Smart Healthcare System Research Articles

A Lightweight and Anonymous Mutual Authentication Scheme for Medical Big Data in Distributed Smart Healthcare Systems.

The rapid development of Big Data technology supports the advancement of many fields like industrial automation, smart healthcare, distributed systems, and many more. Big data is large and heterogeneous data generated from different sources, such as Internet of Things (IoT) devices, weather forecasting, traffic management systems, etc. However, in a distributed smart healthcare industry, unauthorized users or devices can illegally access healthcare Big Data, as well as control the sensor or IoT-enabled devices connected to a patient's body. They can even alter patients' healthcare Big Data by inserting false and misleading data, which may even cause death to the patient. This study presents a lightweight privacy-preserving user authentication scheme to solve the above-said problems in a distributed smart healthcare system. The proposed scheme prevents unauthorized users from getting access to the healthcare system by establishing a secure session for the authorized user. Here, the password protection mechanism allows only a legitimate user to access and modify the patient's healthcare Big Data. The security strength and effectiveness of the proposed authentication scheme is evaluated in this article, which show that it is more efficient and secure than the state-of-the-art schemes.

SBTD: Secured Brain Tumor Detection in IoMT Enabled Smart Healthcare.

Brain tumors are fatal and severely disrupt brain function as they advance. Timely detection and precise monitoring are crucial for improving patient outcomes and survival. A smart healthcare system leveraging the Internet of Medical Things (IoMT) revolutionizes patient care by offering streamlined remote healthcare, especially for individuals with acute medical conditions like brain tumors. However, such systems face significant challenges, such as (1) the increasing prevalence of cyber attacks in the expanding digital healthcare landscape, and (2) the lack of reliability and accuracy in existing tumor detection methods. To address these issues, we propose Secured Brain Tumor Detection (SBTD), the first unified system integrating IoMT with secure tumor detection. SBTD features: (1) a robust security framework, grounded in chaos theory, to safeguard medical data; and (2) a reliable machine learning-based tumor detection framework that accurately localizes tumors using their anatomy. Comprehensive experimental evaluations on different multimodal MRI datasets demonstrate the system's suitability, clinical applicability and superior performance over state-of-the-art algorithms.

Artificial Intelligence and Blockchain Enabled Smart Healthcare System for Monitoring and Detection of COVID-19 in Biomedical Images.

Millions of individuals around the world have been impacted by the ongoing coronavirus outbreak, known as the COVID-19 pandemic. Blockchain, Artificial Intelligence (AI), and other cutting-edge digital and innovative technologies have all offered promising solutions in such situations. AI provides advanced and innovative techniques for classifying and detecting symptoms caused by the coronavirus. Additionally, Blockchain may be utilized in healthcare in a variety of ways thanks to its highly open, secure standards, which permit a significant drop in healthcare costs and opens up new ways for patients to access medical services. Likewise, these techniques and solutions facilitate medical experts in the early diagnosis of diseases and later in treatments and sustaining pharmaceutical manufacturing. Therefore, in this work, a smart blockchain and AI-enabled system is presented for the healthcare sector that helps to combat the coronavirus pandemic. To further incorporate Blockchain technology, a new deep learning-based architecture is designed to identify the virus in radiological images. As a result, the developed system may offer reliable data-gathering platforms and promising security solutions, guaranteeing the high quality of COVID-19 data analytics. We created a multi-layer sequential deep learning architecture using a benchmark data set. In order to make the suggested deep learning architecture for the analysis of radiological images more understandable and interpretable, we also implemented the Gradient-weighted Class Activation Mapping (Grad-CAM) based colour visualization approach to all of the tests. As a result, the architecture achieves a classification accuracy rate of 0.96, thus producing excellent results.

