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Transforming healthcare through advanced sensing technologies
Abstract
Real-time monitoring, data-driven decision-making and improved patient care are all made possible by the integration of sensors and nanosensor networks in smart hospitals which is transforming the healthcare industry. Smart hospitals use cutting-edge technology to improve patient care, maximize operational effectiveness and support data-driven decision-making. They represent a paradigm change in the delivery of healthcare. Sensors are at the vanguard of this revolution which acting as the pivotal point in the assimilation of data-centric methods for healthcare administration. The revolutionary age in healthcare has begun with the development of sensors and nanosensor networks especially in the context of smart hospitals. It captures the essence of the various applications, difficulties and potential directions that using advanced sensing technology in the healthcare industry may take. To provide readers a thorough grasp of how sensors and nanosensor networks will affect healthcare in the future, the chapter synthesizes data and evaluates relevant literature. This chapter also examines the many uses, difficulties and potential uses of sensor technology in the context of smart hospitals.
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Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In a Wireless Body Area Network (WBAN), for example, patients’ health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity, rendering the data transmission slower. Cognitive radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors, which renders the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.
According to WHO and other key statistics, cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. By identifying cancer early, it is more likely to respond to treatment, resulting in a better chance of survival as well as a lower cost of treatment. Early detection of cancer and avoiding delays in treatment can significantly improve the lives of cancer patients. Nanomaterial-based sensors with the ability to extract and identify tumor-specific biomarkers, circulating tumor cells, or extracellular vesicles secreted by the tumor have the potential to diagnose cancer considerably earlier and enhance patient long-term survival. In this review, we provide the application of nanosensors for the early detection of cancer and the recent techniques for clinically diagnosing cancer patients' biomarker levels. Furthermore, the presented works are compared based on their sensitivity and selectivity in the detection of different kinds of cancer biomarkers.
The Internet of Things (IoT) uses wireless networks without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental factors. There are a variety of WSN uses, and some well-known application factors include energy consumption and lifespan duration for routing purposes. The sensors have detecting, processing, and communication capabilities. In this paper, an intelligent healthcare system is proposed which consists of nano sensors that collect real-time health status and transfer it to the doctor’s server. Time consumption and various attacks are major concerns, and some existing techniques contain stumbling blocks. Therefore, in this research, a genetic-based encryption method is advocated to protect data transmitted over a wireless channel using sensors to avoid an uncomfortable data transmission environment. An authentication procedure is also proposed for legitimate users to access the data channel. Results show that the proposed algorithm is lightweight and energy efficient, and time consumption is 90% lower with a higher security ratio.
An assimilated navigation model based on advanced optical systems involves the integration of optical sensors and processing algorithms to provide accurate and reliable navigation information. These sensors gather information about the surrounding environment, such as the location of landmarks, the position of other objects, and the topography of the terrain. The processing algorithms would use this information to create a detailed model of the environment and the vehicle's position within it. This model could be updated in real-time as the vehicle moves and new information is gathered. The proposed IoT and AI enabled navigation model can provide several benefits, including increased accuracy and reliability compared to traditional GPS-based systems, the ability to operate in areas where GPS signals are weak or unavailable, and the potential for greater autonomy and automation in navigation tasks. The ability of integrated navigation systems can be enhanced with the aid of advanced optical systems (AOS), AI and IoT. Therefore, this research is proposing an assimilated navigation system based on AOS, AI and IoT. Assimilated navigation models based on AOS and smart technologies can provide high-precision navigation solutions for various applications such as aviation, maritime and land-based transportation systems. Firstly, the output signal model of the navigation system based on AOS is designed. Then the reliability parameter estimation and target location tracking of the integrated navigation of objects are designed by using the entropy weight characteristic analysis method. The integrated development of reliability parameter estimation and target location tracking system of navigation system is developed innovatively by using machine intelligence techniques. The simulation test analysis shows the superior performance of the proposed method in improving the intelligent operation and maintenance control ability of the navigation output signal with the aid of advanced optical technologies. By integrating components such as optical sensors, machine intelligence based mechanisms, and GPS backup, the proposed AOS based framework for smart navigation system provides accurate, reliable, and user-friendly navigation solutions for various applications.
Recent improvements in the Internet of Things (IoT) have allowed healthcare to evolve rapidly. This article summarizes previous studies on IoT applications in healthcare. A comprehensive review and bibliometric analysis was performed to objectively summarize the growth of IoT research in healthcare. To begin, 2,990 journal articles were carefully selected for further investigation. These publications were analyzed based on various bibliometric metrics, including publication year, journals, authors, institutions, and countries. Keyword co-occurrence and co-citation networks were generated to unravel significant research hotspots. The findings show that IoT research has received considerable interest from the healthcare community. Based on the results of the keyword co-occurrence network, IoT healthcare applications, blockchain applications, artificial intelligence (AI) techniques, 5G telecommunications, as well as data analytics and computing technologies emerged as important topics. The co-citation network analysis reveals other important themes, including authentication schemes, fog computing, cloud-IoT integration, and cognitive smart healthcare. Overall, the review offers scholars an improved understanding of the current status of IoT research in healthcare and identifies knowledge gaps for future research. This review also informs healthcare professionals about the latest developments and applications of IoT in the healthcare sector.
