IoT-based Healthcare Applications Research Articles

Secure and energy-efficient data transmission framework for IoT-based healthcare applications using EMCQLR and EKECC

Secure and energy-efficient data transmission framework for IoT-based healthcare applications using EMCQLR and EKECC

HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications.

The swift advancement of the Internet of Things (IoT), coupled with the growing application of healthcare software in this area, has given rise to significant worries about the protection and confidentiality of critical health data. To address these challenges, blockchain technology has emerged as a promising solution, providing decentralized and immutable data storage and transparent transaction records. However, traditional blockchain systems still face limitations in terms of preserving data privacy. This paper proposes a novel approach to enhancing privacy preservation in IoT-based healthcare applications using homomorphic encryption techniques combined with blockchain technology. Homomorphic encryption facilitates the performance of calculations on encrypted data without requiring decryption, thus safeguarding the data's privacy throughout the computational process. The encrypted data can be processed and analyzed by authorized parties without revealing the actual contents, thereby protecting patient privacy. Furthermore, our approach incorporates smart contracts within the blockchain network to enforce access control and to define data-sharing policies. These smart contracts provide fine-grained permission settings, which ensure that only authorized entities can access and utilize the encrypted data. These settings protect the data from being viewed by unauthorized parties. In addition, our system generates an audit record of all data transactions, which improves both accountability and transparency. We have provided a comparative evaluation with the standard models, taking into account factors such as communication expense, transaction volume, and security. The findings of our experiments suggest that our strategy protects the confidentiality of the data while at the same time enabling effective data processing and analysis. In conclusion, the combination of homomorphic encryption and blockchain technology presents a solution that is both resilient and protective of users' privacy for healthcare applications integrated with IoT. This strategy offers a safe and open setting for the management and exchange of sensitive patient medical data, while simultaneously preserving the confidentiality of the patients involved.

LSO-CSL: Light spectrum optimizer-based convolutional stacked long short term memory for attack detection in IoT-based healthcare applications

One of the new technological issues of this century is cybersecurity in the health domain. Numerous types of research have been undertaken recently to identify weak points and gaps in healthcare facilities’ and hospitals’ cyber-resilience. Also, different evaluation techniques are used to identify cyber-defense tactics and mitigation methods. During a crisis, however, the health sector is not immune to scientific or professional advancement since it must concentrate all of its resources to treating patients and saving lives. Thus, we proposed Multi Step Convolutional neural network Stacked Long short term memory architecture (MSCSL) for attack detection in Internet of Things detection (IoT) based healthcare applications. Here, using Light Spectrum Optimizer (LSO) the MSCSL’s hyperparameter is optimized. The designed activation function combines the best features of the rectified Liner Unit (ReLU), which is more data-adaptive and has a lower computation complexity. In MSCSL architecture the ReLU is replaced with the Leaky Learnable ReLU (LeLeLU) to improve the data adaptability and minimize the computational complexity. The proposed model is evaluated based on different datasets such as the ECU-Internet of Health Things (IoHT), TON-IoT, and Supervisory Control and Data Acquisition International Electrotechnical Commission (SCADA IEC 60870-5-104) datasets. This dataset includes port scans, brute force, and DoS attacks. When evaluated using different metrics, the proposed method achieves a precision of 98.5%, accuracy of 98.85%, F1-score of 98.74%, recall of 96.3%, Kappa score of 98.9%, and Mathew Correlation coefficient (MCC) values of 0.92, which is higher compared to other existing techniques.

Early Diagnosis of Tuberculosis Using Deep Learning Approach for IOT Based Healthcare Applications.