Fog-Enabled Secure and Efficient Fine-Grained Searchable Data Sharing and Management Scheme for IoT-Based Healthcare Systems

In recent times, fog computing has emerged as a helpful extension of cloud computing. It can efficiently handle the prevalent issue of managing silos of data generated by today's digital healthcare services. Moreover, the application of the Internet of Things (IoT) in the development of smart healthcare systems further adds tons of data tirelessly to these silos, thus making the cloud congested. To manage such continuously growing data, the concept of adding a fog layer between the cloud and the end-users (EUs) proved to be beneficial. These intermediary fog nodes (FNs) can handle and store data, and thus facilitate the cloud and alleviate the burden from the EUs. Most of the existing search schemes for encrypted data have been developed for the cloud platform and ignored this helpful extension, which can improve the scheme's efficiency by delegating most of the heavy computations to the intermediary FNs. In this article, a fine-grained searchable data sharing scheme has been proposed using the fog computing platform. The resulting scheme is efficient and lightweight because the FN facilitates EUs by performing computationally intensive tasks on their behalf. A significant reduction in storage and computational cost has been achieved by the proposed scheme at the data owner's end, representing the resource-constrained IoT devices. The storage cost has been reduced to two source group elements, and the computational cost has been reduced to three exponent operations in the source group and one hash operation. Furthermore, the proposed scheme is secure against the selectively chosen keyword attack in the generic bilinear group model.

Tailored MXenes and graphene as efficient telemedicine platforms for personalized health wellness

This comprehensive review paper provides an insightful exploration of the burgeoning field of 2D nanostructures and their development as telemedicine platforms for futuristic smart healthcare systems.

Encryption-then-embedding-based hybrid data hiding scheme for medical images

The smart healthcare system plays a vital role in modern healthcare, facilitating the exchange of Electronic Patient Records (EPR), and improving medical care. Nevertheless, safeguarding the inherent security and confidentiality of EPR data persists as a formidable challenge. This issue demanding rigorous attention and innovative solutions. Digital watermarking safeguards the genuineness and integrity of digital images, and is widely employed. In medical imaging, it is vital to guarantee the confidentiality and security of patient data. In this paper, we propose an Encryption-Then-Embedding-Based data hiding scheme for medical images that combines cryptography and watermarking techniques. The proposed technique first encrypts the patient information using Advanced Encryption Standard-Galois/Counter Mode (AES-GCM) before embedding. The intensity-based image segmentation method is then used to select the Region of Non-interest (RONI) for embedding the encrypted patient information and the watermark using a Fused Transform-Based Method (FTBM). The experiments conducted in this study utilize a large dataset of medical data, The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD). The findings of this study demonstrate the efficacy of the proposed technique in safeguarding patient data while simultaneously preserving the quality of medical images. In comparison to existing techniques, the proposed approach demonstrates superior performance in terms of security, authenticity, and integrity.

Fuzzy-Enhanced Secure Messaging Framework for Smart Healthcare System

Fuzzy-Enhanced Secure Messaging Framework for Smart Healthcare System

Privacy‐preserving in the smart healthcare system using steganography and chaotic functions based on DNA

AbstractThe smart healthcare system is one of the Internet of Things‐based applications that are increasingly used nowadays. One of the requirements of this system is privacy‐preserving, which should protect the disclosure of the patient record contents. In the present paper, a combination of chaotic functions and a new DNA‐based steganography method, and image blocking are suggested to protect patients' privacy‐preserving. First, the image is blocked, and the initial key for the chaotic function will be gained by using the centers of the blocks. Then the data and image are transformed into DNA sequences. Finally, one of the randomly chosen DNA strands of data is XOR with the center of one randomly selected block and will be placed in one of the block pixels. The failure to send the initial key separately to generate random numbers, as well as the random selection of secret data bits and image pixels for steganography has increased the security of the proposed method. The simulation results not only indicate the quality of the stego‐image but also show better performance of the proposed method than the existing methods.