Malfunction of human liver happens due to non-alcoholic fatty liver. Fatty liver measurement is used for grading hepatic steatosis, fibrosis and cirrho-sis. The various imaging techniques for measuring fatty liver are Magnetic Reso-nance Imaging, Ultrasound and Computed Tomography. Imaging modalities lead to the exposure of harmful radiation of electromagnetic waves because of frequent measurement. The continuous monitoring of fatty liver is never achieved through imaging techniques. In this paper, the human fatty liver measured through a Fatty Liver Sensor (FLS). The continuous monitoring of the fatty liver is achieved through the FLS. FLS is fabricated through the screen-printing with materials such as graphene and polyacrylic. The fatty liver sensor is placed around the liver surface for continuous measuring fatty liver. The signal acquired from the fatty liver sensor is processed using blind source separation, a filtering technique removes the random noise from the acquired signal. The denoised signal is processed with tunable Q wavelet transform (TQWT), of FLS based fatty liver measurement fatty liver signals. The continuous fatty liver volume measured and analysis are performed through Long-short term memory and internet of medical things (IoMT). The experimental results are validated with ultrasound lab values.
Cardiovascular events occurring in the bloodstream are responsible for about 40% of human deaths in developed countries. Motivated by this fact, we present a new global network architecture for a system for the diagnosis and treatment of cardiovascular events, focusing on problems related to pulmonary artery occlusion, i.e., situations of artery blockage by a blood clot. The proposed system is based on bio-sensors for detection of artery blockage and bio-actuators for releasing appropriate medicines, both types of devices being implanted in pulmonary arteries. The system can be used by a person leading an active life and provides bidirectional communication with medical personnel via nano-nodes circulating in the bloodstream constituting an in-body area network. We derive an analytical model for calculating the required number of nano-nodes to detect artery blockage and the probability of activating a bio-actuator. We also analyze the performance of the body area component of the system in terms of path loss and of wireless links budget. Results show that the system can diagnose a blocked artery in about 3 hours and that after another 3 hours medicines can be released in the exact spot of the artery occlusion, while with current medical practices the average time for diagnosis varies between 5 to 9 days.
Internet of Things (IoT) based wearable healthcare data monitoring is an emerging application of smart medicines. IoT-based communication and information exchange methods make it adaptable for the recent sixth-generation computing systems. The terahertz and large-scale processing of the 6th generation communication and computation technology is assimilated in the wearable sensor data exchange process. In this article, a fault-tolerant data processing method (FTDPM) is proposed to handle uneven sensor data. The non-uniform and unsynchronised observation interval-based sensor data is analyzed for its impact on healthcare recommendations. The reliability in the recommendation ensures the precise need for sensor data processing, mitigating the faults. In this reliability estimation process, a support vector machine classifier is used. This learning classifier differentiates the uniform and non-uniform sensor data traffic for providing reliable recommendations. The data from the uniform process is replicated in the non-uniform sequence for recommendation filling. This is carried out based on marginal classification and near-to-reliable data as classified using SVM. The IoT wearable alliance is exploited using the 6G communication paradigms in handling monitored data. The proposed method's performance is verified using the metrics processing time, recommendation failure, processing complexity, and recommendation response time.
With the development of 5G and Internet of Things (IoT), the era of big data‐driven product design is booming. In addition, artificial intelligence (AI) is also emerging and evolving by recent breakthroughs in computing power and software architectures. In this regard, the digital twin, analyzing various sensor data with the help of AI algorithms, has become a cutting‐edge technology that connects the physical and virtual worlds, in which the various sensors are highly desirable to collect environmental information. However, although existing sensor technologies, including cameras, microphones, inertial measurement units, etc., are widely used as sensing elements for various applications, high‐power consumption and battery replacement of them is still a problem. Triboelectric nanogenerators (TENGs) as self‐powered sensors supply a feasible platform for realizing self‐sustainable and low‐power systems. Herein, the recent progress on TENG‐based intelligent systems, that is, wearable electronics, robot‐related systems, and smart homes, followed by prospective future development enabled by sensor fusion technology, is focused on. Finally, how to apply artificial intelligence to the design of intelligent sensor systems for the 5G and IoT era is discussed. With the development of Internet of Things (IoT) and artificial intelligence (AI), the era of big data‐driven product design is booming. Digital twin, analyzing various sensor data with the help of AI, has become a cutting‐edge technology. Recent progress on triboelectric nanogenerator (TENG) provides a new possibility for realizing self‐sustainable systems integrated with digital twin.
The term IoT (Internet of Things) constitutes the quickly developing advanced gadgets with highest computing power with in a constrained VLSI design space [...]
The introduction of sensor technology in our daily lives has brought comfort, convenience, and improved health over the past few decades. Technological advances further expanded the use of medical sensors by reducing their size and costs. Medical sensors improve the intelligence and capabilities of healthcare services including, remote health monitoring, surgical procedures, therapy, and rehabilitation. We present a comprehensive review of medical sensors in the last 50 years focusing on their deployment in healthcare applications. The review also discusses the role of Internet of Things (IoT) technology in enhancing the capabilities of sensor technologies for the healthcare domain. Moreover, we also investigate the benefits and challenges of various integrated architectures which have been proposed recently to seamlessly integrate heterogeneous medical sensors with emerging technologies and paradigms that include edge, Mobile Cloud Computing (MCC), fog, and cloud computing technologies. Finally, we identify future challenges that must be addressed to achieve the maximum potential and benefits of medical sensor technologies and ultimately provide robust, scalable, reliable, and cost-effective healthcare delivery.