In the modern world, Tuberculosis (TB) is regarded as a serious health issue with a high rate of mortality. TB can be cured completely by early diagnosis. For achieving this, one tool utilized is CXR (Chest X-rays) which is used to screen active TB. An enhanced deep learning (DL) model is implemented for automatic Tuberculosis detection. This work undergoes the phases like preprocessing, segmentation, feature extraction, and optimized classification. Initially, the CXR image is preprocessed and segmented using AFCM (Adaptive Fuzzy C means) clustering. Then, feature extraction and several features are extracted. Finally, these features are given to the DL classifier Deep Belief Network (DBN). To improve the classification accuracy and to optimize the DBN, a metaheuristic optimization Adaptive Monarch butterfly optimization (AMBO) algorithm is used. Here, the Deep Belief Network with Adaptive Monarch butterfly optimization (DBN-AMBO) is used for enhancing the accuracy, reducing the error function, and optimizing weighting parameters. The overall implementation is carried out on the Python platform. The overall performance evaluations of the DBN-AMBO were carried out on MC and SC datasets and compared over the other approaches on the basis of certain metrics.

Noninvasive, Wearable Multi Biosensors for Continuous, Long-term Monitoring of Blood Pressure via Internet of Things Applications

Continuous blood pressure (BP) monitoring through various physical stress tests is important for the prediction of hypertension and cardiovascular mortality. This study proposes a multimodal wearable biosensor device for continuous BP monitoring through various tests during exercise and internet of things (IoT) applications. We evaluated three pulse transit time (PTT)-BP estimation models to identify the best mode for predicting BP response to exercise. Moreover, the proposed BP monitoring system was designed to connect to the cloud server for health data processing and management. The efficacy of the proposed device was validated by monitoring the natural variability of BP in various cycling tests. Thus, BP estimation based on the impedance plethysmography waveform and arterial impedance offers superiority compared to other conventional PTT estimation methods. These results suggest the feasibility of the multimodal biosensor device for continuous, long-term BP monitoring in remote diagnosis and management with IoT-based healthcare applications.

Chipless RFID Sensors for IoT-Based Healthcare Applications: A Review of State of the Art

Currently, there is a great demand for low-cost solutions in connected healthcare and real-time wireless patient monitoring remotely. Radio Frequency Identification (RFID) is a rapidly emerging wireless technique for remote data capturing utilized in multifarious applications such as healthcare, automated identification, object tracking, security and authentication, food packaging, and automobile tracking. The projected cost of conventional RFID technology hampers it from being used widely. Chipless RFID sensors with the Internet of Things (IoT) technology have the full potential to meet the requirements of the growing demand. With the development of present technologies, the IoT-based solutions are no longer a challenging vision that offers various potential benefits to today’s world, especially healthcare which is an encouraging field of IoT. The design and development of chipless sensors for health monitoring have gathered the attention of the researchers in last few years. This article provides a comprehensive review of chipless RFID sensors that play a vital role in cost-effective healthcare applications. The working principle of a chipless sensing system, fabrication techniques, and classifications of different chipless RFID sensors along with the smart sensing materials are highlighted. This paper throws light on numerous chipless RFID sensors for IoT-based healthcare applications including their challenges and future directions. In addition to that, a comparison table of recent developments in the area of IoT-based healthcare is prepared.

An Experimental Study of Distributed Denial of Service and Sink Hole Attacks on IoT based Healthcare Applications

An Experimental Study of Distributed Denial of Service and Sink Hole Attacks on IoT based Healthcare Applications

EPPDA: An Efficient and Privacy-Preserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare Applications

Nowadays, IoT technology is used in various application domains, including the healthcare, where sensors and IoT enabled medical devices exchange data without human interaction to securely transmit collected sensitive healthcare data towards healthcare professionals to be reviewed and take proper actions if needed. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. In healthcare applications, many miniaturized devices are exploited for healthcare data collection and transmission. Thus, there is a need for secure data aggregation while preserving the data integrity and privacy of the patient. For that, the security, privacy, and aggregation of health data are very important aspects to be considered. This paper proposes a novel secure data aggregation scheme called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation. EPPDA, also, uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The major advantage of homomorphic encryption is allowing complex mathematical operations to be performed on encrypted data without knowing the contents of the original plain data. The proposed system is developed using MySignals HW V2 platform. Security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare.

