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Artículos de revistas sobre el tema "MIoT"

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Autor: Grafiati

Publicado: 14 de marzo de 2023

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1

Zhang, Liqiang, Guanjun Lin, Bixuan Gao, Zhibao Qin, Yonghang Tai y Jun Zhang. "Neural Model Stealing Attack to Smart Mobile Device on Intelligent Medical Platform". Wireless Communications and Mobile Computing 2020 (26 de noviembre de 2020): 1–10. http://dx.doi.org/10.1155/2020/8859489.

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To date, the Medical Internet of Things (MIoT) technology has been recognized and widely applied due to its convenience and practicality. The MIoT enables the application of machine learning to predict diseases of various kinds automatically and accurately, assisting and facilitating effective and efficient medical treatment. However, the MIoT are vulnerable to cyberattacks which have been constantly advancing. In this paper, we establish a MIoT platform and demonstrate a scenario where a trained Convolutional Neural Network (CNN) model for predicting lung cancer complicated with pulmonary embolism can be attacked. First, we use CNN to build a model to predict lung cancer complicated with pulmonary embolism and obtain high detection accuracy. Then, we build a copycat model using only a small amount of data labeled by the target network, aiming to steal the established prediction model. Experimental results prove that the stolen model can also achieve a relatively high prediction outcome, revealing that the copycat network could successfully copy the prediction performance from the target network to a large extent. This also shows that such a prediction model deployed on MIoT devices can be stolen by attackers, and effective prevention strategies are open questions for researchers.

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2

Tian, Qiao, Yun Lin, Xinghao Guo, Jin Wang, Osama AlFarraj y Amr Tolba. "An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology". Sensors 20, n.º 4 (22 de febrero de 2020): 1213. http://dx.doi.org/10.3390/s20041213.

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With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.

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3

Sun, Wencheng, Zhiping Cai, Yangyang Li, Fang Liu, Shengqun Fang y Guoyan Wang. "Security and Privacy in the Medical Internet of Things: A Review". Security and Communication Networks 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/5978636.

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Medical Internet of Things, also well known as MIoT, is playing a more and more important role in improving the health, safety, and care of billions of people after its showing up. Instead of going to the hospital for help, patients’ health-related parameters can be monitored remotely, continuously, and in real time, then processed, and transferred to medical data center, such as cloud storage, which greatly increases the efficiency, convenience, and cost performance of healthcare. The amount of data handled by MIoT devices grows exponentially, which means higher exposure of sensitive data. The security and privacy of the data collected from MIoT devices, either during their transmission to a cloud or while stored in a cloud, are major unsolved concerns. This paper focuses on the security and privacy requirements related to data flow in MIoT. In addition, we make in-depth study on the existing solutions to security and privacy issues, together with the open challenges and research issues for future work.

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4

Kabanov, Aleksey y Vadim Kramar. "Marine Internet of Things Platforms for Interoperability of Marine Robotic Agents: An Overview of Concepts and Architectures". Journal of Marine Science and Engineering 10, n.º 9 (10 de septiembre de 2022): 1279. http://dx.doi.org/10.3390/jmse10091279.

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The creation of a Marine Internet of Things platform, including the Underwater Internet of Things, is needed to ensure the interaction and digital navigation of heterogeneous marine robotic agents. It is necessary to combine the following robotic agents: autonomous underwater vehicles, remotely operated vehicles, active and passive marine sensors, buoys, underwater sonar stations, coastal communication posts, and other elements of the platform. To ensure the interaction of all these elements, it is necessary to use a common communication system within the platform, as well as a common navigation and control system to solve complex problems of the navigation and control of the movement of robotic agents in order to implement a joint mission to collect and transmit data, including video information in real time. The architecture of the Marine Internet of Things platform must first be defined in order to use a unified approach to data exchange. This article provides an overview of approaches to determining the architectures of network underwater and marine communication systems based on the concept of the Internet of Things. This paper provides a comprehensive study of MIoT applications, challenges, and architectures. The main contributions of this paper are summarized as follows: we introduce potential MIoT applications; we point out the challenges of MIoT (i.e., the differences between MIoT and IoT); and we analyze the MIoT system architecture.

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5

Sahidi, Toni Tegar, Achmad Basuki y Herman Tolle. "MIOT Framework, General Purpose Internet of Things Gateway using Smartphone". International Journal of Online Engineering (iJOE) 14, n.º 02 (28 de febrero de 2018): 6. http://dx.doi.org/10.3991/ijoe.v14i02.7326.