The adoption of non-invasive smart implants is inevitable due to recent technological advancements in smart implants and the increasing demand to provide pervasive and personalized care. The integration of non-invasive smart implants presents unprecedented opportunities for effective disease prevention, real-time health data collection, early detection of diseases, real-time monitoring of chronic diseases, virtual patient care, patient-tailored treatment, and minimally invasive management of diseases. Even though the research work in this area is nascent, this study presents the potential benefits and use of non-invasive smart implants in healthcare while reflecting on the potential challenges and limitations of their utilization. With current technological advancements, the adoption of non-invasive smart implants is regaining momentum in managing chronic conditions and diseases such as cancer, cardiovascular diseases, cognitive impairment; orthopedic surgery, dental surgery; and managing and remotely monitoring infectious diseases such as the novel coronavirus disease 2019 (COVID-19). However, the full adoption and utilization of non-invasive smart implants still encounter barriers such as lack of policies and frameworks regulating their use, limited memory space, health consequences and implants' failure, clinical challenges, health hazards imposed by non-invasive smart implants, health data security, and privacy risks. Therefore, there is a need for robust security and privacy measures as well as the formulation of policies guiding the development and use of non-invasive smart implants. With the gained experience from smart implants, the next generation of non-invasive smart implants may include sophisticated modern computational techniques that can analyze health data and suggest adequate therapeutic actions.
Emerging digital technologies continues to evolve posing unprecedented opportunities in health systems globally to improve healthcare services delivery. There has been significant progress in healthcare. However, the lack of emotive recognition coupled with a dearth of personalized and pervasive health applications and emotive smart devices calls for the integration of intelligent sensors health systems through emerging technologies. Although there has been significant progress in smart and connected health care, more research innovation, dissemination and technologies are needed to unbundle new opportunities and move towards healthcare 5.0. Healthcare is at the dawn of a paradigm change to reach the new era of smart disease control and detection, virtual care, smart health management, smart monitoring, and decision-making. Therefore, this study discusses the roles and capacities of sensors, their capabilities and other emerging technologies such as nanotechnology, 5G technologies, drone technology, blockchain, robotics, big data, internet of things, artificial intelligence, and cloud computing. Healthcare 5.0 provides healthcare services including patient remote monitoring, tracking and virtual clinics, emotive telemedicine, ambient assisted living, smart self-management, wellness monitoring and control, smart treatment reminders, compliance and adherence, and personalized and connected health care. However, building resilience and robust healthcare 5.0 is not immune to challenges. Organizational challenges, technological and infrastructural barriers, lack of legal and regulatory frameworks and e-health policies, individual perceptions, misalignment with hospitals’ strategy, lack of funding, religious and cultural barriers are identified as potential barriers to the successful implementation of healthcare 5.0. Therefore, there is a need for building resilient technology-driven healthcare systems. To achieve this, there is a need for expanding technological infrastructure, provision of budgetary support based on sustainable business models, develop appropriate legal and e-health policies, standardization and synchronization of protocols, improving stakeholders’ engagement and involvement and establishment of private and public partnerships and investments.
Wireless Body Area Network is one of the applications of Wireless Sensor Network, for monitoring the health of the patient. Sensor nodes that are used in WBAN are of various types such as ECG sensor, BP sensor, Glucose sensor, Temperature sensor, Pressure sensor, Motion sensor, etc., depends upon the health condition of the patient prescribed by the Doctors/Physician. These nodes can create a connection to the medical server where the physician can access those data and supplied real-time services to the patient via SMS/E-mail. In WBANs, sensor nodes are either implanted inside or outside the body of the patient that senses various physiological actions in the body to get consistent and accurate data.
In this paper, various aspects related to WBAN are discussed, like the quality of the sensor node, the effect of electromagnetic radiation on the human body released by the sensor node, energy consumption, and size of the sensor node that can be easily adjustable inside the human body. Energy consumption is the main issue in WBANs, as the communication unit of the sensor node takes more energy from the battery unit in transmitting the data packet from the inner body sensor to the outer body devices. For transmitting the physiological data, a routing protocol is also required to satisfy QoS (Quality of Service) parameters like energy-efficient, high packet data rate, and minimum delay. QoS parameters associated with WBAN analyze their performance by simulating on MATLAB simulation tool.
The IoT can lead to disruptive healthcare innovation. Research articles on IoT in healthcare and COVID-19 pandemics are thus researched in order to discover the potential of this technology. This literature-based research may help professionals to explore solutions to associated issues and battle the COVID-19 epidemic. Using a process diagram, IoT's significant accomplishments were briefly evaluated. Then seven critical IoT technologies that look useful in healthcare during the COVID-19 Pandemic are identified and illustrated. Finally, in the COVID-19 Pandemic, potential fundamental IoT applications were identified for the medical industry with a short explanation. The present predicament has opened up a fresh avenue to creativity in our everyday lives. The Internet of Things is an up-and-coming technology that enhances and gives better solutions in the medical area, such as appropriate medical record-keeping, sample, device integration, and cause of sickness. IoT's sensor-based technology gives a remarkable ability to lower the danger of intervention in challenging circumstances and is helpful for the pandemic type COVID-19. In the sphere of medicine, IoT's emphasis is on helping to treat diverse COVID-19 situations accurately. It facilitates the work of the surgeon by reducing risks and enhancing overall performance. Using this technology, physicians may readily identify changes in the COVID-19's vital parameters. These information-based services provide new prospects for healthcare as they advance towards the ideal technique for an information system to adapt world-class outcomes by improving hospital treatment systems. Medical students may now be better taught and led in the future for the identification of sickness. Proper use of IoT may assist handle several medical difficulties such as speed, affordability, and complexity appropriately. It may simply be adapted to track patients' calorific intake and therapy with COVID-19 asthma, diabetes, and arthritis. In COVID-19 pandemic days, this digitally managed health management system may enhance the overall healthcare performance.
