Ubiquitous Healthcare Services Research Articles

Secure-fault-tolerant efficient industrial internet of healthcare things framework based on digital twin federated fog-cloud networks

The Industrial Internet of Healthcare Things (IIoHT) is the emerging paradigm in digital healthcare. Context-aware healthcare sensors, local intelligent watches, healthcare devices, wireless communication technologies, fog, and cloud computing are all parts of the IIoHT used in healthcare. The ubiquitous healthcare services it provides to its users in practice. However, the current IIoHT healthcare frameworks have security and failure issues in mobile fog and cloud networks where they are spread out. This paper presents the secure, fault-tolerant IIoHT Framework based on digital twin (DT) federated learning-enabled fog-cloud models. The DT is an effective technology that makes virtual copies of servers at different locations. DT integrated with federated learning inside the fog and cloud environments, where the failure of tasks and execution improved for healthcare sensor data. The study aims to reduce processing time and the risk of task failure. The study presents the Secure and Fault-Tolerant Strategies (SFTS)-enabled IIoHT framework that optimizes wearable sensor data and executes it with the minimum offloading and processing delays. Simulation results show that the proposed work minimized the security risk by 40%, failure risk of tasks risk by 50%, and the training and testing time by 39% for sensor data during the execution of mobile fog cloud networks.

AI Computing as Ubiquitous Healthcare Solution: Predict Parkinson’s for Large Masses in Society

Parkinson’s disease (PD) is one of the macroscale-effected neurological disorders that have adverse effects on the quality of life as they influence the proper functioning of organs. To misfortune, PD has limited diagnosing techniques and lacks treatment to cure at advanced stages. Therefore, there is a prime requirement to diagnose PD at the initial stage with the assistance of advanced technologies. This article focuses on the use of revolutionary computing technology, which is Artificial Intelligence (AI) to early diagnose this fatal disorder and provide an efficient healthcare solution. The compact Two-Dimensional Convolution Neural Network (2-D CNN)-based computing algorithm is developed to diagnose PD from the gait variation. The proposed system is able to provide promising accuracy of 98% in classifying PD that surpasses the current state-of-the-art performance. To provide a ubiquitous healthcare solution for diagnosing PD, the proposed 2-D CNN model will be a quantum leap breakthrough. Consequently, the progressive PD spread, growth, and severity can be efficiently controlled. Therefore, discern ubiquitous healthcare services for large masses in society.

Securing Group Patient Communication in 6G-Aided Dynamic Ubiquitous Healthcare with Real-Time Mobile DNA Sequencing.

(1) Background: With an advanced technique, third-generation sequencing (TGS) provides services with long deoxyribonucleic acid (DNA) reads and super short sequencing time. It enables onsite mobile DNA sequencing solutions for enabling ubiquitous healthcare (U-healthcare) services with modern mobile technology and smart entities in the internet of living things (IoLT). Due to some strict requirements, 6G technology can efficiently facilitate communications in a truly intelligent U-healthcare IoLT system. (2) Research problems: conventional single user-server architecture is not able to enable group conversations where "multiple patients-server" communication or "patient-patient" communication in the group is required. The communications are carried out via the open Internet, which is not a trusted channel. Since heath data and medical information are very sensitive, security and privacy concerns in the communication systems have become extremely important. (3) Purpose: the author aims to propose a dynamic group-based patient-authenticated key distribution protocol for 6G-aided U-healthcare services enabled by mobile DNA sequencing. In the protocol, an authenticated common session key is distributed by the server to the patients. Using the key, patients in a healthcare group are allowed to securely connect with the service provider or with each other for specific purposes of communication. (4) Results: the group key distribution process is protected by a secure three-factor authentication mechanism along with an efficient sequencing-device-based single sign-on (SD-SSO) solution. Based on traceable information stored in the server database, the proposed approach can provide patient-centered services which are available on multiple mobile devices. Security robustness of the proposed protocol is proven by well-known verification tools and a detailed semantic discussion. Performance evaluation shows that the protocol provides more functionality and incurs a reasonable overhead in comparison with the existing works.