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<p class="western">Internet of things (IoT) is a complex system with few best practices in building ones, especially on handling real-time communication between IoT devices to the Internet. A framework is often used to fasten building IoT system. This paper present Mobile Internet of Things (MIOT), a framework which use a smartphone as the main component to handle communication between IoT device and the internet. A smartphone is used as the communication gateway (relay) for IoT devices and not as the IoT controller as in common Smartphone-IoT approach. For evaluation purpose, two implementations of IoT prototype scenario is built, an environmental monitoring and a remote controller (RC) car. The experiment shows a quick and easy deployment of IoT system. The Environment Monitoring able to send data to the server in real-time, and control The RC Car with a reasonable response time.</p><p><span>The experiment on 200 ms interval between each packet, shows that MIOT Framework has round-trip latency between MIOT Server and IoT hardware for ≈ 88.007 ms. The addition of smartphone as the main component in the framework (MIOT Apps) contribute to additional latency ≈ 13.145 ms. </span></p><p><span>Using a Smartphone as a gateway for IoT in MIOT Framework is possible and promising. It can be used as a best practice to develop a reliable IoT system which reduces time, effort, and learning overhead on building IoT systems.</span></p>

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6

Elhoseny, Mohamed, Navod Neranjan Thilakarathne, Mohammed I. Alghamdi, Rakesh Kumar Mahendran, Akber Abid Gardezi, Hesiri Weerasinghe y Anuradhi Welhenge. "Security and Privacy Issues in Medical Internet of Things: Overview, Countermeasures, Challenges and Future Directions". Sustainability 13, n.º 21 (21 de octubre de 2021): 11645. http://dx.doi.org/10.3390/su132111645.

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The rapid development and the expansion of Internet of Things (IoT)-powered technologies have strengthened the way we live and the quality of our lives in many ways by combining Internet and communication technologies through its ubiquitous nature. As a novel technological paradigm, this IoT is being served in many application domains including healthcare, surveillance, manufacturing, industrial automation, smart homes, the military, etc. Medical Internet of Things (MIoT), or the use of IoT in healthcare, is becoming a booming trend towards improving the health and wellbeing of billions of people by offering smooth and seamless medical facilities and by enhancing the services provided by medical practitioners, nurses, pharmaceutical companies, and other related government and non-government organizations. In recent times, this MIoT has gained higher attention for its potential to alleviate the massive burden on global healthcare, which has been caused by the rise of chronic diseases, the aging population, and emergency situations such as the recent COVID-19 global pandemic, where many government and non-government medical resources were challenged, owing to the rising demand for medical resources. It is evident that with this recent growing demand for MIoT, the associated technologies and its interconnected, heterogeneous nature adds new concerns as it becomes accessible to confidential patient data, often without patient or the medical staff consciousness, as the security and privacy of MIoT devices and technologies are often overlooked and undermined by relevant stakeholders. Hence, the growing security breaches that target the MIoT in healthcare are making the security and privacy of Medical IoT a crucial topic that is worth scrutinizing. In this study, we examined the current state of security and privacy of the MIoT, which has become of utmost concern among many security experts and researchers due to its rapid demand in recent times. Nevertheless, pertaining to the current state of security and privacy, we also examine and discuss a number of attack use cases, countermeasures and solutions, recent challenges, and anticipated future directions where further attention is required through this study.

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7

Lu, Yeting, Dawei Yang, Yuing Yang y Chunxue Bai. "MIoT integrates health, MM benefits humans". Clinical eHealth 5 (diciembre de 2022): 17–18. http://dx.doi.org/10.1016/j.ceh.2022.03.001.

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8

Igersheim, François. "Miot Claire, La Première Armée française". Revue d’Alsace, n.º 148 (25 de noviembre de 2022): 450–53. http://dx.doi.org/10.4000/alsace.5319.

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9

Zielinski, Zbigniew, Konrad Wrona, Janusz Furtak y Jan Chudzikiewicz. "Reliability and Fault Tolerance Solutions for MIoT". IEEE Communications Magazine 59, n.º 2 (febrero de 2021): 36–42. http://dx.doi.org/10.1109/mcom.001.2000940.

