Emerging Wireless Sensor Networks and Internet of Things Technologies-Foundations of Smart Healthcare

Development and Application of IoT Monitoring Systems for Typical Large Amusement Facilities

The advent of internet of things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues for real-time surveillance and assessment of the operational conditions of intricate mechanical systems. Nowadays, mechanical system monitoring technologies are extensively utilized in various sectors, such as rotating and reciprocating machinery, expansive bridges, and intricate aircraft. Nevertheless, in comparison to standard mechanical frameworks, large amusement facilities, which constitute the primary manned electromechanical installations in amusement parks and scenic locales, showcase a myriad of structural designs and multiple failure patterns. The predominant method for fault diagnosis still relies on offline manual evaluations and intermittent testing of vital elements. This practice heavily depends on the inspectors' expertise and proficiency for effective detection. Moreover, periodic inspections cannot provide immediate feedback on the safety status of crucial components, they lack preemptive warnings for potential malfunctions, and fail to elevate safety measures during equipment operation. Hence, developing an equipment monitoring system grounded in IoT technology and sensor networks is paramount, especially considering the structural nuances and risk profiles of large amusement facilities. This study aims to develop customized operational status monitoring sensors and an IoT platform for large roller coasters, encompassing the design and fabrication of sensors and IoT platforms and data acquisition and processing. The ultimate objective is to enable timely warnings when monitoring signals deviate from normal ranges or violate relevant standards, thereby facilitating the prompt identification of potential safety hazards and equipment faults.

Securing the future of IoT-healthcare systems: A meta-synthesis of mandatory security requirements

INTRODUCTION

Healthcare-based Internet of Things (Healthcare-IoT) is a turning point in the development of health information systems. This emerging trend significantly contributes to enhancing users' awareness of their health, ultimately leading to an extension in life expectancy. Security and privacy are among the greatest challenges for H-IoT systems. To establish complete safety and security in these systems, the implementation of mandatory security requirements is imperative. For this reason, this study identifies the necessary security requirements for H-IoT systems using a Meta-Synthesis approach.

METHODS

Initially, following the Seven-Stage Sandelowski & Barroso approach, the existing literature was searched in the Scopus and Web of Science databases. Among the 844 extracted articles from the period of 2010 to 2020, 78 final articles were reviewed and analyzed, leading to the identification of 51 security requirements. Subsequently, to assess the quality of the identified requirements and their overlap, interviews were conducted with two experts.

RESULTS

Finally, 14 security requirements, predominantly with technical and quantitative aspects, were identified for designing a Healthcare-IoT system and implementing security mechanisms.

CONCLUSION

The findings of this study emphasize that addressing the identified 14 security requirements is crucial for safeguarding Healthcare-IoT systems and ensuring their robustness in the evolving health information landscape.

Inkjet-printed high-performance and mechanically flexible organic photodiodes for optical wireless communication

Emerging areas such as the Internet of Things (IoT), wearable and wireless sensor networks require the implementation of optoelectronic devices that are cost-efficient, high-performing and capable of conforming to different surfaces. Organic semiconductors and their deposition via digital printing techniques have opened up new possibilities for optical devices that are particularly suitable for these innovative fields of application. In this work, we present the fabrication and characterization of high-performance organic photodiodes (OPDs) and their use as an optical receiver in an indoor visible light communication (VLC) system. We investigate and compare different device architectures including spin-coated, partially-printed, and fully-printed OPDs. The presented devices exhibited state-of-the-art performance and reached faster detection speeds than any other OPD previously reported as organic receivers in VLC systems. Finally, our results demonstrate that the high-performance of the fabricated OPDs can be maintained in the VLC system even after the fabrication method is transferred to a fully-inkjet-printed process deposited on a mechanically flexible substrate. A comparison between rigid and flexible samples shows absolute differences of only 0.2 b s-1 Hz-1 and 2.9 Mb s-1 for the spectral efficiency and the data rate, respectively.