Using Medical Data and Clustering Techniques for a Smart Healthcare System

With the rapid advancement of information technology, both hardware and software, smart healthcare has become increasingly achievable. The integration of medical data and machine-learning technology is the key to realizing this potential. The quality of medical data influences the results of a smart healthcare system to a great extent. This study aimed to design a smart healthcare system based on clustering techniques and medical data (SHCM) to analyze potential risks and trends in patients in a given time frame. Evidence-based medicine was also employed to explore the results generated by the proposed SHCM system. Thus, similar and different discoveries examined by applying evidence-based medicine could be investigated and integrated into the SHCM to provide personalized smart medical services. In addition, the presented SHCM system analyzes the relationship between health conditions and patients in terms of the clustering results. The findings of this study show the similarities and differences in the clusters obtained between indigenous patients and non-indigenous patients in terms of diseases, time, and numbers. Therefore, the analyzed potential health risks could be further employed in hospital management, such as personalized health education control, personal healthcare, improvement in the utilization of medical resources, and the evaluation of medical expenses.

Blockchain and trusted reputation assessment-based incentive mechanism for healthcare services

Blockchain-based healthcare IoT technology research enhances security for smart healthcare services such as real-time monitoring and remote disease diagnosis. To incentivize positive behavior among participants within a blockchain-based smart healthcare system, existing efforts employ benefit distribution and reputation assessment methods to enhance performance. Yet, there remains a significant gap in multidimensional assessment strategies and consensus improvements in addressing complex healthcare scenarios. In this paper, we propose a blockchain and trusted reputation assessment-based incentive mechanism for healthcare services (BtRaI). BtRaI provides a realistic and comprehensive reputation assessment with feedback to motivate blockchain consensus node participation, thus effectively defending against malicious behavior in the healthcare service system. Specifically, BtRaI first introduces multiple moderation factors for comprehensive multidimensional reputation assessment and credibly records the assessment results on the blockchain. Then, we propose an improved PBFT algorithm, grounded in the reputation assessment, to augment blockchain consensus efficiency. Finally, BtRaI designs a token-based reward and punishment mechanism to motivate honest participation in the blockchain, inhibit potential misbehavior, and promote enhanced service quality in the healthcare system. Theoretical analysis and simulation experiments conducted across various scenarios demonstrate that BtRaI effectively suppresses malicious attacks in healthcare services, improves blockchain node fault tolerance rates, and achieves blockchain transaction processing efficiency within 0.5 s in a 100-node consortium chain. BtRaI’s reputation assessment and token incentive mechanism, characterized by realistic differentiation granularity and change curves, are well-suited for dynamic and complex healthcare service environments.

A secure blockchain framework for the internet of medical things

AbstractTechnological advancements in the Internet of Medical Things (IoMT) enable medical services to provide incredibly challenging real‐time functions. The proliferation of health sensors has improved patient monitoring, both for patient care and medical purposes, giving professionals more data for early diagnosis and treatment recommendations. Privacy and security are critical considerations for collecting and transferring data from these heterogeneous sensors across the database and the IoT network. This study leverages blockchain technology to improve Electronic Health Record (EHR) privacy and security. This article proposes a novel decentralized architecture using IoT and Blockchain technology for storing EHR and monitoring real‐time patient data. The proposed patient‐centric framework and architecture offer a straightforward solution that fulfills data integrity and anonymity and meets the data interoperability demands. Self‐contained encryption–decryption and primitive cryptographic techniques enhance the system's security. The proposed study models IoT health sensors such as body temperature, pulse rate, spO2, and Electromyogram (EMG) in a blockchain‐based IoT framework. The proposed system has been evaluated to determine that it can process and store Remote Patient Monitoring (RPM) data with encouraging results. The article provides performance, security, and threat analysis to contribute toward a robust, secure, and smart healthcare system using metrics such as latency, throughput, IoT sensor activity, and resource usage. Experimental results present that the proposed method beats the standard healthcare system regarding service quality and patient data monitoring.