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10

Dinis, Hugo, João Rocha, Tiago Matos, Luís M. Gonçalves y Marcos Martins. "The Challenge of Long-Distance Over-the-Air Wireless Links in the Ocean: A Survey on Water-to-Water and Water-to-Land MIoT Communication". Applied Sciences 12, n.º 13 (24 de junio de 2022): 6439. http://dx.doi.org/10.3390/app12136439.

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Robust wireless communication networks are a cornerstone of the modern world, allowing data to be transferred quickly and reliably. Establishing such a network at sea, a Maritime Internet of Things (MIoT), would enhance services related to safety and security at sea, environmental protection, and research. However, given the remote and harsh nature of the sea, installing robust wireless communication networks with adequate data rates and low cost is a difficult endeavor. This paper reviews recent MIoT systems developed and deployed by researchers and engineers over the past few years. It contains an analysis of short-range and long-range over-the-air radio-frequency wireless communication protocols and the synergy between these two in the pursuit of an MIoT. The goal of this paper is to serve as a go-to guide for engineers and researchers that need to implement a wireless sensor network at sea. The selection criterion for the papers included in this review was that the implemented wireless communication networks were tested in a real-world scenario.

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11

Wang, Shu-Ching, Wei-Shu Hsiung, Chia-Fen Hsieh y Yao-Te Tsai. "Reliability Enhancement of Edge Computing Paradigm Using Agreement". Symmetry 11, n.º 2 (1 de febrero de 2019): 167. http://dx.doi.org/10.3390/sym11020167.

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Driven by the vision of the Internet of Things (IoT), there has been a dramatic shift in mobile computing in recent years from centralized mobile cloud computing (MCC) to mobile edge computing (MEC). The main features of MECs are to promote mobile computing, network control, and storage to the edge of the network in order to achieve computationally intensive and latency-critical applications on resource-constrained mobile devices. Therefore, MEC is proposed to enable computing directly at the edge of the network, which can deliver new applications and services, especially for the IoT. In order to provide a highly flexible and reliable platform for the IoT, a MEC-based IoT platform (MIoT) is proposed in this study. Through the MIoT, the information asymmetrical symmetry between the consumer and producer can be reduced to a certain extent. Because of the IoT platform, fault tolerance is an important research topic. In order to deal with the impact of a faulty component, it is important to reach an agreement in the event of a failure before performing certain special tasks. For example, the initial time of all devices and the time stamp of all applications should be the same in a smart city before further processing. However, previous protocols for distributed computing were not sufficient for MIoT. Therefore, in this study, a new polynomial time and optimal algorithm is proposed to revisit the agreement problem. The algorithm makes all fault-free nodes decide on the same initial value with minimal rounds of message exchanges and tolerate the maximal number of allowable faulty components in the MIoT.

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12

Zwetyenga, N., E. Broly, D. Guillier, A. Hallier, J. Levasseur y V. Moris. "Classification proposed of malignant intraosseous odontogenic tumors (MIOT)". Journal of Stomatology, Oral and Maxillofacial Surgery 118, n.º 3 (junio de 2017): 143–46. http://dx.doi.org/10.1016/j.jormas.2017.04.001.

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13

Darwish, Salaheddin, Ilia Nouretdinov y Stephen D. Wolthusen. "Towards Composable Threat Assessment for Medical IoT (MIoT)". Procedia Computer Science 113 (2017): 627–32. http://dx.doi.org/10.1016/j.procs.2017.08.314.

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14

Lin, Yi-Bing, Chien-Chao Tseng y Ming-Hung Wang. "Effects of Transport Network Slicing on 5G Applications". Future Internet 13, n.º 3 (11 de marzo de 2021): 69. http://dx.doi.org/10.3390/fi13030069.

