Remote Health Monitoring Applications Research Articles

Smart spirits: an integrated IoT-based liquor and health monitoring system

This project entails the development of a comprehensive health monitoring system utilizing Arduino Uno, a range of sensors (including alcohol, pulse, and DHT11), an LCD display, and a GSM module for data transmission to the Thingspeak application. Its objective is to monitor vital health parameters such as temperature, humidity, pulse rate, and alcohol levels in real-time, accessible via the Thingspeak mobile app. Key components include an Arduino Uno microcontroller, DHT11 sensor for temperature and humidity, pulse sensor for heart rate, alcohol sensor for detecting alcohol levels, LCD display for immediate feedback, and a GSM module for remote data transmission. The user-friendly Thingspeak app serves as an interface for seamless health metric monitoring. The system operates by collecting sensor data, displaying it locally on the LCD screen, and simultaneously transmitting it to the Thingspeak app via GSM communication. Users can then access the app to visualize and monitor their health metrics in real-time, allowing for prompt interventions or medical assistance if needed. The code implementation integrates sensor readings, LCD display control, and Thingspeak functionality. Leveraging Thingspeak's cloud-based platform enhances usability and accessibility, making remote health monitoring feasible. This system offers a versatile solution for individuals looking to efficiently track and manage their well-being, thanks to the integration of Arduino Uno, sensors, Thingspeak features, and GSM communication, making it suitable for both local and remote health monitoring applications.

Resource-aware fog service placement using deferred acceptance in edge computing

Fog computing aims at reducing network latency, bandwidth and processing delay by bringing computation resources closer to the data sources. Modern IoT applications, such as smart surveillance systems, remote health monitoring applications, ambient-aware applications etc., are time-critical and computationally intensive. For such applications, it is ideal to consider an intermediate layer of processing i.e a fog layer in addition to cloud. However, processing the workload of such IoT applications with an intermediate layer of fog nodes has several challenges. The significant challenges include the mobility of end-user nodes, dynamic patterns of workload generation leading to highly-variant resource requirements and sensitive quality of service (QOS) requirements. Hence a dynamic approach is required to precisely map the service requests from end-user devices to a suitable fog node. The optimality of a fog node for service placement depends on several factors, including location, resource utilization rates such as CPU, Memory and Disk utilization % and energy consumption. Several meta-heuristic techniques and evolutionary algorithms have been proposed in the past for optimal service placement among fog nodes. This paper investigates the applicability of a game theory-based approach called Deferred acceptance (DA) for the same. The DA algorithm, originally designed as a stable matching mechanism for the marriage market is adapted for service placement problems in fog-based IoT systems. The effectiveness of optimized service placement through the DA approach is validated experimentally. The experiment is carried out by creating a test bed that mimics a fog computing-based intelligent traffic management application. The proposed approach's performance is evaluated through parameters such as the startup time for the DA approach to produce outcomes, total execution time and handshake latency. The numerical results of the DA approach are compared with those of a simple scheduling algorithm ( FIFO) and a basic genetic algorithm-based approach. The DA approach appears to perform on par with the genetic algorithm in terms of start-up time, but exceeds the genetic algorithm with respect to stability of the outcomes produced.

Current security and privacy posture in wireless body area networks

Wireless Body Area Networks (WBANs) have emerged as a promising technology for remote health monitoring and healthcare applications. However, ensuring the security and privacy of sensitive health data in WBANs is crucial to foster user trust and prevent unauthorized access or data breaches. This paper provides an overview of the key challenges, techniques, and research gaps in WBAN security and privacy. The findings indicate that the challenges in WBAN security and privacy include resource constraints, compatibility issues, privacy concerns, dynamic network environments, security and usability trade-offs, emerging threat landscape, and user awareness and education. To address these challenges, various security techniques have been developed, such as authentication and authorization mechanisms, encryption, access control, secure communication protocols, intrusion detection systems, and privacy-preserving data handling techniques. Despite the progress made, there are research gaps that require further investigation. These research gaps include the development of secure and lightweight authentication mechanisms, privacy-preserving data analysis techniques, trust and security management frameworks, resilience to insider threats, security of data aggregation and fusion, user-centric security designs, and addressing legal and ethical considerations. Addressing these research gaps and challenges requires collaboration between researchers, device manufacturers, policymakers, and end-users. Ongoing research and innovation are necessary to develop robust security techniques, privacy-enhancing technologies, and user-friendly solutions tailored for WBANs. Additionally, compliance with privacy regulations, user education, and awareness are critical for responsible and ethical use of WBANs.