The issue of medication noncompliance has resulted in major risks to public safety and financial loss. The new omnipresent medicine enabled by the Internet of things offers fascinating new possibilities. Additionally, an in-home healthcare station (IHHS), it is necessary to meet the rapidly increasing need for routine nursing and on-site diagnosis and prognosis. This article proposes a universal and preventive strategy to drug management based on intelligent and interactive packaging (I2Pack) and IMedBox. The controlled delamination material (CDM) seals and regulates wireless technologies in novel medicine packaging. As such, wearable biomedical sensors may capture a variety of crucial parameters via wireless communication. On-site treatment and prediction of these critical factors are made possible by high-performance architecture. The user interface is also highlighted to make surgery easier for the elderly, disabled, and patients. Land testing incorporates and validates an approach for prototyping I2Pack and iMedBox. Additionally, sustainability, increased product safety, and quality standards are crucial throughout the life sciences. To achieve these standards, intelligent packaging is also used in the food and pharmaceutical industries. These technologies will continuously monitor the quality of a product and communicate with the user. Data carriers, indications, and sensors are the three most important groups. They are not widely used at the moment, although their potential is well understood. Intelligent packaging should be used in these sectors and the functionality of the systems and the values presented in this analysis.
Self-sustainable sensing systems composed of micro/nano sensors and nano-energy harvesters contribute significantly to developing the internet of things (IoT) systems. As one of the most promising IoT applications, smart home relies on implementing wireless sensor networks with miniaturized and multi-functional sensors, and distributed, reliable, and sustainable power sources, namely energy harvesters with a variety of conversion mechanisms. To extend the capabilities of IoT in the smart home, a technology fusion of IoT and artificial intelligence (AI), called the artificial intelligence of things (AIoT), enables the detection, analysis, and decision-making functions with the aids of machine learning assisted algorithms to form a smart home based intelligent system. In this review, we introduce the conventional rigid microelectromechanical system (MEMS) based micro/nano sensors and energy harvesters, followed by presenting the advances in the wearable counterparts for better human interactions. We then discuss the viable integration approaches for micro/nano sensors and energy harvesters to form self-sustainable IoT systems. Whereafter, we emphasize the recent development of AIoT based systems and the corresponding applications enabled by the machine learning algorithms. Smart home based healthcare technology enabled by the integrated multi-functional sensing platform and bioelectronic medicine is also presented as an important future direction, as well as wearable photonics sensing system as a complement to the wearable electronics sensing system.
Background/objectives
The Internet of Things (IoT) can create disruptive innovation in healthcare. Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare. For this, a brief study is required for research directions.
Methods
Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology's capabilities. This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic.
Results
Briefly studied the significant achievements of IoT with the help of a process chart. Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic. Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them.
Conclusions
In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives. Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases. IoT's sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic. In the medical field, IoT's focus is to help perform the treatment of different COVID-19 cases precisely. It makes the surgeon job easier by minimising risks and increasing the overall performance. By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient. This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital. Medical students can now be better trained for disease detection and well guided for the future course of action. IoT's proper usage can help correctly resolve different medical challenges like speed, price, and complexity. It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient. This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days.
Internet of Things (IoT) is a new buzzword in information technology where real-world physical objects are made smart by integrating them with internet-enabled technologies. The things can sense information around them, communicate the sensed information over some protocol and employ the information to solve real-life problems. In IoT, several technologies are integrated under a common umbrella so that they can connect and exchange data over a network protocol. A huge amount of data is generated from diverse geographical locations with the consequent urge for fast aggregation of overall sensed information, leading to an increase in the need to store and process such data in a more efficient and effective manner. The traditional fields of embedded systems, WSN, real-time analytics, automation system, machine learning and others all contribute to enabling the IoT. This article is focused on discussing the various IoT technologies, protocols and their application and usage in our daily life. It also summarizes the current state-of-the-art IoT architecture in various spheres conventionally and all related terminologies that will give the forthcoming researchers a glimpse of IoT as a whole.
Recent advances in nano-materials and nanotechnology have paved the way for building integrated devices with a nanometric size, named nano-nodes. These nano-nodes are composed of nano-processor, nano-memory, nano-batteries, nano-transceiver, nano-antenna and nano-sensors, which operate at nano-scale level. They are able to perform simple tasks, such as sensing, computing and actuation. The interconnection between microdevices and nanonodes/nanosensors has enabled the development of a new network standard, called Wireless Nano-Sensors Network (WNSN). This paper provides an in-depth review of WNSN, its architectures, application areas, and challenges, which need to be addressed, while identifying opportunities for their implementation in various application domains.
Advances in remote interchanges, the internet of nano things have empowered the wireless body area networks (WBAN) to end up a promising systems of networking standard. It involves interconnected tiny sensors to gather ongoing biomedical data and transmit over the network for further analysis. Due to possibility of active and passive number of attacks, the healthcare data security is quite essential and challenging. This paper presents the systematic literature review (SLR) of the multiple security schemes for WBAN. We have identified a research question to analyses the possibility of several attacks while preserving the memory constraints. We have performed quality valuation to ensure the relevance of schemes with the research question. Moreover, the schemes are considered from 2016 to 2020 to focus on recent work. In literature, several existing schemes are explored to identify how the security is enhanced for exchanging patients' healthcare data. The data security schemes using AES, ECC, SHA-1 and hybrid encryption are analyzed based on influential traits. Several methodologies for data security in WBAN are considered and the most appropriate methodologies are appraised. We also analyses the security for different attack scenarios.