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Network slicing is considered a key technology in enabling the underlying 5G mobile network infrastructure to meet diverse service requirements. In this article, we demonstrate how transport network slicing accommodates the various network service requirements of Massive IoT (MIoT), Critical IoT (CIoT), and Mobile Broadband (MBB) applications. Given that most of the research conducted previously to measure 5G network slicing is done through simulations, we utilized SimTalk, an IoT application traffic emulator, to emulate large amounts of realistic traffic patterns in order to study the effects of transport network slicing on IoT and MBB applications. Furthermore, we developed several MIoT, CIoT, and MBB applications that operate sustainably on several campuses and directed both real and emulated traffic into a Programming Protocol-Independent Packet Processors (P4)-based 5G testbed. We then examined the performance in terms of throughput, packet loss, and latency. Our study indicates that applications with different traffic characteristics need different corresponding Committed Information Rate (CIR) ratios. The CIR ratio is the CIR setting for a P4 meter in physical switch hardware over the aggregated data rate of applications of the same type. A low CIR ratio adversely affects the application’s performance because P4 switches will dispatch application packets to the low-priority queue if the packet arrival rate exceeds the CIR setting for the same type of applications. In our testbed, both exemplar MBB applications required a CIR ratio of 140% to achieve, respectively, a near 100% throughput percentage with a 0.0035% loss rate and an approximate 100% throughput percentage with a 0.0017% loss rate. However, the exemplar CIoT and MIoT applications required a CIR ratio of 120% and 100%, respectively, to reach a 100% throughput percentage without any packet loss. With the proper CIR settings for the P4 meters, the proposed transport network slicing mechanism can enforce the committed rates and fulfill the latency and reliability requirements for 5G MIoT, CIoT, and MBB applications in both TCP and UDP.

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15

Moon, Hyeongyun y Daejin Park. "An Efficient On-Demand Hardware Replacement Platform for Metamorphic Functional Processing in Edge-Centric IoT Applications". Electronics 10, n.º 17 (28 de agosto de 2021): 2088. http://dx.doi.org/10.3390/electronics10172088.

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The paradigm of Internet-of-things (IoT) systems is changing from a cloud-based system to an edge-based system. These changes were able to solve the delay caused by the rapid concentration of data in the communication network, the delay caused by the lack of server computing capacity, and the security issues that occur in the data communication process. However, edge-based IoT systems performance was insufficient to process large numbers of data due to limited power supply, fixed hardware functions, and limited hardware resources. To improve their performance, application-specific hardware can be installed in edge devices, but performance cannot be improved except for specific applications due to a fixed function of an application-specific hardware. This paper introduces an edge-centric metamorphic IoT (mIoT) platform that can use various hardware modules through on-demand partial reconfiguration, despite the limited hardware resources of edge devices. In addition, this paper introduces an RISC-V based metamorphic IoT processor (mIoTP) with reconfigurable peripheral modules. We experimented to prove that the proposed structure can reduce the server access of edges and can be applied to a large-scale IoT system. Experiments were conducted in a single-edge environment and a large-scale environment combining one physical edge and 99 virtual edges. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration prediction algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to our previous study in which the prediction process was executed at the server. Furthermore, we confirmed that there is no additional reconfiguration time overhead even for the large IoT systems.

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16

Trivisonno, Riccardo, Massimo Condoluci, Xueli An y Toktam Mahmoodi. "mIoT Slice for 5G Systems: Design and Performance Evaluation". Sensors 18, n.º 2 (21 de febrero de 2018): 635. http://dx.doi.org/10.3390/s18020635.

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17

Chang, Yuxia, Chen Fang y Wenzhuo Sun. "A Blockchain-Based Federated Learning Method for Smart Healthcare". Computational Intelligence and Neuroscience 2021 (24 de noviembre de 2021): 1–12. http://dx.doi.org/10.1155/2021/4376418.

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The development of artificial intelligence and worldwide epidemic events has promoted the implementation of smart healthcare while bringing issues of data privacy, malicious attack, and service quality. The Medical Internet of Things (MIoT), along with the technologies of federated learning and blockchain, has become a feasible solution for these issues. In this paper, we present a blockchain-based federated learning method for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and MIoT devices implement the federated learning to make full of the distributed clinical data. In particular, we design an adaptive differential privacy algorithm to protect data privacy and gradient verification-based consensus protocol to detect poisoning attacks. We compare our method with two similar methods on a real-world diabetes dataset. Promising experimental results show that our method can achieve high model accuracy in acceptable running time while also showing good performance in reducing the privacy budget consumption and resisting poisoning attacks.

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18

Gaurav, Akshat, Konstantinos Psannis y Dragan Peraković. "Security of Cloud-Based Medical Internet of Things (MIoTs)". International Journal of Software Science and Computational Intelligence 14, n.º 1 (enero de 2022): 1–16. http://dx.doi.org/10.4018/ijssci.285593.