A Review of Cognitive Hybrid Radio Frequency/Visible Light Communication Systems for Wireless Sensor Networks

The development and growth of Wireless Sensor Networks (WSNs) is significantly propelled by advances in Radio Frequency (RF) and Visible Light Communication (VLC) technologies. This paper endeavors to present a comprehensive review of the state-of-the-art in cognitive hybrid RF-VLC systems for WSNs, emphasizing the critical task of seamlessly integrating Cognitive Radio Sensor Networks (CRSNs) and VLC technologies. The central challenge addressed is the intricate landscape of this integration, characterized by notable trade-offs between performance and complexity, which escalate with the addition of more devices and increased data rates. This scenario necessitates the development of advanced cognitive radio strategies, potentially facilitated by Machine Learning (ML) and Deep Learning (DL) approaches, albeit introducing new complexities such as the necessity for pre-training with extensive datasets. The review scrutinizes the fundamental aspects of CRSNs and VLC, spotlighting key areas like Energy Efficient Resource Allocation, Industrial Scenarios, and Energy Harvesting, and explores the synergistic amalgamation of these technologies as a promising pathway for enhanced spectrum utilization and network performance. By delving into the integration of cognitive radio technology with visible light, this study furnishes valuable insights into the potential for innovative applications in wireless communication, presenting a balanced overview of the current advancements and prospective avenues in the field of cognitive hybrid RF/VLC systems.

The Influence of Diet on Tinnitus Severity: Results of a Large-Scale, Online Survey

Optimization of dietary intake is an essential component in the multidimensional effort to prevent and manage chronic disease. Recently, demand has increased for nutrition-focused management strategies for chronic tinnitus. The primary aim of this study was to evaluate 10 dietary items for their association with changes in subjective tinnitus severity. A secondary aim was to develop an algorithm to better identify those individuals who might benefit from dietary modification strategies. A total of 5017 anonymous users of the TinnitusTalk forum completed an online survey regarding how various dietary items affected the severity of their tinnitus. Results suggest that, while intake of caffeine [positive effect (PE): 0.4%; negative effect (NE): 16.2%], alcohol (PE: 2.7%; NE: 13.3%, and salt (PE: 0.1%; NE: 9.9%) was most likely to influence tinnitus severity, it did so only for a small proportion of participants and reported effects were most commonly mild. Further, though a classification algorithm was able to leverage participant demographic, comorbidity, and tinnitus characteristics to identify those individuals most likely to benefit from dietary modification above chance levels, further efforts are required to achieve significant clinical utility. Taken together, these results do not support dietary modification as a primary treatment strategy for chronic tinnitus in the general population, though clinically meaningful effects might be observable in certain individuals.

Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review

During the COVID-19 pandemic, the patient care delivery paradigm rapidly shifted to remote technological solutions. Rising rates of life expectancy of older people, and deaths due to chronic diseases (CDs) such as cancer, diabetes and respiratory disease pose many challenges to healthcare. While the feasibility of Remote Patient Monitoring (RPM) with a Smart Healthcare Monitoring (SHM) framework was somewhat questionable before the COVID-19 pandemic, it is now a proven commodity and is on its way to becoming ubiquitous. More health organizations are adopting RPM to enable CD management in the absence of individual monitoring. The current studies on SHM have reviewed the applications of IoT and/or Machine Learning (ML) in the domain, their architecture, security, privacy and other network related issues. However, no study has analyzed the AI and ubiquitous computing advances in SHM frameworks. The objective of this research is to identify and map key technical concepts in the SHM framework. In this context an interesting and meaningful classification of the research articles surveyed for this work is presented. The comprehensive and systematic review is based on the "Preferred Reporting Items for Systematic Review and Meta-Analysis" (PRISMA) approach. A total of 2540 papers were screened from leading research archives from 2016 to March 2021, and finally, 50 articles were selected for review. The major advantages, developments, distinctive architectural structure, components, technical challenges and possibilities in SHM are briefly discussed. A review of various recent cloud and fog computing based architectures, major ML implementation challenges, prospects and future trends is also presented. The survey primarily encourages the data driven predictive analytics aspects of healthcare and the development of ML models for health empowerment.