In today's health care context, the application of the Internet of Things (IoT) offers suitability for doctors and patients as we can use them in many medical fields. So, we have emphasized the particular use of the IoT in medicine and health care, such as clinical devices management, medication management, clinical data management, distant medicine, mobile medical care, and individual health management. We have done an organized review to enhance health management systems via many up‐to‐date IoT‐oriented health care applications. The article approbates with the methodological necessities of systematic literature reviews. We have identified several issues caused by the quick acceptance of IoT‐oriented systems' health management. IoT necessitates new safety infrastructure, according to the novel practical principles. Outcomes illustrate that health experts can assist in supplying necessities, information, or standards to improve IoT devices or systems. Also, they are able to present novel fields or applications for which IoT is fit. This survey aids the hospitals and relevant institutions to recognize IoT needs. Also, the article will serve as an initial location for future IoT security management and design. Policymakers and opinion formers need to understand the IoT and its implications.
Terahertz-based nano-networks are emerging as a groundbreaking technology able to play a decisive role in future medical applications owing to their ability to precisely quantify figures, such as the viral load in a patient or to predict sepsis shock or heart attacks before they occur. Due to the extremely limited size of the devices composing these nano-networks, the use of the Terahertz (THz) band has emerged as the enabling technology for their communication. However, the characteristics of the THz band, which strictly reduce the communication range inside the human body, together with the energy limitations of nano-nodes make the in-body deployment of nano-nodes a challenging task. To overcome these problems, we propose a novel in-body flow-guided nano-network architecture consisting of three different devices: i) nano-node, ii) nano-router, and iii) bio-sensor. As the performance of this type of nano-network has not been previously explored, a theoretical framework capturing all its particularities is derived to properly model its behavior and evaluate its feasibility in real medical applications. Employing this analytical model, a thorough sensitivity study of its key parameters is accomplished. Finally, we analyze the terahertz flow-guided nano-network design to satisfy the requirements of several medical applications of interest.
With the development of communication and information technologies, smart tourism is gradually changing the tourism industry. Internet of Things (IoT) plays an important role in smart tourism. However, it is a challenge to apply IoT for smart tourism because of the need for dealing with a vast amount of data and low-latency communication. To this end, in this article, we outline 5G and AI-empowered IoT systems for smart tourism. Efficient data transmission based on 5G technology and smart data processing based on AI technology are significant to unlocking IoT based smart tourism applications. To demonstrate the superior performance of our proposed method, we perform a case study on POI recommendation. The experiment results demonstrate the efficiency and effectiveness of our proposed method.
The Internet of Bio-NanoThings (IoBNT) concept envisions the connection between biological cells and the Internet. The ultimate goal of IoBNT is to catalyze a revolution in biomedical technologies through advances in molecular communication, integrated systems, bio-nanosensors and synthetic biology to improve human health and quality of life. In this paper, an application of IoBNT called PANACEA (a solution or remedy for all difficulties or diseases in Latin) is presented as a solution for an end-to-end design towards realizing the IoBNT for the first time in the literature. The architecture of PANACEA is tailored to focus on diagnosis and therapy of infectious diseases. In PANACEA, to detect the communication within the cells of the body to deduce infection level, a submilimeter implantable bio-electronic device, a Bio-NanoThing, is proposed. BNT can transmit the detected infection data remotely to a wearable hub/gateway outside of the body. The hub can use mobile devices and the backbone network such as Internet or cellular systems to reach the healthcare providers who can remotely control the BNTs. Hence, PANACEA provides a system, where sensing, actuation and computing processes are tightly coupled to provide a reliable and responsive disease detection and infection recovery system. Incorporating molecular communication and conventional networks brings many challenges that are attacked in various fronts such as circuit and biosensor design, communications engineering, with novel solutions presented in this paper, accompanied with simulation results.
Body Sensor Networks (BSN) have emerged as a particularization of Wireless Sensor Networks (WSN) in the context of body monitoring environments, closely linked to healthcare applications. These networks are made up of smart biomedical sensors that allow the monitoring of physiological parameters and serve as the basis for e-Health applications. This Special Issue collects some of the latest developments in the field of BSN related to new developments in biomedical sensor technologies, the design and experimental characterization of on-body/in-body antennas and new communication protocols for BSN, including some review studies.
Internet of things (IoT) is considered as the next evolution of the Internet. IoT is considered as a global network of things, having a distinct identity, and these are interconnected via a wide range of the network to exchange data and present it into vital information. IoT is an emerging technology that shaping the development of the ICT sector at large. The Internet of Things can also be considered as a uniquely recognizable smart object and their virtual representation in an Internet-like structure. To interchange data, IoT resources using the internet makes use of multiple interconnected technologies like Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID). Also, integration of IoT with cloud provides improved services to end-user. For beneficence to the growing body of research on IoT, authors have presented the four-layered architecture of IoT, services offered by IoT, current IoT applications as well as issues and challenges of IoT technology in detail in this paper.
Healthcare sector is probably the most benefited from the applications of nanotechnology. The nanotechnology, in the forms of nanomedicine, nanoimplants, nanobiosensors along with the internet of nano things (IoNT), has the potential to bring a revolutionizing advancement in the field of medicine and healthcare services. The primary aim of this paper is to explore the clinical and medical possibilities of these different implementations of nanotechnology. This paper provides a comprehensive overview of nanotechnology, biosensors, nanobiosensors, and IoNT. Furthermore, multilevel taxonomies of nanotechnology, nanoparticles, biosensors, nanobiosensors, and nanozymes are presented. The potential medical and clinical applications of these technologies are discussed in details with several examples. This paper specifically focuses on IoNT and its role in healthcare. In addition to describing a general architecture of IoNT for healthcare, the communication architecture of the IoNT is also explained. The challenges in the successful realization of IoNT are also discussed critically, along with a special discussion on internet of bio-nano things (IoBNT) and its potential in making IoNT more compatible to human body.