Платформа поглибленого навчання для аналізу лікувальних факторів в інтернеті медичних речей

Поява технологій інтернету речей IoT (Internet-of-Things), таких які використовуються у дистанційних застосунках для контролю здоров’я, призвели до революційних змін у традиційній медичній допомозі та лікуванні. Крім того, підхід, застосований для отримання глибинного аналізу, на основі ретельного дослідження елементів способу життя та діяльності, є критично важливим для успіху послуг спеціальної медичної допомоги та профілактики захворювань. Алгоритми відбору та класифікації розумних даних дозволяють досліджувати захворювання та прогнозувати аномальні стани здоров’я. Стратегія згорткових нейронних мереж CNN (convolutional neural network) застосовується для передбачення таких аномалій, оскільки вона дозволяє успішно визначити знання, які є важливими для прогнозування захворювання на основі нечітких записів медичної картки хворого. Навпаки, якщо використовується повністю зв’язана топологія мережі, стратегія CNN потребує величезного об’єму пам’яті. Крім того, аналіз складності моделі може виникати у зв’язку зі збільшенням кількості шарів. Таким чином, запропоновано стратегію визначення та прогнозування цілі CNN на основі коефіцієнта кореляції Пірсона PCC (Pearson correlation coefficient), і роботи стандартного шаблону для розгляду цих недоліків моделі CNN. Вона побудована в межах цієї структури і застосовується для цілей класифікації. В початковому прихованому шарі вибираються найбільш важливі фактори, пов’язані зі станом здоров’я, а в наступному шарі здійснюється аналіз коефіцієнта кореляції для розділення факторів стану здоров’я на позитивно і негативно корельовані групи. Аналіз виявлення регулярних структур серед розподілених параметрів стану здоров’я також виявляє поведінку регулярних структур. Результатом цієї моделі є розподіл на фактори надмірної ваги, підвищеного тиску та фактори, що пов’язані з діабетом, при відомих рівнях кореляції. Для зменшення впливу парадигми відкриття знань, що є типовим для CNN, використано два окремих набори даних. Експериментальні результати показали, що пропонована модель переважає три інші методи машинного навчання, потребуючи менших розрахункових витрат.

Low Power IoT based Implementation ECG & Health Monitoring System.

The demand for efficient and cost-effective health monitoring systems is increasing rapidly due to the growing aging population and rising health concerns. In this context, the Low Power IoT-Based Implementation ECG & Health Monitoring System proposed in this project is a significant step forward. The system incorporates the MAX30100 sensor and ESP32 to monitor and track the user's electrocardiogram (ECG) signals and various other health parameters, such as heart rate, blood oxygen levels, and temperature. The proposed system is highly reliable, accurate, and portable, making it an ideal choice for health monitoring applications.
 The MAX30100 sensor is a low-cost, highly sensitive optical sensor that measures blood oxygen levels and heart rate through the fingertip. The ESP32 is a low-power, high-performance microcontroller with built-in Wi-Fi and Bluetooth capabilities, enabling the proposed system to be connected to the internet and transmit data wirelessly. In addition, the system features an inbuilt LCD that displays the readings and a Telegram bot that provides real-time remote monitoring of the user's health data.
 The proposed system's power consumption is incredibly low, making it suitable for long-term health monitoring applications. Moreover, the system's low-power consumption also ensures extended battery life, making it highly efficient and cost-effective.
 Overall, the proposed Low Power IoT-Based Implementation ECG & Health Monitoring System offers a highly reliable and efficient solution for health monitoring applications. The combination of the MAX30100 sensor and ESP32 microcontroller provides highly accurate measurements, and the system's low-power consumption ensures extended battery life. The Telegram bot and inbuilt LCD offer real-time monitoring capabilities, making it an ideal choice for remote health monitoring applications.