Inter-Multilevel Super-Orthogonal Space-Time Coding Scheme for Reliable ZigBee-Based IoMT Communications

The Internet of Things (IoT) technology has revolutionized the healthcare industry by enabling a new paradigm for healthcare delivery. This paradigm is known as the Internet of Medical Things (IoMT). IoMT devices are typically connected via a wide range of wireless communication technologies, such as Bluetooth, radio-frequency identification (RFID), ZigBee, Wi-Fi, and cellular networks. The ZigBee protocol is considered to be an ideal protocol for IoMT communication due to its low cost, low power usage, easy implementation, and appropriate level of security. However, maintaining ZigBee's high reliability is a major challenge due to multi-path fading and interference from coexisting wireless networks. This has increased the demand for more efficient channel coding schemes that can achieve a more reliable transmission of vital patient data for ZigBee-based IoMT communications. To meet this demand, a novel coding scheme called inter-multilevel super-orthogonal space-time coding (IM-SOSTC) can be implemented by combining the multilevel coding and set partitioning of super-orthogonal space-time block codes based on the coding gain distance (CGD) criterion. The proposed IM-SOSTC utilizes a technique that provides inter-level dependency between adjacent multilevel coded blocks to facilitate high spectral efficiency, which has been compromised previously by the high coding gain due to the multilevel outer code. In this paper, the performance of IM-SOSTC is compared to other related schemes via a computer simulation that utilizes the quasi-static Rayleigh fading channel. The simulation results show that IM-SOSTC outperforms other related coding schemes and is capable of providing the optimal trade-off between coding gain and spectral efficiency whilst guaranteeing full diversity and low complexity.

Breaking Barriers in Emerging Biomedical Applications

The recent global COVID-19 pandemic has revealed that the current healthcare system in modern society can hardly cope with the increased number of patients. Part of the load can be alleviated by incorporating smart healthcare infrastructure in the current system to enable patient's remote monitoring and personalized treatment. Technological advances in communications and sensing devices have enabled the development of new, portable, and more power-efficient biomedical sensors, as well as innovative healthcare applications. Nevertheless, such applications require reliable, resilient, and secure networks. This paper aims to identify the communication requirements for mass deployment of such smart healthcare sensors by providing the overview of underlying Internet of Things (IoT) technologies. Moreover, it highlights the importance of information theory in understanding the limits and barriers in this emerging field. With this motivation, the paper indicates how data compression and entropy used in security algorithms may pave the way towards mass deployment of such IoT healthcare devices. Future medical practices and paradigms are also discussed.

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.

Internet of things in health: Requirements, issues, and gaps

BACKGROUND AND OBJECTIVES

The Internet of Things (IoT) paradigm has been extensively applied to several sectors in the last years, ranging from industry to smart cities. In the health domain, IoT makes possible new scenarios of healthcare delivery as well as collecting and processing health data in real time from sensors in order to make informed decisions. However, this domain is complex and presents several technological challenges. Despite the extensive literature about this topic, the application of IoT in healthcare scarcely covers requirements of this sector.

METHODS

A literature review from January 2010 to February 2021 was performed resulting in 12,108 articles. After filtering by title, abstract, and content, 86 were eligible and examined according to three requirement themes: data lifecycle; trust, security, and privacy; and human-related issues.

RESULTS

The analysis of the reviewed literature shows that most approaches consider IoT application in healthcare merely as in any other domain (industry, smart cities…), with no regard of the specific requirements of this domain.

CONCLUSIONS

Future efforts in this matter should be aligned with the specific requirements and needs of the health domain, so that exploiting the capabilities of the IoT paradigm may represent a meaningful step forward in the application of this technology in healthcare.

Internet of things-based smart wearable system to monitor sports person health

BACKGROUND

The modern Internet of Things (IoT) makes small devices that can sense, process, interact, connect devices, and other sensors ready to understand the environment. IoT technologies and intelligent health apps have multiplied. The main challenges in the sports environment are playing without injuries and healthily.

OBJECTIVE

In this paper the Internet of Things-based Smart Wearable System (IoT-SWS) is introduced for monitoring sports person activity to improve sports person health and performance in a healthy way.