Nanosensors are nanoscale devices that measure physical attributes and convert these signals into analyzable information. In preparation, for the impending reality of nanosensors in clinical practice, we confront important questions regarding the evidence supporting widespread device use. Our objectives are to demonstrate the value and implications for new nanosensors as they relate to the next phase of remote patient monitoring and to apply lessons learned from digital health devices through real-world examples.
Notice of Violation of IEEE Publication Principles
"Affirmative Fusion Process for Improving Wearable Sensor Data Availability in Artificial Intelligence of Medical Things,"
by P. M. Kumar, L. U. Khan and C. S. Hong,
in IEEE Sensors Journal, Early Access
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.
The submitting author, Priyan Malarvizhi Kumar, added the coauthors Latif U. Khan and Choong Seon Hong without their consent. Due to the nature of this violation, the Editor in Chief has decided the article will not be published in an issue of IEEE Sensors Journal.
Artificial Intelligence of Medical Things (AIoMT) is a hybridized outcome of Internet of Things (IoT), machine learning (ML) paradigms, and data analytics procedures for sophisticated healthcare services and applications. However, the fluctuating or lacking wearable sensors (WSs) data cause trivial computing errors that lead to incomplete diagnosis/ recommendation in healthcare applications. This article proposes a novel Affirmative Fusion Process (AFP) to enable high quality WS data with fewer fluctuations in in medical diagnosis. The proposed process assimilates sensed data with the existing datasets for avoiding discrete availability of WS data during the analysis. In this fusion process, based on the dataset inputs, the discreteness in the sensed data is identified. The discreteness is mitigated through precise replacement consideration from the existing datasets, preventing computational errors. The fusion process is monitored using simulated annealing and neural learning for output approximation and identification. The fused output with and without discreteness is identified for which annealing-based approximation is performed. In this process, the recurrence of the learning iterates is confined to identifying the final best solution. The proposed process is assessed using an activity dataset for the metrics fusion ratio, time delay, complexity, and data availability.
The smartphone has been broadly combined with nanosensors, including sensor chips, test strips, and handheld detectors for biochemical applications because of their ubiquitous and portability and availability. The smartphone-based nanosensors can primarily be classified through transducers. They combine superiorities of nanomaterials and sensing platforms that are used for selective, quick, and sensitive disease determination and are of great interest in the biology, chemistry, and medical communities. The prompt and precise diagnosis of infected people is the most important action in controlling diagnosis and monitoring of health services. Nanosensors must provide important requirements such as response accuracy, reproducibility, high selectivity, nontoxicity, sensitivity, and cost-effectiveness. This chapter provides a wide survey of a variety of smartphone nanosensors for disaster prevention using various approaches. This chapter also highlights the smart sensing methods and mentions their usage in disaster detection. It is also finalized with opportunities and challenges for diagnosis of diseases employing smartphone-based nanosensors.
Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug’s risks and adverse drug reactions. These goals can be achieved by using ‘smart hospitals’ as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions.
Nano Sensors are sensing devices with a dimension of less than or equal to 100 nm. They are incredibly tiny devices that transform physical, chemical, or biological substances into detectable signals. Because of this device's capacity to detect physical and chemical changes, nanotechnology has emerged as a technology of choice in a variety of industries. The device provides efficient and cost-effective methods for detecting and measuring chemical and physical characteristics. This overview discusses the status of Nano Sensors, as well as their accomplishments and potential applications toward downstream targets in medical, security, agriculture as well in Covid-19 detection. The paper provides a summary and critical analysis of various architectures (structures) employed in the development and use of Nano Sensors. Surface engineering is used to generate diverse chemistries for both general and specialised purposes. We derived fresh findings from available data on the mechanism, prospective development of various structures, approaches, and applications, and highlighted the contrasts and similarities in their characteristics and working processes. The review further summarized ability and future expected of this sensor in dealing with the various challenges where different nano sensors, types, fabrication techniques and applications with highlighted novelties of these techniques and applications are presented.
The innovative technologies emerged with the industrial revolution “Industry 4.0” as well as the new ones on the way of advanced digitalization enable delivering enhanced, value-added and cost-effective manufacturing and service operations. One of the first areas of focus for Industry 4.0 applications is operations related to healthcare services. Effective management of healthcare resources, clinical care processes, service planning, delivery and evaluation of healthcare operations are essential for a well-functioning healthcare system. Yet, with the adoption of technologies such as Internet of Health Things, Medical Cyber-Physical Systems, Machine Learning, and Big Data (BD), the healthcare sector has recognized the relevance of Industry 4.0. The concept of BD offered numerous advantages and opportunities in this field. It changed the way information is gathered, shared and utilized. Hence, in this study our main ambition is to provide readers with a review of publications which lie within the intersection of Industry 4.0, BD, and healthcare operations and give future perspectives. Our review shows that BD constitutes an important place on the technologies Industry 4.0 provides in the healthcare domain. It helps design, improve, analyze, assess and optimize operations in the domain.
People’s suffering from COVID- 19 pandemic is a major challenge before the international
and national healthcare agencies. Good health is considered to be a priority among all people
and globally, the awareness of maintaining good health is the central agenda to fight with
virus, bacteria and illness. The World Health Organization, 1948 (WHO) rationalizes and
fosters the provisions regarding public healthcare and issues various guidelines to member
nations to prepare for fight against the disease and promotes measures for better health. A
novel strain of corona virus (nCoV-2019) was identified and formidable outbreak of
pneumonia short of a clear cause in the city of Wuhan (China) in December 2019 and also
stretched worldwide. The official name of this disease was given as Corona virus Disease-
2019 (COVID-19) by World Health Organization. The infection due to pneumonia can be
threatening for life to anyone and the symptoms for this disease may include a cough, fever
and difficulty in breathing. There are more chances of transmission from one person of this
virus infection which may happen over droplet or contact transmission. Still, there is no exact
treatment for COVID-19 though many medicines for the cure for this virus are under
research. The laboratories diagnose this virus disease by using real -time RT-PCR “real-time
reverse transcription polymerase chain reaction” test to detect.