A 3D Nanomaterials‐Printed Wearable, Battery‐Free, Biocompatible, Flexible, and Wireless pH Sensor System for Real‐Time Health Monitoring

AbstractThe pH sensing devices can provide important health information with applications in infection detection, disease diagnosis, and personalized medicine. However, these devices are often expensive with modest flexibility and require bulky readout instruments, thus inappropriate for wearable, remote, and continuous health monitoring applications. Herein, an integrated, miniaturized, modular, wearable, battery‐free, biocompatible, flexible, 3D‐printed (WB2F3D) sensor system for on‐demand, continuous, wireless, and real‐time pH monitoring is proposed, developed, and fully characterized. The 3D‐printing of nanomaterials on skin‐like flexible substrates is innovatively applied to enable multimaterial and multilayer printing of the sensors, reusable electronic/communication circuity, and antennas in a tailorable, low‐cost, and time‐efficient manner. The battery‐free and flexible readout system is designed to enable wireless and on‐demand energy and data transmission for continuous and real‐time pH monitoring. This sensor system exhibits high sensitivity (≈|51.76| mV pH−1), specificity, repeatability, reproducibility toward various pH ranges (3.0–10.0), excellent mechanical flexibility, and outstanding biocompatibility (cell viability > = 90%). It successfully demonstrates the pH change monitoring in an ex situ hydrogel‐based wound model. The WB2F3D sensor system is envisioned to provide an integrated platform for accurate, on‐demand, battery‐free, wireless, and real‐time human health monitoring, and another step toward personalized medicine.

New Wearable Technologies and Devices to Efficiently Scavenge Energy from the Human Body: State of the Art and Future Trends

Wearable technology represents a new technological paradigm for promoting physical activity, enabling monitoring of performances and athletic gestures. In addition, they can be employed for remote health monitoring applications, allowing continuous acquisition of users’ vital signs directly at home, emergency alerting, and computer-assisted rehabilitation. Commonly, these devices depend on batteries which are not the better option since researchers aim for dispositive who need minimal human intervention. Energy harvesting devices can be useful to extract energy from the human body, especially by integrating them into the garments, giving health monitoring devices enough energy for their independent operation. This review work focuses on the main new wearable technologies and devices to scavenge energy from the human body. First, the most suitable energy sources exploitable for wearable applications are investigated. Afterward, an overview of the main harvesting technologies (piezoelectric, triboelectric, thermoelectric, solar fabrics, and hybrid solution) is presented. In detail, we focused on flexible and thin textiles with energy harvesting capability, allowing easy integration into clothes fabric. Furthermore, comparative analyses of each harvesting technology are proposed, providing useful insights related to the best technologies for developing future self-sustainable wearable devices. Finally, a comparison between our review work and similar ones is introduced, highlighting its strengths in completeness and specificity.

Realtime PPG based respiration rate estimation for remote health monitoring applications

Among the many vital parameters of human body, breath rate monitoring is paramount to detect the symptoms of several respiratory diseases such as sleep apnea syndrome, chronic obstructive pulmonary disease (COPD), and asthma. Hence, this paper puts forward a novel framework to measure the respiration rate (RR) using the reflective type photoplethysmogram (PPG) signals acquired using an inexpensive and easy-to-use wearable device. Extracting the respiration induced amplitude variations (RIAV) from PPG signal using the incremental merge segmentation (IMS) algorithm, the proposed approach shows that robust RR estimation in realtime is feasible through low cost Cortex-M4 microcontroller. Using the sliding window approach augmented with adaptive thresholding technique that can deal with motion artifacts, we show that robust RR estimation is viable using reflectance type PPG, which is largely unexplored. Moreover, to handle the non-uniform nature of RIAV signal, this work employs uniform interval interpolation technique and removes the non-respiratory frequencies using finite impulse response (FIR) band-pass filter. Through the fast Fourier transform (FFT) analysis of regular interval RIAV sequence, the dominant frequency corresponding to the RR is extracted. The performance of the proposed scheme is validated not only on two publicly available PPG datasets but also on a custom designed experimental setup integrated with the smartphone. The experimental results highlight that our approach can achieve a RR estimation accuracy of 1 bpm deviation against the ground truth.

Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis.

BackgroundPhotoplethysmography is a noninvasive and low-cost method to remotely and continuously track vital signs. The Oura Ring is a compact photoplethysmography-based smart ring, which has recently drawn attention to remote health monitoring and wellness applications. The ring is used to acquire nocturnal heart rate (HR) and HR variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noise. Therefore, a validity assessment of the parameters is required in everyday settings.ObjectiveThis study aims to evaluate the accuracy of HR and time domain and frequency domain HRV parameters collected by the Oura Ring against a medical grade chest electrocardiogram monitor.MethodsWe conducted overnight home-based monitoring using an Oura Ring and a Shimmer3 electrocardiogram device. The nocturnal HR and HRV parameters of 35 healthy individuals were collected and assessed. We evaluated the parameters within 2 tests, that is, values collected from 5-minute recordings (ie, short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland–Altman plot were used to compare the measurements of the 2 devices.ResultsOur findings showed low mean biases of the HR and HRV parameters collected by the Oura Ring in both the 5-minute and average-per-night tests. In the 5-minute test, the error variances of the parameters were different. The parameters provided by the Oura Ring dashboard (ie, HR and root mean square of successive differences [RMSSD]) showed relatively low error variance compared with the HRV parameters extracted from the normal interbeat interval signals. The Pearson correlation coefficient tests (P<.001) indicated that HR, RMSSD, average of normal heart beat intervals (AVNN), and percentage of successive normal beat-to-beat intervals that differ by more than 50 ms (pNN50) had high positive correlations with the baseline values; SD of normal beat-to-beat intervals (SDNN) and high frequency (HF) had moderate positive correlations, and low frequency (LF) and LF:HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% CIs; however, SDNN, LF, HF, and LF:HF ratio had relatively wider 95% CIs. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P<.001), and the LF:HF ratio had a moderate positive relationship (P<.001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters.ConclusionsThe Oura Ring could accurately measure nocturnal HR and RMSSD in both the 5-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the 5-minute test. In contrast, the LF and LF:HF ratio of the ring had high error rates in both tests.

On-Body Edge Computing Through E-Textile Programmable Logic Array

E-textiles have received tremendous attention in recent years due to the capability of integrating sensors into a garment, enabling high-precision sensing of the human body. Besides sensing, a number of solutions for e-textile garments have also integrated wireless interfaces, allowing sensing data to be transmitted, and also inbuilt capacitive touch sensors, allowing users to provide instructions. While this has provided a new level of sensing that can result in unprecedented applications, there has been little attention placed around on-body edge computing for e-textiles. This study focuses on the need for a noninvasive and remote health-monitoring solution with inbuilt on-body edge computing, and how enabling such sensing and computing capabilities in a fabric environment can act as a new method for healthcare monitoring through the use of embedded computing intelligence in smart garments. Facilitating computing in e-textiles can result in a new form of on-body edge computing, where sensor information is processed very close to the body before being transmitted to an external device or wireless access point. This form of computing can provide new security and data privacy capabilities and, at the same time, provide opportunities for new energy-harvesting mechanisms to process the data through the garment. This study proposes this concept through embroidered programmable logic arrays (PLAs) integrated into e-textiles. In the same way that PLAs have programmable logic circuits by interconnecting different AND, NOT, and OR gates, we propose e-textile–based gates that are sewn into a garment and connected through conductive thread stitching. Two designs are proposed, and this includes single- and multi-layered PLAs. Experimental validations have been conducted at the individual gates and the entire PLA circuits to determine the voltage utilization and logic computing reliability. The multilayered PLA garment superseded the single-layered garment with higher levels of accuracy in the yielded results due to the enhanced design layout, which reduces the potential for short circuits and errors occurring. Our proposed approach can usher in a new form of on-body edge computing for e-textile garments for future wearable technologies, and, in particular, with the current pandemic that requires noninvasive remote health-monitoring applications.