Cross-Server End-to-End Patient Key Agreement Protocol for DNA-Based U-Healthcare in the Internet of Living Things

(1) Background: Third-generation sequencing (TGS) technique directly sequences single deoxyribonucleic acid (DNA) molecules, enabling real-time sequencing and reducing sequencing time from a few days to a few hours. Sequencing devices can be miniaturized and DNA-reading sensors placed on the body to monitor human health and vital signs, building an “internet of living things” (IoLT) facilitating ubiquitous healthcare services. In many cases, patients may wish to directly connect to each other for purposes of sharing real-time sequencing data, medical status or trading genomic data, etc. (2) Problems: User registration for a specific service may be limited due to some reason. Registering for multiple redundant services would also result in wasted money and possible wasteful communication overhead. In addition, since medical data and health information are very sensitive, security and privacy issues in the network are of paramount importance. (3) Methods: In this article, I propose a cross-server end-to-end (CS-E2E) patient authenticated key agreement protocol for DNA-based healthcare services in IoLT networks. My work allows two patients to mutually authenticate each other through assistance of respective servers, so that they can establish a reliable shared session key for securing E2E communications. The design employs multiple cost-saving solutions and robust cryptographic primitives, including smart-card-based single sign-on, elliptic curve cryptography, biohash function, etc. (4) Results: My proposed protocol is proven to be secure against various attacks and to incur reasonable communication cost compared to its predecessor works. The protocol also provides the support for more security properties and better functionalities. (5) Conclusions: The E2E communications between the patients are properly protected using the proposed approach. This assures a secure and efficient cross-server patient conversation for multiple purposes of healthcare communication.

MEC-IoT-Healthcare: Analysis and Prospects

Physical sensors, intelligent sensors, and output recommendations are all examples of smart health technology that can be used to monitor patients’ health and change their behavior. Smart health is an Internet-of-Things (IoT)-aware network and sensing infrastructure that provides real-time, intelligent, and ubiquitous healthcare services. Because of the rapid development of cloud computing, as well as related technologies such as fog computing, smart health research is progressively moving in the right direction. Cloud, fog computing, IoT sensors, blockchain, privacy and security, and other related technologies have been the focus of smart health research in recent years. At the moment, the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance, including cloud storage, retrieval, and calculation of health big data. This article reviews state-of-the-art edge computing methods that has shifted to the collection, transmission, and calculation of health data, which includes various sensors and wearable devices used to collect health data, various wireless sensor technologies, and how to process health data and improve edge performance, among other things. Finally, the typical smart health application cases, blockchain’s application in smart health, and related privacy and security issues were reviewed, as well as future difficulties and potential for smart health services. The comparative analysis provides a reference for the the mobile edge computing in healthcare systems.

Mitigate Inter-WBAN Interference in Body-to-Body Network to Restrain Epidemic Spread.

Rapid growth of wireless technology and machine learning fueled demand for ubiquitous healthcare service in India. This necessity is served by an emerging technology called wireless body area network (WBAN). This facilitates an individual to be aware of his health status anywhere anytime without any assistance. In case of any emergency, this network is capable to initialize other automated systems. During an epidemic, if we can early detect a susceptible individual, spread of the disease can be curbed. In our paper, early detection is achieved using multiple cooperating WBANs that leads to a network called Body-to-Body Network (BBN). We have also proposed quarantine strategies by minimizing contact between different staged WBANs based on their health status. An unsupervised learning algorithm is used to efficiently divide the area into non-overlapping clusters minimizing inter-WBAN interference. We have considered two test case scenarios based on how the WBANs are distributed in BBN architecture. OMNet++-based simulator Castalia 3.2 is used to evaluate routing protocol in BBN network. Performance of our system is assessed based on network parameters like Packet Delivery Ratio (PDR). Results ensure that our method guarantees low epidemic spread of disease in enclosed area by enhancing throughput and minimizing interference of our stable system.

Guest Editorial Emerging IoT-Driven Smart Health: From Cloud to Edge

The papers in this special section focus on emerging Internet of Medical Things. Recent advances in advances in healthcare can be experienced with the development of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), edge computing, Edge AI, 6G, cloud computing, and connected healthcare have attracted a great deal of attention and a wide range of views. However, the need to deliver real-time and accurate healthcare services to patients, while reducing costs is a challenging issue [1]. Especially, COVID-19 has recently demonstrated the importance of fast, comprehensive, and accurate intelligent healthcare involving different types of medical, physiological, and epidemiological investigation data to diagnose the virus. Smart health is a real-time, intelligent, ubiquitous healthcare service based on Internet of bioMedical Things (IoMT). With the rapid development of related technologies such as deep learning, edge computing and IoT, smart health is playing vital role in healthcare industry to increase the accuracy, reliability, and productivity of mobile sensory devices.

Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge.

Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.

Synergetic enhancement of the energy harvesting performance in flexible hybrid generator driven by human body using thermoelectric and piezoelectric combine effects

Flexible thermoelectric-piezoelectric generator (f-TPEG) has emerged as the leading candidate to realize long-term biomedical monitoring-based ubiquitous healthcare services. We developed a f-TPEG composed of a piezoelectric (PE) poly(vinylidene fluoride-co-trifluoroethylene) thin film and thermoelectric (TE) bismuth telluride-based alloy blocks. The surficial properties of TE and PE components of f-TPEG were thoroughly optimized to minimize the loss of output performance and enhance the mechanical stability. The f-TPEG harvested a maximum electric voltage of ~17 V and current signals of ~3.8μA at a temperature difference of 3 K by periodic bendings: The harvested output power of the fabricated hybrid energy generator was well integrated without sacrificing each PE and TE-based energy generation. In addition, to confirm the biological energy harvesting efficiency of the generator, we demonstrated the f-TPEG taped onto human fingers, which converts an output voltage of about 8 V and a current pulse of about 6 μA. The results obtained herein suggest that the f-TPEG would play a variety of important roles in wearable device, which needs long-term biomedical monitoring and applications in smart clothing.

Health-BlockEdge: Blockchain-Edge Framework for Reliable Low-Latency Digital Healthcare Applications.

The rapid evolution of technology allows the healthcare sector to adopt intelligent, context-aware, secure, and ubiquitous healthcare services. Together with the global trend of an aging population, it has become highly important to propose value-creating, yet cost-efficient digital solutions for healthcare systems. These solutions should provide effective means of healthcare services in both the hospital and home care scenarios. In this paper, we focused on the latter case, where the goal was to provide easy-to-use, reliable, and secure remote monitoring and aid for elderly persons at their home. We proposed a framework to integrate the capabilities of edge computing and blockchain technology to address some of the key requirements of smart remote healthcare systems, such as long operating times, low cost, resilience to network problems, security, and trust in highly dynamic network conditions. In order to assess the feasibility of our approach, we evaluated the performance of our framework in terms of latency, power consumption, network utilization, and computational load, compared to a scenario where no blockchain was used.

Real-time Health Monitoring System Using Wireless Body Area Network

Wireless body area network (WBAN) being a sub-domain of wireless sensor network (WSN) is a new emerging technology for healthcare applications. A WBAN consists of low-power tiny wireless nodes placed on or around the human body that continuously observe vital health signs of a patient. These sensors are capable of sending information of physiological parameters taken from human body to other devices for diagnosis procedures and prescription. WBAN provides ubiquitous healthcare services and enables greater mobility without restricting human normal activities, as the medical personnel can observe the patient health conditions based on the data received through the wireless network. This research work provides a WBAN based healthcare monitoring system that can provide the electrocardiogram (ECG), heartbeat, and human body temperature information. The wireless transmission of the received data from human body is performed by using Zigbee IEEE802.15.4 communication standard. The physiological data will be communicated to remote medical server where data is stored and analyzed. In case any disease is diagnosed, medical personnel can provide immediate assistance to the patients.

Real-time Health Monitoring using Wireless Body Area Network

Wireless body area network (WBAN) being a sub-domain of wireless sensor network (WSN) is a new emerging technology for healthcare applications. A WBAN consists of low-power tiny wireless nodes placed on or around the human body that continuously observe vital health signs of a patient. These sensors are capable of sending information of physiological parameters taken from human body to other devices for diagnosis procedures and prescription. WBAN provides ubiquitous healthcare services and enables greater mobility without restricting human normal activities, as the medical personnel can observe the patient health conditions based on the data received through the wireless network. This research work provides a WBAN based healthcare monitoring system that can provide the electrocardiogram (ECG), heartbeat, and human body temperature information. The wireless transmission of the received data from human body is performed by using Zigbee IEEE802.15.4 communication standard. The physiological data will be communicated to remote medical server where data is stored and analyzed. In case any disease is diagnosed, medical personnel can provide immediate assistance to the patients.