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.
Industrial Internet of Things (IIoT) is a convincing stage by interfacing different sensors around us to the Internet, giving incredible chances for the acknowledgment of brilliant living. It is a fast growing technology in the present scenario. IIoT has its effect on almost every advanced field in the society. It has impact not only on work, but also on the living style of individual and organization. Due to high availability of internet, the connecting cost is decreasing and more advanced systems has been developed with Wi-Fi capabilities. The concept of connecting any device with internet is “IIoT”, which is becoming new rule for the future. This manuscript discusses about the applications of Internet of Things in different areas like — automotive industries, embedded devices, environment monitoring, agriculture, construction, smart grid, health care, etc. A regressive review of the existing systems of the automotive industry, emergency response, and chain management on IIoT has been carried out, and it is observed that IIoT found its place almost in every field of technology.
Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.
In Internet of Things (IoT) based systems, the multi-level user requirements are satisfied by the integration of communication technology with distributed homogeneous networks termed as the ubiquitous computing systems (UCS). The PCS demands openness in heterogeneity support, management levels and communication for distributed users. However, providing these features is still a major challenge. In wearable IoT (WIoT) based medical sensors based applications, the end users reliability of communication is enhanced using a scalable distributed computational framework introduced in this paper. The demand and sharing parameters forms the basis of analysis of resource allocation by means of recurrent learning in this framework. The rate of communication may be improved while reducing the time delay for the end users of WIoT based medical sensors with the help of UCS and estimated resource requirements. Other than data transfer, sharing and resource allocation, end-user mobility management may also be performed on the WIoT medical sensors using the proposed framework. Certain metrics are used for proving the consistency of the framework that are assessed with the help of experimental analysis and performance estimation. Parameters inclusive of storage utilization, bandwidth, request backlogs, requests handled, request failure and response time are estimated. Reduced response time, backlogs and request failure with improved storage utilization, bandwidth and requests handled are evident using the proposed framework when compared to the existing models.
Intrabody Nanonetworks (IBN) are composed of nanosensors that have tremendous potential to enable cellular level monitoring and precision in drug delivery and diagnosis. Nanoscale communication using Electromagnetic (EM) waves propagation in the Terahertz (THz) band suffers from molecular absorption noise that has been regarded as the leading cause of heat generation along with antenna communication radiations. In the presented work, we propose a novel Temperature–Aware routing protocol (TA-IBN) that explicitly addresses the thermal related constraints of IBN. The proposed routing scheme aims at stabilizing the temperature in the whole network by avoiding congestion and preventing temperature rise in the heated regions. To avoid temperature rise, nanorouters estimate the temperature increase in their region and excludes data collection from the hotspots areas. Moreover, during data collection, nanonodes’ selection is also optimized based on data freshness to enable reporting of a more accurate state of physiological parameters with minimized antenna radiation exposure time. The temperature increase analysis provided in this work can also be used for safety health assessment in medical applications. We have evaluated the performance of TA-IBN by conducting extensive simulations using the Nano-SIM tool. In addition, we compare TA-IBN with the flooding scheme and Thermal-Aware Routing Algorithm (TARA) to gain further insights into our proposed protocol TA-IBN. The results obtained confirm that our protocol ensures safer intrabody routing and traffic distribution in different regions to normalize temperature rise, avoid congestion, and reduce the communication delay.
Currently India’s population is increasing at the rate of 1.2% and to provide good healthcare services to these people healthcare sector needs a transformation. Information and Communication Technology (ICT) along with nanotechnology revolution has opened up whole new opportunities for developing countries like India to improve the healthcare for the betterment of 1.34 billion lives. With the use of ICT and nano-electronics, we can provide our patients better and specialized healthcare services at a reduced cost. ICT-based healthcare initiatives like eHealth and mHealth includes healthcare centers, mobile telemedicine, electronic patient records, remote patient monitoring, mobile patient monitoring, health surveys, awareness raising, and decision support systems that can play a crucial role towards the accomplishment of development goals to enhance Healthcare services in India. Nanosensors and actuators will play a critical role in the success of these technologies. In this paper, we are going to present a comprehensive review of existing and future wearable healthcare sensor and actuator technologies along with their merits and demerits.
Internet of Things (IoT) emerges as disruptive innovation and development in the fields of drug delivery and biomedical sciences using on-target active transportation, sensors, wearable devices, real-time diagnostics, etc. Semiconducting fluorescence emitting material, quantum dots on integration with IoT displayed interesting results in healthcare sector especially in hospitals and pathological laboratories. Presently, the integrated system is used to improve productivity without the interference of human and offer cost-effective system. This integrated system can be used for detection of various diseases like epilepsy, cancer, diabetes, etc. and various biomedical applications like energy storage, lights, sensor technology, light filters, etc. The integrated technology is implemented into the field of medicine for simplifying the approaches in therapeutics and diagnostic applications. The collected and analyzed data are further useful for healthcare professionals to find patient-centric solutions. Artificial Intelligence-aided IoT emerges as a novel technology for transmitting and securing the health data. Despite some of the limitations like e-waste and risk of hacking, IoT-based QD system will be considered as a modern healthcare provider with life-saving products for enriching medical quality and real-time accessibility.