Printed Structural Temperature Monitoring Embedded in Multi-Process Hybrid Additive Manufacturing

A printed and built-in geometry with a capability of detecting temperatures over a wide range are highly sought-after characteristics for a structural health monitoring temperature sensor. In this work, we describe a printable copper-graphene electronic sensor for temperature monitoring with real-time deployment into a printed metal structural component. The smart component is manufactured using a combination of three additive processes: a direct-ink writing, laser powder bed fusion (LPBF), and ultrasonic additive manufacturing (UAM). The sensor is printed on a flexible ceramic substrate, embedded in the stainless steel LPBF component and capped with a UAM aluminum layer. The sensor exhibits a high resistance temperature sensitivity in a wide temperature detection range, with the performance capping at 500°C. These results, and the demonstrated ability to read temperature wirelessly, provide a path for remote structural health monitoring applications for additively manufactured components.

A novel algorithm for minute ventilation estimation in remote health monitoring with magnetometer plethysmography

PurposeThe purpose of this study was to evaluate the accuracy of minute ventilation (V˙E) estimation using a novel method based on a non-linear algorithm coupled with cycle-based features. The experiment protocol was well adapted for remote health monitoring applications by exploiting data streams from respiratory magnetometer plethysmography (RMP) during different physical activity (PA) types. Methods Thirteen subjects with an age distribution of 24.1±3.4 years performed thirteen PA ranging from sedentary to moderate intensity (walking at 4 and 6 km/h, running at 9 and 12 km/h, biking at 90 W and 110 W). In total, 3359 temporal segments of 10s were acquired using the Nomics RMP device while the iWorx spirometer was used for reference V˙E measurements. An artificial neural network (ANN) model based on respiration features was used to estimate V˙E and compared to the multiple linear regression (MLR) model. We also compared the subject-specific approach with the subject-independent approach. Results The ANN model using subject-specific approach achieved better accuracy for the V˙E estimation. The bias was between 0.20±0.87 and 0.78±3 l/min with the ANN model as compared to 0.73±3.19 and 4.17±2.61 l/min with the MLR model. Conclusion Our results demonstrated the pertinence of processing data streams from wearable RMP device to estimate the V˙E with sufficient accuracy for various PA types. Due to its low-complexity and real-time algorithm design, the current approach can be easily integrated into most remote health monitoring applications coupled with wearable sensors.

I-SEE: Intelligent, Secure, and Energy-Efficient Techniques for Medical Data Transmission Using Deep Reinforcement Learning

The rapid evolution of remote health monitoring applications is foreseen to be a crucial solution for facing an unpredictable health crisis and improving the quality of life. However, such applications come with many challenges, including: the transmission of a large amount of private medical data and the limited power budget for battery-operated devices. Thus, this article proposes an intelligent, secure, and energy-efficient (I-SEE) framework for secure and energy-efficient medical data transmission, leveraging the potential of physical-layer security. In particular, we incorporate a practical secrecy metric, namely, the secrecy outage probability (SOP), along with the adaptive compression at the edge for providing a secure solution for health monitoring applications. In the proposed framework, we first formulate an optimization problem that maximizes the energy efficiency, while maintaining quality-of-service constraints of the health application. Second, we propose a deep reinforcement learning process that obtains the optimal strategy for secure data transmission. Specifically, a multiobjective reward function is defined to optimize energy efficiency and distortion, resulting from the compression scheme. Then, a deep deterministic policy gradients (DDPGs) algorithm, named Static-DDPG is proposed to solve our problem efficiently. Third, the problem is extended to consider the battery lifetime maximization with varying channel conditions. Indeed, a Dynamic-DDPG algorithm is proposed in order to allow the edge to adapt to the environment dynamics while maximizing its battery lifetime. The conducted simulations validate the efficiency of the proposed algorithms in terms of finding the optimal policy that addresses the tradeoff between the considered conflicting objectives, along with the battery lifetime maximization