Lightweight and Secure Three-Factor Authentication Scheme for Remote Patient Monitoring Using On-Body Wireless Networks

On-body wireless networks (oBWNs) play a crucial role in improving the ubiquitous healthcare services. Using oBWNs, the vital physiological information of the patient can be gathered from the wearable sensor nodes and accessed by the authorized user like the health professional or the doctor. Since the open nature of wireless communication and the sensitivity of physiological information, secure communication has always been the vital issue in oBWNs-based systems. In recent years, several authentication schemes have been proposed for remote patient monitoring. However, most of these schemes are so susceptible to security threats and not suitable for practical use. Specifically, all these schemes using lightweight cryptographic primitives fail to provide forward secrecy and suffer from the desynchronization attack. To overcome the historical security problems, in this paper, we present a lightweight and secure three-factor authentication scheme for remote patient monitoring using oBWNs. The proposed scheme adopts one-time hash chain technique to ensure forward secrecy, and the pseudonym identity method is employed to provide user anonymity and resist against desynchronization attack. The formal and informal security analyses demonstrate that the proposed scheme not only overcomes the security weaknesses in previous schemes but also provides more excellent security and functional features. The comparisons with six state-of-the-art schemes indicate that the proposed scheme is practical with acceptable computational and communication efficiency.

IILS: Intelligent imaging layout system for automatic imaging report standardization and intra-interdisciplinary clinical workflow optimization

BackgroundTo achieve imaging report standardization and improve the quality and efficiency of the intra-interdisciplinary clinical workflow, we proposed an intelligent imaging layout system (IILS) for a clinical decision support system-based ubiquitous healthcare service, which is a lung nodule management system using medical images. MethodsWe created a lung IILS based on deep learning for imaging report standardization and workflow optimization for the identification of nodules. Our IILS utilized a deep learning plus adaptive auto layout tool, which trained and tested a neural network with imaging data from all the main CT manufacturers from 11,205 patients. Model performance was evaluated by the receiver operating characteristic curve (ROC) and calculating the corresponding area under the curve (AUC). The clinical application value for our IILS was assessed by a comprehensive comparison of multiple aspects. FindingsOur IILS is clinically applicable due to the consistency with nodules detected by IILS, with its highest consistency of 0·94 and an AUC of 90·6% for malignant pulmonary nodules versus benign nodules with a sensitivity of 76·5% and specificity of 89·1%. Applying this IILS to a dataset of chest CT images, we demonstrate performance comparable to that of human experts in providing a better layout and aiding in diagnosis in 100% valid images and nodule display. The IILS was superior to the traditional manual system in performance, such as reducing the number of clicks from 14·45 ± 0·38 to 2, time consumed from 16·87 ± 0·38 s to 6·92 ± 0·10 s, number of invalid images from 7·06 ± 0·24 to 0, and missing lung nodules from 46·8% to 0%. InterpretationThis IILS might achieve imaging report standardization, and improve the clinical workflow therefore opening a new window for clinical application of artificial intelligence. FundThe National Natural Science Foundation of China.

Fog-Enabled Smart Health: Toward Cooperative and Secure Healthcare Service Provision

The rise of smart health promotes ubiquitous healthcare services with the adoption of information and communication technologies. However, increasing demands of medical services require more computing and storage resources in proximity of medical users for intelligent sensing, processing, and analysis. Fog computing emerges to enable in situ data processing and service provision for smart health in proximity of medical users, exploiting a large number of small-scale servers. In this article, we investigate fog-enabled smart health toward cooperative and secure healthcare service provision. Specifically, we first introduce the overall infrastructure and some promising applications, including emergent healthcare service, health risk assessment, and healthcare notification. We then discuss the challenges of fog-enabled smart health from the perspectives of cooperation and security. A case study is presented to demonstrate efficient and secure health data sharing through naive Bayes classification and attribute-based encryption with assistance from fog computing. Finally, by exploring interesting future directions, more attention can be attracted to this emerging area.

Fusing Identity Management, HL7 and Blockchain into a Global Healthcare Record Sharing Architecture

Healthcare record sharing among various medical roles is a critical and challenging research problem especially in today’s everchanging global IT solutions. The emergence of blockchain as a new enabling technology brought radical changes to numerous business applications, including healthcare. Blockchain is a trusted distributed ledger that forms a decentral-ized infrastructure. There have been several proposals regarding the sharing of critical healthcare records over blockchain infras-tructure without requiring prior knowledge/trust of the parties involved (patients, service providers, and insurance companies). Another yet important issue is to securely share medical records across various countries for travelling patients to ensure an integrated and ubiquitous healthcare service. In this paper, we present a globally integrated healthcare record sharing architec-ture based on blockchain and HL7 client. Healthcare records are stored at the hosting country and are not stored on the blockchain. This architecture avails medical records of travelling patients temporarily and after performing necessary authentication. The actual authorisation process is performed on a federated identity management system, such as, the Shibboleth. Though there are similarities with identity management systems, our system is unique as it involves the patient in the permission process and discloses to them the identities of entities accessed their health records. Our solution also improves performance and guarantees privacy and security through the use of blockchain and identity management system.

