The Internet Of Things
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2022 ◽  
Vol 18 (1) ◽  
pp. 1-51
Author(s):  
Alberto Giaretta ◽  
Nicola Dragoni ◽  
Fabio Massacci

The Internet of Things (IoT) revolutionised the way devices, and human beings, cooperate and interact. The interconnectivity and mobility brought by IoT devices led to extremely variable networks, as well as unpredictable information flows. In turn, security proved to be a serious issue for the IoT, far more serious than it has been in the past for other technologies. We claim that IoT devices need detailed descriptions of their behaviour to achieve secure default configurations, sufficient security configurability, and self-configurability. In this article, we propose S×C4IoT, a framework that addresses these issues by combining two paradigms: Security by Contract (S×C) and Fog computing. First, we summarise the necessary background such as the basic S×C definitions. Then, we describe how devices interact within S×C4IoT and how our framework manages the dynamic evolution that naturally result from IoT devices life-cycles. Furthermore, we show that S×C4IoT can allow legacy S×C-noncompliant devices to participate with an S×C network, we illustrate two different integration approaches, and we show how they fit into S×C4IoT. Last, we implement the framework as a proof-of-concept. We show the feasibility of S×C4IoT and we run different experiments to evaluate its impact in terms of communication and storage space overhead.


Author(s):  
R. Senthil Prabhu ◽  
D. Sabitha Ananthi ◽  
S. Rajasoundarya ◽  
R. Janakan ◽  
R. Priyanka

Technologies that could allow literally billions of everyday objects to communicate with each other over the internet have enormous potential to change all our lives. The Internet of Things (IoT) is a transformative development, these technologies are a way of boosting productivity, keeping us healthier, making transport more efficient, reducing energy needs and making our homes more comfortable. In recent years, Internet of Things (IoT) and Internet of Nanothings (IoNT) have drawn significant research attention in numerous fields such as Healthcare, Defence, Environmental monitoring, Food and water quality control etc., There are various transformations such as Smart cities, Smart homes, Smart factories, Smart transportation, due to IoT and IoNT. Health care delivery requires the support of new technologies like IoT, IoNT to fight and look against the new pandemic diseases. For the past two years COVID-19 spreaded over worldwide including India, are fighting with pandemic disease and still looking for a practical and cost-effective solution to face the problems arising in several ways. To minimize the person to person, contact and to maintain social distancing various technologies are utilized, among them IoT and IoNT play a major role in healthcare system and allied fields. This review mainly discuss about the IoT, IoNT, its components and various applications in healthcare and allied fields.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lingqi Xue

With the advent of the era of big data, Internet of things technology and wireless communication technology have been in a state of rapid development. Opportunities and challenges in all walks of life are being subverted. Financial management, as the foundation of corporate governance, is important for improving economic efficiency and achieving sustainable business development which plays an important role. In order to realize the management and classification of financial big data, better identify the financial data of different enterprises, strengthen the safe storage of financial information, and provide early warning for the security issues involved, this article is based on the Internet of things and wireless communication networks. In the method part, this article introduces the framework of the Internet of things, Bluetooth, and infrared data transmission in wireless network communication and the principles of financial big data. The algorithm introduces a single-user MIMO system, free space propagation, and spectrum and energy efficiency. The analysis part analyzes the spectrum efficiency of different algorithms, social utility, average number of retransmissions, comprehensive scores of competitiveness in various fields of the Internet of things, and the significance of financial indicators. By comparing the data, it can be seen that the algorithm in this paper is superior to the two algorithms of IAN-CoMP and IA-CoMP. When the number of users is 100, the social utility of the algorithm in this paper is 4.45, while IAN-CoMP is 3.43 and IA-CoMP is 3.67. When the number of users increases to 700, the social utility of the algorithm in this paper is 28.34. The other two algorithms are, respectively, 24.45 and 25.99, and we know that the social utility of the algorithm in this paper is the best. Through comprehensive analysis, it is concluded that the financial big data model based on the Internet of things and wireless network communication in this paper can better realize data management and collection, so as to meet the needs of information developers.


Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1246
Author(s):  
Siyoung Lee ◽  
Eun Kwang Lee ◽  
Eunho Lee ◽  
Geun Yeol Bae

With the advent of human–machine interaction and the Internet of Things, wearable and flexible vibration sensors have been developed to detect human voices and surrounding vibrations transmitted to humans. However, previous wearable vibration sensors have limitations in the sensing performance, such as frequency response, linearity of sensitivity, and esthetics. In this study, a transparent and flexible vibration sensor was developed by incorporating organic/inorganic hybrid materials into ultrathin membranes. The sensor exhibited a linear and high sensitivity (20 mV/g) and a flat frequency response (80–3000 Hz), which are attributed to the wheel-shaped capacitive diaphragm structure fabricated by exploiting the high processability and low stiffness of the organic material SU-8 and the high conductivity of the inorganic material ITO. The sensor also has sufficient esthetics as a wearable device because of the high transparency of SU-8 and ITO. In addition, the temperature of the post-annealing process after ITO sputtering was optimized for the high transparency and conductivity. The fabricated sensor showed significant potential for use in transparent healthcare devices to monitor the vibrations transmitted from hand-held vibration tools and in a skin-attachable vocal sensor.


Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6636
Author(s):  
Fouad Sakr ◽  
Riccardo Berta ◽  
Joseph Doyle ◽  
Alessandro De De Gloria ◽  
Francesco Bellotti

The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and challenging to annotate. This paper introduces the self-learning autonomous edge learning and inferencing pipeline (AEP), deployable in a resource-constrained embedded system, which can be used for unsupervised local training and classification. AEP uses two complementary approaches: pseudo-label generation with a confidence measure using k-means clustering and periodic training of one of the supported classifiers, namely decision tree (DT) and k-nearest neighbor (k-NN), exploiting the pseudo-labels. We tested the proposed system on two IoT datasets. The AEP, running on the STM NUCLEO-H743ZI2 microcontroller, achieves comparable accuracy levels as same-type models trained on actual labels. The paper makes an in-depth performance analysis of the system, particularly addressing the limited memory footprint of embedded devices and the need to support remote training robustness.


Author(s):  
Haythem Hayouni

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jianan Yu

Ubiquitous sensors cover many areas of modern society. As the sensor network matures, various applications based on the Internet of Things are setting off a new revolution in all aspects of social life. In order to in-depth study whether the Internet of Things technology can be used in the automatic evaluation of piano performance, this article uses artificial system comparison method, database establishment method, and model construction method to collect samples, analyze the automatic evaluation model, and streamline the algorithm, and based on these foundations, a practical automatic evaluation system for piano performance was created. However, the role of this article does not stop there. There are also a variety of algorithm-like models and the construction of technical models. First, the practicality of the created model is studied, and it is found that the traditional manual recognition rate is about 52%, while the recognition rate of the system in this paper is more than 90%, and the average recognition time of the system is 1.1 s. The start-up process and recognition process time of other systems are all no more than 6 s, indicating the superior performance of the system. On this basis, select the classic piano textbook: Thompson’s Simple Piano Tutorial. From it, select representative pieces as test samples. We can find that the overall F-measure value is above 90%, and the average F-measure value is 96.8%; the system performance test is good and can provide accurate evaluation results for piano learners. The results show that the number of identifications and missing numbers of the system are not much different from those of manual identification, which further proves its superiority. It is basically realized that starting from the Internet of Things technology, a system model that can automatically evaluate most piano repertoires has been designed.


Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6841
Author(s):  
Sergio Cofre-Martel ◽  
Enrique Lopez Droguett ◽  
Mohammad Modarres

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Andrew Ebekozien ◽  
Marvelous Aigbedion ◽  
Okechukwu Saviour Dominic Duru ◽  
Oguike Hyginus Udeagwu ◽  
Ibeabuchi Lawrence Aginah

Purpose Studies have proved that wood sawmill workers are exposed to high occupational risks if not well managed. In developing countries, many wood sawmills are found in urban and semi-urban areas. Studies exploring how residents near these wood sawmills perceive and react to these risks is scarce in Nigeria. The application of the fourth industrial revolution (4IR) technology is possibly one of the ways to manage the likely hazards. This study aims to investigate the possible hazards associated with timber sawmills in residential areas and the role of 4IR technologies in proffering feasible solutions to mitigate them in Nigeria’s cities. Design/methodology/approach Data were sourced from three cities and nine sawmills across Nigeria. Face-to-face interviews were conducted with authoritative participants (residents, environmentalists, government agencies, sawmill owners, 4IR technology experts and medical experts) who have been championing the regulation and safety of timber sawmill locations within the cities (Lagos, Benin City and Owerri) via a phenomenology type of qualitative research and supplemented by secondary sources. Findings Findings show that timber sawmills are located across the three cities in Nigeria and may have contributed to the health and environmental challenges of the people living in the neighbourhood. The identified hazards were grouped into three sub-themes (physical, health and environmental hazards). Findings identify robots, modularisation, cyber-physical systems, the internet of things and services and human-computer interaction as the digitalised technology that can be used in sawmills to mitigate hazards for the benefit of mankind. Research limitations/implications The paper is limited to hazards that residents in timber sawmills locations may face in Nigeria’s cities and data collected via face-to-face 23 interviews. The paper’s referral to past publications in the findings and discussion section compensated for the small sample size. Practical implications As part of this paper’s implications, the emerged recommendations will strengthen collaboration with relevant stakeholders regarding control measures via the use of 4IR technologies in timber sawmills. This will stir up policymakers to develop possible policies that will promote and create the platform for the implementation of 4IR technologies in city sawmills. Originality/value Apart from probably being the first paper to explore the hazards of residents in timber sawmill locations and proffer solutions via the usage of the 4IR technology, this paper’s contribution emphasis the need for in-depth future studies regarding the risk perceptions of Nigeria’s residents living in timber sawmill area.


2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Haidong Sun ◽  
Zhengtao Zhang ◽  
Peng Li

The continuous development of information technology and various electronic devices has accelerated the process of informatization and digitization, enabling the development and application of the emerging technology of wireless communication and the Internet of Things. Since the continuous occurrence of vicious bridge collapse accidents in China in recent years, the problem of bridge inspection has become a hot topic among the people. At the same time, how to apply wireless communication and the Internet of Things technology to bridge inspection systems has also become a new research topic. This article mainly studies the design and analysis of bridge detection systems based on wireless communication and Internet of Things technology. In order to expand the field of bridge detection and standard management and improve the credibility and reliability of safety problem prediction and evaluation, the bridge detection system will integrate IoT sensing, internet, remote communication, digital signal analysis and processing, big data knowledge mining, big data prediction and other technologies, design and analysis of the main structure of roads and bridges, and other multifaceted knowledge fields and build a professional intelligent digital network based on bridge inspection data collection, monitoring, analysis, evaluation, and early warning. From design to use and maintenance of the bridge, a digital neural network spanning time and space throughout the life cycle is constructed to construct a digital brain with bridge sensing points as neurons. This paper uses high-power infrared sensor equipment, satellite positioning systems, sensor equipment, and other technical equipment to achieve the purpose of data communication and exchange and realize intelligent positioning, identification, supervision, tracking, and other functions, making the wireless communication and Internet of Things reliable transmission, comprehensive perception, intelligent processing, and other capabilities very effective in the field of bridge inspection. Through the research and analysis of this article, there are more and more bridge inspection systems developed by the Internet of Things and wireless communication technology in China, and the percentage of related equipment used can reach more than 90%. The functions of the bridge inspection system are becoming more and more complete, and the results of the inspection data are also increasing.


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