of the most important technologies in the Internet of Things (IoT) to influence the processing, management, and utilization of data.A major advantage that is offered by edge computing by bringing computation and storage resources closer to IoT devices is that it minimizes latency as well as conserves bandwidth and offers greater capabilities for real time decision making.These changes from massive cloud processing to multiple neighboring edge nodes solve IoT architectural challenges common in earlier types of design, including those with low-latency responses and offline capabilities of applications such as smart cities, self-driving cars, and industrial control systems.The potential advantages of edge computing in IoT include privacy and security, because the collected information can be analyzed on the fly on the endpoint without sending it to the cloud; scalability, given that a great amount of IoT devices can be monitored by edge nodes without putting extra load on the centralized facilities.However, the use of edge computing in IoT also poses some challenges for example restricted resources at edge nodes, issue of the network infrastructure and more so the security that has to be enhanced.This abstract discusses the existing works done on architecture of edge computing IoT, the advantages and the limitation of integrating edge computing IoT system and indicates how it is capable of revolutionizing industries and enable a new IoT generation application system equipped with intelligence and decentralization.

Recently, most of the Internet of things (IoT) infrastructures are highly centralized with single points of failure, which results in serious security and privacy issues of IoT data. Fortunately, blockchain technique can provide a decentralized and secure IoT framework to deal with security issues based on the characteristics of decentralization, non-tampering, openness, transparency, and traceability. However, the blockchain consensus protocol guarantees the safety and reliability of data, but it also brings problems such as scalability limitations and poor storage extensibility, resulting in the inability to directly integrate blockchain and the IoT in existing conditions. In this article, a private three-tier local blockchain, Three-tier architecture Blockchain (TBchain), is proposed to solve the problem by splitting part of the transactions in the public blockchain and locking them in a higher-level blockchain TBchain. Additionally, the private blockchain TBchain is connected to the public blockchain to build a hierarchical blockchain network to provide privacy protection for the IoT data stored on the blockchain. Finally, we implement an IoT framework based on TBchain and the InterPlanetary File System (IPFS) to realize the decentralized IoT, which guarantees the user’s access control right to personal data. Experimental results show that the IoT framework based on TBchain and IPFS realizes the user’s access control right to personal data by verifying in advance to ensure the confidentiality and security of shared data, and improves the security and privacy of IoT data and transactions. Moreover, we prove that the scalability and storage extensibility of the blockchain is positively correlated with the number of data blocks in TBchain.

Power line communication (PLC) technology has created a niche use in the Internet of Things (IoT) by offering flexible and reliable connection among power-driven IoT devices/sensors over existing wired networks. In IoT over PLC networks, massive real-time data generated by the ever-growing connected devices will eventually pose an overwhelming burden on the IoT cloud, which in turn severely degrades the network performance. To cope with these issues, edge computing (EC) has emerged as a complement to cloud computing, aiming at offloading a portion of computing in the cloud to the network edges closer to the IoT devices. However, confronting a practical scenario that some electrical devices cannot communicate with wireless and mobile networks directly, existing EC paradigms may not be directly applied to IoT over PLC networks. In this paper, we propose a novel EC-IoT over PLC paradigm to reduce the transmission latency while migrating a portion of computing from the cloud to the edges. First, we develop a distributed EC platform to serve terminal users (TUs) in different IoT systems with various IoT services. Second, we put forth a cache-enabled scheme to store the popular contents from the cloud and edge sensors to reduce redundant data transmissions between TUs and the cloud. Finally, our experimental results demonstrate that the proposed EC-IoT over PLC network can significantly reduce energy consumption and transmission latency.

The Internet of Things (IoT) has transcended its futuristic perception and become an omnipresent reality. Its pervasive nature encompasses devices, sensors, clouds, big data, and business interactions. This revolutionary concept amalgamates traditional embedded systems with wireless microsensors, automation-driven control systems, and other elements to establish a vast infrastructure. The integration of wireless communication, micro electro mechanical devices, and the Internet has given rise to novel IoT applications. The IoT is essentially a network of interconnected objects accessible through the Internet, each object uniquely identifiable. The advent of IPv6, superseding IPv4, plays a pivotal role in expanding the address space for IoT development. The primary objective of IoT applications is to imbue objects with intelligence, eliminating the need for human intervention. However, the proliferation of smart nodes and the exponential data generated by each node present new challenges pertaining to data privacy, scalability, security, manageability, and other critical issues, which we delve into in this comprehensive exploration