Smart Healthcare System Using IoT, Cloud, and AI/ML

Smart Healthcare System Using IoT, Cloud, and AI/ML

A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients

An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medical assistance. Such human-centered smart healthcare systems can not only provide continuous medical assistance to diabetes patients but can also reduce overall medical expenses. In the last decade, machine learning has been successfully implemented to design more accurate and precise medical applications. In this paper, a smart sensing technologies-based architecture is proposed that uses AI and the Internet of Things (IoT) for continuous monitoring and health assistance for diabetes patients. The designed system senses various health parameters, such as blood pressure, blood oxygen, blood glucose (non-invasively), body temperature, and pulse rate, using a wrist band. We also designed a non-invasive blood sugar sensor using a near-infrared (NIR) sensor. The proposed system can predict the patient’s health condition, which is evaluated by a set of machine learning algorithms with the support of a fuzzy logic decision-making system. The designed system was validated on a large data set of 50 diabetes patients. The results of the simulation manifest that the random forest classifier gives the highest accuracy in comparison to other machine learning algorithms. The system predicts the patient’s condition accurately and sends it to the doctor’s portal.

Strategy maintenance in smart healthcare systems

BackgroundsThe size of medical strategies is expected to grow in conjunction with the expansion of modern diseases’ complexity. When a strategy includes more than ten statements, its manual management becomes very challenging, and in some cases, impossible. As a result, the updates they get may result in the unavoidable appearance of anomalies. This causes an interruption in the outflow of imperfect knowledge.MethodsIn this paper, we propose an approach called TAnom-HS to verify healthcare strategies. We focus on the management and maintenance, in a convenient and automatic way, of a large strategy to guarantee knowledge accuracy and enhance the efficiency of the inference process in healthcare systems.ResultsWe developed a prototype of our proposal and we applied it on some cases from the BioPortal repository. The evaluation of both steps of TAnom-HS proved the efficiency of our proposal.ConclusionTo increase ontologies expressiveness, a set of rules called strategy is added to it. TAnom-HS is a two-step approach that treats anomalies in healthcare strategies. Such a task helps to take automatic and efficient healthcare decisions.

A Softwarized Intrusion Detection System for IoT-Enabled Smart Healthcare System

The Internet of Things-enabled Smart Healthcare System (IoT-SHS) is a networked infrastructure of intelligent wearables, software applications, health systems, and services that continuously monitors and transmits patient-sensitive data using an open wireless channel. The conventional security mechanisms are unsuitable for detecting attacks in the dynamic IoT-SHS context due to resource limitations and heterogeneity in low-cost healthcare devices. Deep Learning (DL) solutions for Intrusion Detection System (IDS) and softwarization of the network has the potential to achieve secure network services in the IoT-SHS environment. Motivated by the aforementioned discussion, we propose an intelligent softwarized IDS for protecting the critical infrastructure of the IoT-SHS ecosystem. Specifically, the DL-based IDS is designed using a hybrid cuda Long Short-Term Memory Deep Neural Network (cuLSTM-DNN) algorithm to assist network administrators in efficient decision-making for the generated intrusions. To further bolster the system’s resilience, we suggest a deployment architecture for the proposed CUDA-powered IDS using OpenStack Tacker in a real SDN environment, ensuring that virtual machines can directly utilize the host’s NVIDIA GPU, thereby streamlining and enhancing the network’s operational efficiency. The experimental results using the CICDDoS2019 dataset confirm the effectiveness of the proposed framework over some baseline and recent state-of-the-art techniques.