A Rigid-Flex Wearable Health Monitoring Sensor Patch for IoT-Connected Healthcare Applications

The Internet of Things (IoT) is a new communication paradigm that can connect elements from various fields through the Internet. One of the most attractive IoT applications is in the modern healthcare area, as the traditional healthcare system has an increasing demand for social resources, including doctors, nurses, hospital beds, and health monitoring devices. In this article, the design of a compact wearable sensor patch is presented for measurements of different physiological signals, such as the electrocardiogram (ECG), photoplethysmography (PPG), and body temperature. As ECG and PPG sensors are integrated with the same device, the proposed sensor patch can be used to estimate blood pressure (BP) continuously based on the pulse arrival time (PAT) without extra wires and devices. The sensor patch consists of a center board for signal acquisition and processing, a power board for energy supply and charging batteries, and three sensors for vital signs monitoring. All the components are designed in a rigid-flex structure, which can be easily attached to the human body for remote health monitoring applications. The sensors can be detached from the center board for customized measurements of a certain physiological signal (e.g., ECG) to reduce power consumption. Experiments are conducted to validate the performance of the proposed sensor patch by comparison with a commercial reference device. With the integration of a miniaturized Bluetooth low-energy (BLE) module, the proposed sensor system can transmit physiological measurements wirelessly to a gateway. Data encryption is applied on both the sensor patch and gateways to protect data for privacy and security purposes during transmission. Both a mobile gateway (based on smartphones) and a fixed gateway (based on portable computers) are designed as the bridge between the wearable sensor system and the Internet cloud, where health data can be stored and further analyzed. The experimental results demonstrate the feasibility of the overall platform for IoT-connected healthcare applications.

CNN-Based Health Model for Regular Health Factors Analysis in Internet-of-Medical Things Environment

Remote health monitoring applications with the advent of Internet of Things (IoT) technologies have changed traditional healthcare services. Additionally, in terms of personalized healthcare and disease prevention services, these depend primarily on the strategy used to derive knowledge from the analysis of lifestyle factors and activities. Through the use of intelligent data retrieval and classification models, it is possible to study disease, or even predict any abnormal health conditions. To predict such abnormality, the Convolutional neural network (CNN) model is used, which can detect the knowledge related to disease prediction accurately from unstructured medical health records. However, CNN uses a large amount of memory if it uses a fully connected network structure. Moreover, the increase in the number of layers can lead to an increase in the complexity analysis of the model. Therefore, to overcome these limitations of the CNN-model, we propose a CNN-regular target detection and recognition model based on the Pearson Correlation Coefficient and regular pattern behavior, where the term “regular” denotes objects that generally appear in similar contexts and have structures with low variability. In this framework, we develop a CNN-regular pattern discovery model for data classification. First, the most important health-related factors are selected in the first hidden layer, then in the second layer, a correlation coefficient analysis is conducted to classify the positively and negatively correlated health factors. Moreover, regular patterns' behaviors are discovered through mining the regular pattern occurrence among the classified health factors. The output of the model is subdivided into regular-correlated parameters related to obesity, high blood pressure, and diabetes. Two distinct datasets are adopted to mitigate the effects of the CNN-regular knowledge discovery model. The experimental results show that the proposed model has better accuracy, and low computational load, compared with three different machine learning techniques methods.

Dynamic HUB Selection Process Based on Specific Absorption Rate for WBANs

Wireless body area networks (WBANs) play an important role in remote health monitoring applications nowadays. WBANs consist of several sensor nodes and a fixed HUB on, in, or around the human body. HUB collects the data from the sensor nodes and sends the received data to a gateway. High data rates from HUB cause to increase in temperature of tissues. If an organ receives electromagnetic signals for longer time period, then it will be affected by heat and might be damaged. In this paper, specific absorption rate, battery level, and priority of the sensor nodes are taken into account for dynamical HUB selection process in WBANs. Therefore, the task of the HUB is shared among the sensor nodes to reduce the negative effects of electromagnetic signals due to fixed HUB placement.