METHOD

Wearable systems are commonly used to capture individual sports details on a real-time basis. Collecting data from wearable devices and IoT technologies can help organizations learn how to optimize in-game strategies, identify opponents' vulnerabilities, and make smarter draft choices and trading decisions for a sportsperson.

RESULTS

The experimental result shows that IoT-SWS achieve the highest accuracy of 98.22% and efficient in predicting the sports person's health to improve sports person performance reliably.

Cross-SN: A Lightweight Authentication Scheme for a Multi-Server Platform Using IoT-Based Wireless Medical Sensor Network

Several wireless devices and applications can be connected through wireless communication technologies to exchange data in future intelligent health systems (e.g., the Internet of Medical Things (IoMT)). Smart healthcare requires ample bandwidth, reliable and effective communications networks, energy-efficient operations, and quality of service support (QoS). Healthcare service providers host multi-servers to ensure seamless services are provided to the end-users. By supporting a multi-server environment, healthcare medical sensors produce many data transmitted via servers, which is impossible in a single-server architecture. To ensure data security, secure online communication must be considered since the transmitted data are sensitive. Hence, the adversary may try to interrupt the transmission and drop or modify the message. Many researchers have proposed an authentication scheme to secure the data, but the schemes are vulnerable to specific attacks (modification attacks, replay attacks, server spoofing attacks, Man-in-the middle (MiTM) attacks, etc.). However, the absence of an authentication scheme that supports a multi-server security in such a comprehensive development in a distributed server is still an issue. In this paper, a secure authentication scheme using wireless medical sensor networks for a multi-server environment is proposed (Cross-SN). The scheme is implemented with a smart card, password, and user identity. Elliptic curve cryptography is utilized in the scheme, and Burrows–Abadi–Needham (BAN) logic is utilized to secure mutual authentication and to analyse the proposed scheme’s security. It offers adequate protection against replies, impersonation, and privileged insider attacks and secure communication in multi-server parties that communicate with each other.

Wireless technologies, medical applications and future challenges in WBAN: a survey

Interest and need for Wireless Body Area Networks (WBANs) have significantly increased recently. WBAN consists of miniaturized sensors designed to collect and transmit data through wireless network, enabling medical specialists to monitor patients during their normal daily life and providing real time opinions for medical diagnosis. Many wireless technologies have proved themselves in WBAN applications, while others are still under investigations. The choice of the technology to adopt may depend on the disease to monitor and the performance requirements, i.e. reliability, latency and data rate. In addition, the suitable sensor is essential when seeking to extract the data related to a medical measure. This paper aims at surveying the wireless technologies used in WBAN systems. In addition to a detailed survey on the existing technologies, the use of the emerging Low Power Wide Area Network (LPWAN) technologies, and the future 5G, B5G and 6G is investigated, where the suitability of these technologies to WBAN applications is studied from several perspectives. Furthermore, medical applications of WBAN are discussed by presenting their methodologies, the adopted wireless technologies and the used sensors. Given that each medical application requires the appropriate sensor to extract the data, we highlight a wide range of the sensors used in the market for medical systems. Recent and future challenges in WBAN systems are given related to the power consumption, the emergence of the Internet of Things (IoT) technologies in WBAN and others.

Hybridized Nanogenerators for Multifunctional Self-Powered Sensing: Principles, Prototypes, and Perspectives

Sensors are a key component of the Internet of Things (IoTs) to collect information of environments or objects. Considering the tremendous number and complex working conditions of sensors, multifunction and self-powered feathers are two basic requirements. Nanogenerators are a kind of devices based on the triboelectric, piezoelectric, or pyroelectric effects to harvest ambient energy and then converting to electricity. The hybridized nanogenerators that combined multiple effects in one device have great potential in multifunctional self-powered sensors because of the unique superiority such as generating electrical signals directly, responding to diverse stimuli, etc. This review aims at introducing the latest advancements of hybridized nanogenerators for multifunctional self-powered sensing. Firstly, the principles and sensor prototypes based on TENG are summarized. To avoid signal interference and energy insufficiently, the multifunctional self-powered sensors based on hybridized nanogenerators are reviewed. At last, the challenges and future development of multifunctional self-powered sensors have prospected.