Recent years have witnessed the widespread popularity of Internet of things (IoT). By providing sufficient data for model training and inference, IoT has promoted the development of artificial intelligence (AI) to a great extent. Under this background and trend, the traditional cloud computing model may nevertheless encounter many problems in independently tackling the massive data generated by IoT and meeting corresponding practical needs. In response, a new computing model called edge computing (EC) has drawn extensive attention from both industry and academia. With the continuous deepening of the research on EC, however, scholars have found that traditional (non-AI) methods have their limitations in enhancing the performance of EC. Seeing the successful application of AI in various fields, EC researchers start to set their sights on AI, especially from a perspective of machine learning, a branch of AI that has gained increased popularity in the past decades. In this article, we first explain the formal definition of EC and the reasons why EC has become a favorable computing model. Then, we discuss the problems of interest in EC. We summarize the traditional solutions and hightlight their limitations. By explaining the research results of using AI to optimize EC and applying AI to other fields under the EC architecture, this article can serve as a guide to explore new research ideas in these two aspects while enjoying the mutually beneficial relationship between AI and EC.

Major advances in the Internet of Things (IoT) technologies are offering unprecedented automated services to various sectors. IoT has shown great promise in transforming buildings and institutions into smart buildings and institutions, making traditional task management more efficient. In particular, there has been remarkable progress in successfully integrating IoT into various contexts, such as smart homes, businesses and cities. Success in such technological transformations is not only due to the proper deployment and implementation of IoT but also to the effective employment of related technologies, such as big data and cloud computing technologies, and making informed decisions on the inclusion of the required hardware and software for all the elements involved in the IoT environment. While a significant amount of IoT research in various contexts has been proliferated in recent years, attention given to its deployment for linguistic landscapes (LLs) is still very rare. The aim of this paper is to propose a smart Al-Noor Mall LL which takes into account the appropriate deployment of IoT tools and relevant technologies. The paper also thoroughly discusses the background of the field, covering IoT technologies, cloud computing, and big data studies, followed by a review of the recent publications involving the successful deployment of IoT technologies combined with big data and cloud computing in various contexts. The literature also covers the success factors that contribute to the robustness of the required real-time operating system (RTOS) in an IoT-based environment in order to enable IoT devices to manage their resources efficiently. Based on a detailed analysis of the literature, a three-level framework (i.e. the object, communications and application levels) together with the elements that are required for IoT-based smart LL solutions is proposed. A case study of Al-Noor Mall is used to validate the proposed framework and the results indicate that IBM Bluemix can help Al-Noor Mall management and customers to locate any particular shop in the huge shopping mall, which thereby makes the customers’ shopping experience more satisfying. Finally, the challenges (i.e. energy efficiency, security, and intelligent data analysis) facing the successful deployment of IoT technologies are discussed with proposals for future research directions in the IoT research field.

This study aims to explore how big data and the Internet of Things can improve the economic benefits of enterprises in online marketing. We first introduced the concept and development trend of big data, Internet of Things, and Internet marketing and then collected a large number of empirical data through the design and implementation of questionnaire survey. Our data analysis results show that the level of use of big data and the Internet of Things has a significant positive impact on business sales. However, there are also some problems and challenges in the implementation of big data and the Internet of Things, such as resource and technology limitations, and legal and ethical issues such as data security and privacy protection. Therefore, we suggest that enterprises should reasonably plan the use of big data and the Internet of Things according to their own resources and capabilities, while constantly upgrading their own technical level, focusing on data security and privacy protection, and tracking the latest technology and market trends. In addition, the government and industry organizations should also formulate and improve relevant policies and standards to support the development of big data and the Internet of Things and protect the interests of consumers. In general, big data and the Internet of Things provide new opportunities and challenges for enterprise online marketing, which need further research and discussion.

In current years, Internet of Things (IoT) has come a long way and is well emerged within many organizations and fields covering the sector of healthcare. Continuously, encompassing of the Internet of Medical Things (IoMT) in the field of the medical sector helps in the rise of medical data and analysis of that. Smart healthcare plays important role in human life and various research projects are running on it. It consists of diverse ingredients like wireless communication technology, cloud computing, edge computing, medical sensors, artificial intelligence (AI), IoMT, and IoT. The chapter introduces IoMT with respect to the multiple challenges like privacy, security and low-power operation faced while incorporating IoT with healthcare sector. For emergency treatment, a cloud-based healthcare framework provides a platform to support patients over internet communication by medical experts. Privacy and security to the medical records are on higher priority due to the very sensitive nature of the content. Moreover, a comprehensive survey on IoMT-based cloud and edge-intelligent healthcare system is also described. The current advancement in the field of IoT, IoMT, cloud computing, and AI related to privacy and security, some pathways for forthcoming work in collaboration of healthcare sector and IoT based on a collection of challenges and open issues are also addressed.