Internet of Medical Things (IoMT) platform serves as an interoperable medium for healthcare applications by connecting wearable sensors, end-users, and clinical diagnosis centers. This interoperable medium provides solutions for disease diagnosis; prediction and monitoring of end-user health using the physiological vital signs sensed wearable sensor data. The communicating and data exchanging internet of things (IoT) platform imposes latency and overloading uncertainties in the heterogeneous environment. This article introduces cognitive data processing for uncertainty analysis (CDP-UA) for improving the efficiency of WS data management. CDP-UA addresses uncertainties in two levels namely aggregation and dissemination of WS data. The uncertainties in synchronizing aggregation and dissemination slot mapping are addressed using classification learning. In the dissemination process overloaded intervals are identified and segregated using regression learning and conditional sigmoid function analysis. The joint learning process helps to classify overloaded and latency-centric dissemination and aggregation instances to improve the delivery ratio of WS data in the clinical/ medical analysis center. The experimental analysis shows that the proposed method is reliable in achieving less uncertainty factor, latency, and overloaded intervals for varying disseminations and sensing intervals.
Nanotechnology, being one of the buzzwords of the era, enthralled us to usher light in the fields which would have the way for implementation of this technology in medical sector, in future. The objective of this paper is to propose a routing protocol that works in a single-hop transmission process in order to establish communication between nano-devices deployed inside the human body. This paper solely focuses on the routing protocol based on a three-tier handshaking process between the nano-sensors which are dynamically movable inside body and nano-routers which are fixed at certain places in the body also. This routing protocol would operate in terahertz band so that it can take advantage of the potentials of terahertz band channel mode. The system would ensure that the data packet is received by the authorized entity through which it minimizes data loss in an efficient way where energy usage is also minimum by the system. The system uses TS-OOK communication method in order to lessen the code weight. Finally, the process ensures that the recipient receives the data from the very beginning of the process as our nano-sensors are dynamically movable inside the body.
Technology, sports and healthcare industry have been deeply intertwined for a long time. However, new opportunities are now rapidly expanding on the Internet (population), and the result is a huge data. Also, people around the world will be able to wear biosensors for personalizing things, as well as new applications have started using 5G technology. Therefore, the design and development of the 5G network Internet games and healthcare applications of (Internet of Things) IoT and 5G have taken place. 5G-assisted smart medical networks are the convergence of IoT devices that require improved network performance and enhanced mobile phone radio waves. Today's connectivity solutions face the challenges of the Internet of Things, such as equipment, standardization, energy efficiency, device density, and the vast number of security supports. In this article, we will give a comprehensive review of IoT-assisted 5G-assisted smart sports and health care solutions. It must be categorized, and presented in the structure of 5G smart medicine through existing literature. Its smart sports and healthcare system also have key requirements for a successful 5G deployment in some cases. Finally, to discuss the challenges of researching IoT smart 5G sports and healthcare solutions with some open issues.
Telemedicine provides an attractive vision for tele-monitoring human health conditions and, thus, offers the opportunity for timely preventing chronic disease. A key limitation of promoting telemedicine in clinic application is the lack of a noninvasive med-tech and effective monitoring platform, which should be wearable and capable of high-performance tele-monitoring of health risk. Here we proposed a volatolomics-based telemedicine for continuously and noninvasively assessing human health status through continuously tracking the variation of volatile markers derived from human breath or skin. Particularly, a nanosensor-based flexible electronic was specifically designed to serve as a powerful platform for implementing the proposed cost-effective healthcare. An all-flexible and highly packed makeup (all functional units were integrated in a 2*2*0.19 cm 3 plate) enables an electronic, compact configuration and the capability of resisting negative impact derived from customers' daily movement. Notably, the nanosensor-based electronic demonstrates high specificity, quick response rate (t 90% = 4.5 s), and desirable low detection limit (down to 0.117 ppm) in continuous tele-monitoring chronic-disease-related volatile marker (e.g., acetone). Assisted by the power saved light fidelity (Li-Fi) communicating technology, a clinic proof on the specifically designed electronic for noninvasively and uninterrupted assessing potential health risk (e.g., diabetics) is successfully implemented, with the accuracy of around 81%. A further increase in the accuracy of prewarning is predicted by excluding the impact of individual differences such as the gender, age, and smoking status of the customer. These promising pilot results indicate a bright future for the tailor-made nanosensing-device-supported volatolomics-based telemedicine in preventing chronic diseases and increasing patients' survival rate.
Biosensors are emerging as efficient (sensitive and selective) and affordable analytical diagnostic tools for early-stage disease detection, as required for personalized health wellness management. Low-level detection of a targeted disease biomarker (pM level) has emerged extremely useful to evaluate the progression of disease under therapy. Such collected bioinformatics and its multi-aspects-oriented analytics is in demand to explore the effectiveness of prescribed treatment, optimize therapy, and correlate biomarker level with disease pathogenesis. Owing to nanotechnology-enabled advancements in sensing unit fabrication, device integration, interfacing, packaging, and sensing performance at point-of-care (POC) made diagnostics according to the requirements of disease management and patient disease profile i.e., in a personalized manner. Efforts are continuously being made to promote state of art biosensing technology as a next-generation non-invasive disease diagnostics methodology. Keeping this in view, this progressive opinion article describes personalized health care management related analytical tools which provide access to better health for everyone, overall to manage healthy tomorrow timely. Considering accomplishments and predictions, such affordable intelligent diagnostics tools are urgently required to manage COVID-19 pandemic, a life-threatening respiratory infectious disease, where a rapid, selective and sensitive detection of human beta severe acute respiratory system coronavirus (SARS-COoV-2) protein is the key factor.