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In this digital era expectations for medical quality have increased. As the number of patients continues to increase, conventional health care methods are having to deal with new complications. In light of these observations, researchers suggested a hybrid combination of conventional health care methods with IoT technology and develop MIoT. The goal of IoMT is to ensure that patients can respond more effectively and efficiently to their treatment. But preserving user privacy is a critical issue when it comes to collecting and handling highly sensitive personal health data. However, IoMTs have limited processing power; hence, they can only implement minimal security techniques. Consequently, throughout the health data transfer through MIoT, patient’s data is at risk of data leakage. This manuscript per the authors emphasizes the need of implementing suitable security measures to increase the IoMT's resilience to cyberattacks. Additionally, this manuscript per the authors discusses the main security and privacy issues associated with IoMT and provide an overview of existing techniques.

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19

Rekha Sivakumar, Nithya, Sara Abdelwahab Ghorashi, Faten Khalid Karim, Eatedal Alabdulkreem y Amal Al-Rasheed. "MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning". Computers, Materials & Continua 73, n.º 3 (2022): 6253–67. http://dx.doi.org/10.32604/cmc.2022.029266.

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20

Baldassarre, Giorgio, Paolo Lo Giudice, Lorenzo Musarella y Domenico Ursino. "The MIoT paradigm: Main features and an “ad-hoc” crawler". Future Generation Computer Systems 92 (marzo de 2019): 29–42. http://dx.doi.org/10.1016/j.future.2018.09.015.

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21

Giaffreda, Raffaele y Mattia Antonini. "IoT Technologies and Privacy in a Data-Bloated Society: Where Do We Stand in the Fight to Prepare for the Next Pandemic?" IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): 2–3. http://dx.doi.org/10.1109/miot.2020.9319621.

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22

Narang, N. Kishor. "Mentor's Musings on Artificial Computational Intelligence and the Internet of Everything". IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): 4–8. http://dx.doi.org/10.1109/miot.2020.9319622.

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23

Narang, N. Kishor y John K. Zhao. "Mentor's Musings on IoT, Environment and Standards Interplay…". IEEE Internet of Things Magazine 4, n.º 1 (marzo de 2021): 4–8. http://dx.doi.org/10.1109/miot.2021.9390455.

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24

Khabbaz, Maurice. "Message from the Interim Editor-in-Chief". IEEE Internet of Things Magazine 4, n.º 1 (marzo de 2021): 2–3. http://dx.doi.org/10.1109/miot.2021.9390456.

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Vecchio, Massimo. "Guest Editorial: IoT and the Environment". IEEE Internet of Things Magazine 4, n.º 1 (marzo de 2021): 10–11. http://dx.doi.org/10.1109/miot.2021.9390452.

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26

Lv, Zhihan, Jaime Lloret, Houbing Song, Jun Shen y Wojciech Mazurczyk. "Guest Editorial: Secure Communications Over the Internet of Artificially Intelligent Things: Part 2". IEEE Internet of Things Magazine 5, n.º 2 (junio de 2022): 46–49. http://dx.doi.org/10.1109/miot.2022.9889289.

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27

Nguyen, Tu, Linh Le, Vincenzo Piuri, Brij B. Gupta, Lianyong Qi, Shahid Mumtaz y Huang-Chen Lee. "Guest Editorial: Deep Learning Assisted Visual IoT Technologies for Critical Infrastructure Protection". IEEE Internet of Things Magazine 5, n.º 2 (junio de 2022): 10–12. http://dx.doi.org/10.1109/miot.2022.9889269.

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Narang, N. Kishor. "Mentor's Musings on Standardization Imperatives for the Connected Vehicles for Seamless Integration in Sustainable Mobility". IEEE Internet of Things Magazine 5, n.º 2 (junio de 2022): 4–8. http://dx.doi.org/10.1109/miot.2022.9889259.

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Lv, Zhihan, Jaime Lloret, Houbing Song, Jun Shen y Wojciech Mazurczyk. "Guest Editorial: Secure Communications Over the Internet of Artificially Intelligent Things". IEEE Internet of Things Magazine 5, n.º 1 (marzo de 2022): 58–60. http://dx.doi.org/10.1109/miot.2022.9773087.

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Narang, N. Kishor. "Mentor's Musings on Disruptive Technologies and Standards Interplay in Industrial Transformation". IEEE Internet of Things Magazine 5, n.º 1 (marzo de 2022): 4–12. http://dx.doi.org/10.1109/miot.2022.9773142.

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Narang, N. Kishor. "Mentor's Musings on Security Standardization Challenges and Imperatives for Artificial Intelligence of Things". IEEE Internet of Things Magazine 5, n.º 1 (marzo de 2022): 14–21. http://dx.doi.org/10.1109/miot.2022.9773094.