Novel Edge Computing-Based Privacy-Preserving Approach for Smart Healthcare Systems in the Internet of Medical Things

Novel Edge Computing-Based Privacy-Preserving Approach for Smart Healthcare Systems in the Internet of Medical Things

Strengthening Privacy and Data Security in Biomedical Microelectromechanical Systems by IoT Communication Security and Protection in Smart Healthcare

Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings forth a complex array of security and privacy challenges intrinsic to IoT communications within smart healthcare ecosystems, demanding comprehensive scrutiny. In this manuscript, we embark on an extensive analysis of the intricate security terrain associated with IoT communications in the realm of BioMEMS, addressing a spectrum of vulnerabilities that spans cyber threats, data manipulation, and interception of communications. The integration of real-world case studies serves to illuminate the direct repercussions of security breaches within smart healthcare systems, highlighting the imperative to safeguard both patient safety and the integrity of medical data. We delve into a suite of security solutions, encompassing rigorous authentication processes, data encryption, designs resistant to attacks, and continuous monitoring mechanisms, all tailored to fortify BioMEMS in the face of ever-evolving threats within smart healthcare environments. Furthermore, the paper underscores the vital role of ethical and regulatory considerations, emphasizing the need to uphold patient autonomy, ensure the confidentiality of data, and maintain equitable access to healthcare in the context of IoT communication security. Looking forward, we explore the impending landscape of BioMEMS security as it intertwines with emerging technologies such as AI-driven diagnostics, quantum computing, and genomic integration, anticipating potential challenges and strategizing for the future. In doing so, this paper highlights the paramount importance of adopting an integrated approach that seamlessly blends technological innovation, ethical foresight, and collaborative ingenuity, thereby steering BioMEMS towards a secure and resilient future within smart healthcare systems, in the ambit of IoT communication security and protection.

An Improved AI-Based Secure M-Trust Privacy Protocol for Medical Internet of Things in Smart Healthcare System

Medical Internet of Things (MIoT) is a rapidly growing field that promises to revolutionize health care. The ability to connect devices and collect data from them has the potential to transform the way it monitors and treat patients. However, the security and privacy of this data is a major concern. The cryptography and other conventional methods for security in resource constrained MIoT has several shortcomings. First, it does not provide adequate protection for patient data. Second, it is not designed to work with the large number of devices and huge volumes of data that are typically generated in MIoT applications. In this paper, Secure M-Trust Privacy protocol (SMP) has been designed to address these issues. The SMP protocol uses a combination of trust, cryptographic and Machine Learning techniques to provide security and privacy for data in transit. The SMP protocol has been designed to work with the Smart Health Care Monitoring System to provide a secure and private communication channel between devices in the system. The SMP protocol is an improvement over the existing security and privacy protocols for medical data. The simulation results proves that the SMP protocol is more efficient and scalable than the existing protocols. The SMP protocol is a valuable addition to the MIoT landscape and can help improve the privacy of data exchanged between medical devices.

Time-Sensitive Networking-Driven Deterministic Low-Latency Communication for Real-Time Telemedicine and e-Health Services

With the convergence of telemedicine and electronic health (e-health) services, Consumer Internet of Things (CIoT) enabled smart healthcare has attracted much attention from researchers. Telemedicine and e-health care can help to eliminate geographical barriers and provide high-quality medicine services. Whereas, deterministic low-latency communication is a major problem in smart healthcare system with commercial network, since the latency can affect the process of diagnosis and treatment while producing inaccuracy. To address this issue, Time Sensitive Networking (TSN) is introduced in this article for bounded lowlatency network services. First, we present a Digital Twin based TSN (DTTSN) framework where digital modeling and intelligent simulation are performed under a virtual environment. Second, a digital twin scheduling model is created to predict the forwarding delay. In order to enhance model adaption in different scenarios, cycle generative adversarial network (CycleGAN) is adopted to train the scheduling model. Finally, the optimal routing strategy deduced in digital twin network is fed back to physical network for automated configuration. Simulation results verify that our digital twin network can promote visibility of operations and predictions of physical network scheduling and routing, while optimizing delay and balancing link load.

Cryptographic evidence-based cybersecurity for smart healthcare systems

Cryptographic evidence-based cybersecurity for smart healthcare systems