FoG-Oriented Secure and Lightweight Data Aggregation in IoMT

Internet of Medical Things (IoMT) is becoming an essential part of remote health monitoring due to the evolution of medical wireless sensors and intelligent communication technologies. IoT-based healthcare applications are employed in the medical centers to provide continuous health monitoring of a patient. However, recent smart medical devices have limited resources to handle the huge amount of healthcare data. IoMT faces several challenging issues, like security, privacy, anonymity, and interoperability. In data aggregation and communication, the privacy and security of medical information is a demanding task. Therefore, we proposed a suitable scheme to overcome the limitations of existing research studies. This paper presents an Efficient and Secure Data Transmission and Aggregation (ESDTA) scheme to enhance aggregation efficiency and data security. Our proposed work provides secure data aggregation and data forwarding of healthcare parameter values by employing the Secure Message Aggregation (SMA) algorithm and Secure Message Decryption (SMD) algorithm at the Mobile Node (MN) and Fog Node (FN), respectively. From a security perspective, the proposed scheme preserves the data integrity and also protect against several security threats like data fabrication and replay attack. The proposed scenario is simulated through simulation tool NS 2.35. The simulation results prove that aggregation at the MN effectively reduces transmission and communication costs. Furthermore, the effective computation at the FN minimizes the storage and computational cost at the cloud server. Thus, the analysis of the proposed scheme shows the supremacy of our proposed work. We compare our scheme with other related secure data aggregation-based schemes in terms of communication cost, energy consumption, resilience, storage and computational cost.

Security attacks in S-WBANs on IoT based Healthcare Applications

The internet exists as a global interconnection network for information sharing, commerce, and entertainment. The usage of internet has grown tremendously over the past decade in the field of E- health care monitoring system. In E- health care monitoring systems, Smart Wireless Body Area Networks (S-WBANs) is one among such technologies and is being explored. S-WBANs are primarily used as a smart object with computer-assisted rehabilitation. They serve as a remote monitoring service in smart electronic healthcare to continuously measure the vital parameters like heart rate, insulin level, body temperature, etc. They are thereby providing doctors with early detection of a patient’s medical conditions. However, sharing of sensitive personal data over a wireless network requires a high level of security. Since any breach in the system security will lead to a direct violation of the patient’s privacy. In order to address the security vulnerability of the existing system, in this paper a block chain based Smart Wireless Body Area Networks has been proposed to provide better security with enhanced privacy and access control for E- health care monitoring system. Moreover, the proposed system enhances the scalability and provides mitigation against all the security attacks in IoT Environment.

New efficient M2C and M2M mutual authentication protocols for IoT-based healthcare applications

With the rapid advancement of heterogeneous wireless technologies and their proliferation in ambient connected objects, the Internet of Things (IoT) is a paradigm that revolutionizes communication between people/objects. Communication between connected objects is achieved via various communication modes, including Machine-to-Machine (M2M) and Machine-to-Cloud (M2C). In the medical field, monitoring devices help to collect, exchange and process patient health parameters, and are employed in open and unprotected environments, which expose them to various attacks. For this reason, providing high levels of security and privacy become crucial, and a first requirement to ensure this is authentication. In this paper, we propose three new lightweight, efficient authentication protocols for IoT-based healthcare applications. We formally verify them using AVISPA and ProVerif automated tools. For each protocol, we provide a security analysis and a performance evaluation that we compare to related existing proposals.

Algebraic secret sharing using privacy homomorphisms for IoT-basedhealthcare systems

Healthcare industry is one of the promising fields adopting the Internet of Things (IoT) solutions. In this paper, we study secret sharing mechanisms towards resolving privacy and security issues in IoT-based healthcare applications. In particular, we show how multiple sources are possible to share their data amongst a group of participants without revealing their own data to one another as well as the dealer. Only an authorised subset of participants is able to reconstruct the data. A collusion of fewer participants has no better chance of guessing the private data than a non-participant who has no shares at all. To realise this system, we introduce a novel research upon secret sharing in the encrypted domain. In modern healthcare industry, a patient's health Article often contains data acquired from various sensor nodes. In order to protect information privacy, the data from sensor nodes is encrypted at once and shared among a number of cloud servers of medical institutions via a gateway device. The complete health Article will be retrieved for diagnosis only if the number of presented shares meets the access policy. The retrieval procedure does not involve decryption and therefore the scheme is favourable in some time-sensitive circumstances such as a surgical emergency. We analyse the pros and cons of several possible solutions and develop practical secret sharing schemes for IoT- based healthcare systems.