Dynamic Connectivity Establishment and Cooperative Scheduling for QoS-Aware Wireless Body Area Networks

In a hospital environment, the total number of Wireless Body Area Network (WBAN) equipped patients requesting ubiquitous healthcare services in an area increases significantly. Therefore, increased traffic load and group-based mobility of WBANs degrades the performance of each WBAN significantly, concerning service delay and network throughput. In addition, the mobility of WBANs affects connectivity between a WBAN and an Access Point (AP) dynamically, which affects the variation in link quality significantly. To address the connectivity problem and provide Quality of Services (QoS) in the network, we propose a dynamic connectivity establishment and cooperative scheduling scheme, which minimizes the packet delivery delay and maximizes the network throughput. First, to secure the reliable connectivity among WBANs and APs dynamically, we formulate a selection parameter using a price-based approach. Thereafter, we formulate a utility function for the WBANs to offer QoS using a coalition game-theoretic approach. We study the performance of the proposed approach holistically, based on different network parameters. We also compare the performance of the proposed scheme with the existing state-of-the-art.

An Internet of Things based physiological signal monitoring and receiving system for virtual enhanced health care network.

Wireless physiological signal monitoring system designing with secured data communication in the health care system is an important and dynamic process. We propose a signal monitoring system using NI myRIO connected with the wireless body sensor network through multi-channel signal acquisition method. Based on the server side validation of the signal, the data connected to the local server is updated in the cloud. The Internet of Things (IoT) architecture is used to get the mobility and fast access of patient data to healthcare service providers. This research work proposes a novel architecture for wireless physiological signal monitoring system using ubiquitous healthcare services by virtual Internet of Things. We showed an improvement in method of access and real time dynamic monitoring of physiological signal of this remote monitoring system using virtual Internet of thing approach. This remote monitoring and access system is evaluated in conventional value. This proposed system is envisioned to modern smart health care system by high utility and user friendly in clinical applications. We claim that the proposed scheme significantly improves the accuracy of the remote monitoring system compared to the other wireless communication methods in clinical system.

Efficient end-to-end authentication protocol for wearable health monitoring systems

Wearable health monitoring systems (WHMSs) will play an increasingly important role in future e-healthcare and enable smart and ubiquitous healthcare services. Given its sensitivity, the health data should be protected against unauthorized access. As a result, it is critical to design an end-to-end mutual authentication protocol that enables secure communication between the wearable sensor and medical professionals. Recently, Amin et al. proposed an anonymity preserving mutual authentication protocol for WHMSs. However, we identify that their protocol suffers from stolen mobile device attack, desynchronization attack, and sensor key exposure. Then we put forward an improved end-to-end authentication protocol based on quadratic residues. Comprehensive security analysis is conducted to show that the proposed protocol fixes these flaws of Amin et al.’s protocol and satisfies all desired requirements. The comparison with these existing protocols demonstrates that our protocol provides a practical end-to-end security solution for WHMSs.

An IoT approach for integration of computational intelligence and wearable sensors for Parkinson’s disease diagnosis and monitoring

Nowadays the continuous growing in global population and the related increase of life expectancy lead to explore new ways of making the most of the limited resources humanity has. This endeavor challenges especially the current health care of elderly population, which is particularly associated with a marked prevalence of chronic neurological disorders such as Parkinson’s Disease. Internet of Things and wearable technologies have opened up a new revolution in the domain of healthcare. Minimizing the response time in diagnosis and treatment, Internet of Things thrives towards omnipresence of the healthcare services. Using wearable devices, the lifestyle data is collected from multifarious sources, which is then accumulated, analyzed and acted upon. The emerging technological area of Wearable Sensors and the Internet of Things seems to provide a smart and intelligent way of catering ubiquitous healthcare services to the elderly population, taking healthcare facilities to a higher dimension of omnipresence.