CMDP-based intelligent transmission for wireless body area network in remote health monitoring

Remote health monitoring is one kind of E-health service, which transfer the users’ physiological data to the medical data center for analysis or diagnosis. Wireless body area network (WBAN) is a promising technology to achieve physiological information acquiring and delivering and thus has been widely adopted in remote health-monitoring applications. For WBAN, energy consumption is the major concern which has been addressed in many researches. Different from existing works, this work studies a joint scheduling and admission control problem with objective of optimizing the energy efficiency of both intra- and beyond-WBAN link. The problem is formulated as constrained Markov decision processes, and the relative value iteration and Lagrange multiplier approach are used to derive the optimal intelligent algorithm. Simulation results show the proposed algorithm is capable of, in comparison with greedy scheme, achieving nearly 100% throughput improvement in various power consumption budgets. Moreover, the proposed algorithm can achieve up to 5.5× power consumption saving for sensor node in comparison with other scheduling algorithms.

Scheduling IoT Healthcare Tasks in Fog Computing Based on their Importance

The Internet-of-Things (IoT) is the future of the Internet and one of the most important technologies used in recent times in all areas, where everything will be connected to the Internet. One of them is using IoT in healthcare area such as mobile health and remote patient monitoring with various diseases such as heart disease, blood pressure, diabetes, and other chronic diseases. The real-time for remote health monitoring applications is very important; any delay caused by transferring data to the cloud and back to the application is unacceptable. So, we propose to use fog computing between sensors and cloud computing to collect and process data in an efficient manner, reduces the amount of data that is transported between the cloud and the sensors, and increase the entire system efficiency. Wireless sensors networks (WSNs) that used in the area of health monitoring send a huge number of tasks with different importance levels and lengths simultaneously to fog computing. Therefore, we need to implement an appropriate task scheduling algorithm with an ability to give priority for tasks accurately and make the main factor in giving priority to tasks is their importance regardless of their length. In this research, we attempt to improve static task scheduling algorithms performance by using a new method called Tasks Classification and Virtual Machines Categorization (TCVC) based on tasks importance. Tasks that received by IoT classified based on their importance into three classes: high importance tasks, medium importance tasks, and low importance tasks based on the patient’s health status. In order to measure the performance achieved by these methods, they will be applied on MAX-MIN scheduling algorithm. The cloudsim simulator has been used to measure their impact on algorithm complexity, resource availability, Total Execution Time (TET), Total Waiting Time (TWT), and Total Finish Time (TFT).

Edge-based compression and classification for smart healthcare systems: Concept, implementation and evaluation

Smart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the network edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart healthcare systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make autonomous decisions for smart health applications. In particular, we present and implement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low computational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient’s conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%.

Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes

Internet of Things (IoT) technology provides a competent and structured approach to handle service deliverance aspects of healthcare in terms of mobile health and remote patient monitoring. IoT generates an unprecedented amount of data that can be processed using cloud computing. But for real-time remote health monitoring applications, the delay caused by transferring data to the cloud and back to the application is unacceptable. Relative to this context, we proposed the remote patient health monitoring in smart homes by using the concept of fog computing at the smart gateway. The proposed model uses advanced techniques and services, such as embedded data mining, distributed storage, and notification services at the edge of the network. Event triggering-based data transmission methodology is adopted to process the patient’s real-time data at fog layer. Temporal mining concept is used to analyze the events adversity by calculating the temporal health index of the patient. In order to determine the validity of the system, health data of 67 patients in IoT-based smart home environment was systematically generated for 30 days. Results depict that the proposed Bayesian belief network classifier-based model has high accuracy and response time in determining the state of an event when compared with other classification algorithms. Moreover, decision making based on real-time healthcare data further enhances the utility of the proposed system.