Human Vital Physiological Parameters Monitoring: A Wireless Body Area Technology Based Internet of Things

Human vital physiological parameters (HVPP) monitoring with embedded sensors integration has improved the smart system technology in this era of a ubiquitous platform. Several IoT-based healthcare applications have been proposed for remote health monitoring. Most of the devices developed require one on one contact with doctors before any medical diagnosis is undertaken. Thereby, make it difficult for frequent visitation to the health center. In this paper, embedded heartbeat and temperature sensors for remote monitoring have been developed using Arduino lily as the system controller and processing unit. The Bluetooth low power enables with Android mobile apps is used for remote monitoring and communication of HVPP in a real time. This gives medical personnel and individual customers opportunity of monitoring their vital physiological parameters such as heartbeat rate and body temperature. However, it moderates sudden attack of chronic ailment like hypertension and reduces congestion of patient in the hospitals.

A Software Requirement Engineering Technique Using OOADA-RE and CSC for IoT Based Healthcare Applications

A Software Requirement Engineering Technique Using OOADA-RE and CSC for IoT Based Healthcare Applications

A Wireless Implantable Sensor Design With Subcutaneous Energy Harvesting for Long-Term IoT Healthcare Applications

In this paper, a wireless implantable sensor prototype with subcutaneous solar energy harvesting is proposed. To evaluate the performance of a flexible solar panel under skin, ex-vivo experiments are conducted under natural sunlight and artificial light sources. The results show that the solar panel covered by a 3 mm thick porcine flap can output tens of microWatts to a few milliWatts depending on the light conditions. The subcutaneous solar energy harvester is tested on different body parts, which suggests the optimal position for the harvester to implant is between neck and shoulder. A wireless implantable system powered by the subcutaneous energy harvester is presented, which consists of a power management circuit, a temperature sensor, and a Bluetooth low energy module. An application is developed for data visualization on mobile devices, which can be a gateway for future IoT-based healthcare applications. The entire device is embedded in a transparent silicone housing (38 mm $\times$ 32 mm $\times$ 4 mm), including a 7 mAh rechargeable battery for energy storage. The average power consumption of the implants is about $30~\mu \text{W}$ in a 10 min operation cycle. With the subcutaneous solar energy harvester, the self-powered operation of the implantable sensor prototype is demonstrated by long-term experimental results. Two worst-case scenarios (no exposure to light and battery depletion) are considered with ex-vivo experiment simulations.

Majority-consensus fusion approach for elderly IoT-based healthcare applications

Nowadays, tremendous growth of Internet of Things (IoT) applications is seen in smart environments such as medical remote care applications which are crucial due to the general aging of the population. With the recent advancements in IoT-based healthcare technologies, activity recognition can be used as the key part of the intelligent healthcare systems to monitor elderly people to live independently at homes and promote a better care. Recently, the evidence theory and its derivates approaches began to take place in the fields of activity recognition in these smart systems. However, these approaches are generally inconsistent with the probability calculus due to the lower and upper probability bounds considering the combined evidences. To overcome these challenges and to get more precisely the reconcilement between the evidence theory with the frequentist approach of probability calculus, this work proposes a new methodology for combining beliefs, addressing some of the disadvantages exhibited by the evidence theory and its derivatives. This methodology merges the non-normalized conjunctive and the majority rules. The proposed rule is evaluated in numerical simulation case studies involving the activity recognition in a smart home environment. The results show that this strategy produces intuitive results in favor of the more committed hypothesis.