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32

Wouhaybi, Rita, Ravikumar Balakrishnan, Navid NaderiAlizadeh, Ahmad Beirami, Marco Di Felice, Shadi Noghabi, Anirudh Badam y Shugong Xu. "Guest Editorial: An End-to-End Machine Learning Perspective on Industrial IoT". IEEE Internet of Things Magazine 5, n.º 1 (marzo de 2022): 22–23. http://dx.doi.org/10.1109/miot.2022.9773141.

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Vannithamby, Rath. "Updates on IoT Magazine and the Editorial Board". IEEE Internet of Things Magazine 4, n.º 3 (septiembre de 2021): 2. http://dx.doi.org/10.1109/miot.2021.9548992.

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Narang, N. Kishor. "Mentor's Musings on the Role of Disruptive Technologies and Innovation in Making Healthcare Systems More Sustainable". IEEE Internet of Things Magazine 4, n.º 3 (septiembre de 2021): 80–89. http://dx.doi.org/10.1109/miot.2021.9548847.

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35

Gupta, Deepak, Victor Hugo C. de Albuquerque, Sheng-Lung Peng, Ashish Khanna, Nguyen Gia Nhu y Oscar Castillo. "GUEST EDITORIAL: Internet of Things for e-Health Applications". IEEE Internet of Things Magazine 4, n.º 3 (septiembre de 2021): 4–5. http://dx.doi.org/10.1109/miot.2021.9548995.

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Narang, N. Kishor. "Mentor's Musings on Standardization Landscape & Imperatives for the Internet of Drones". IEEE Internet of Things Magazine 4, n.º 4 (diciembre de 2021): 2–7. http://dx.doi.org/10.1109/miot.2021.9712395.

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Chamola, Vinay, F. Richard Yu, Biplab Sikdar, Salil Kanhere y Mohsen Guizani. "Guest Editorial: Internet of Drones: Novel Applications, Recent Deployments, and Integration". IEEE Internet of Things Magazine 4, n.º 4 (diciembre de 2021): 8–10. http://dx.doi.org/10.1109/miot.2021.9712460.

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38

Torkey, Hanaa, Elhossiny Ibrahim, EZZ El-Din Hemdan, Ayman El-Sayed y Marwa A. Shouman. "Diabetes classification application with efficient missing and outliers data handling algorithms". Complex & Intelligent Systems, 17 de abril de 2021. http://dx.doi.org/10.1007/s40747-021-00349-2.

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AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

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Noura, Hassan N., Ola Salman, Raphael Couturier y Ali Chehab. "Efficient and secure selective cipher scheme for MIoT compressed images". Ad Hoc Networks, julio de 2022, 102928. http://dx.doi.org/10.1016/j.adhoc.2022.102928.

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"IEEE Access [advertisement]". IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): C3. http://dx.doi.org/10.1109/miot.2020.9319685.

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"[Front cover]". IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): C1. http://dx.doi.org/10.1109/miot.2020.9319686.

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"Call for papers". IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): 81. http://dx.doi.org/10.1109/miot.2020.9319681.

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"Table of Contents". IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): 1. http://dx.doi.org/10.1109/miot.2020.9319679.

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"Call for papers". IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): 29. http://dx.doi.org/10.1109/miot.2020.9319687.

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"Afterword: Special Thanks for this Special Issue". IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): 102. http://dx.doi.org/10.1109/miot.2020.9319684.

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"What + If = IEEE [Advertisement]". IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): C2. http://dx.doi.org/10.1109/miot.2020.9319680.

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"The 7th IEEE World Forum on the Internet of Things - WFIoT2021 [Call for papers". IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): 9. http://dx.doi.org/10.1109/miot.2020.9319682.

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"IEEE Foundation [advertisement]". IEEE Internet of Things Magazine 3, n.º 4 (diciembre de 2020): C4. http://dx.doi.org/10.1109/miot.2020.9319683.

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"Comsoc Training". IEEE Internet of Things Magazine 4, n.º 1 (marzo de 2021): 39. http://dx.doi.org/10.1109/miot.2021.9390517.

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"[Front cover]". IEEE Internet of Things Magazine 4, n.º 1 (marzo de 2021): C1. http://dx.doi.org/10.1109/miot.2021.9390520.

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