Industrial quality healthcare services using Internet of Things and fog computing approach (original) (raw)
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Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex; 2) The data, when communicated, are vulnerable to security and privacy issues; 3) The communication of the continuously collected data is not only costly but also energy hungry; 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating con-nectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection. The book chapter ends with experiments and results showing how fog computing could lessen the obstacles of existing cloud-driven medical IoT solutions and enhance the overall performance of the system in terms of computing intelligence , transmission, storage, configurable, and security. The case studies on various types of physiological data shows that the proposed Fog architecture could be used for signal enhancement, processing and analysis of various types of bio-signals.
A Novel Edge-Computing-Based Framework for an Intelligent Smart Healthcare System in Smart Cities
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The wide use of internet-enabled devices has not left the healthcare sector untouched. The health status of each individual is being monitored irrespective of his/her medical conditions. The advent of such medical devices is beneficial not only for patients but also for physicians, hospitals, and insurance companies. It makes healthcare fast, reliable, and hassle-free. People can keep an eye on their blood pressure, pulse rate, etc., and thus take preventive measures on their own. In hospitals, too, the Internet of Things (IoT) is being deployed for various tasks such as monitoring oxygen and blood sugar levels, electrocardiograms (ECGs), etc. The IoT in healthcare also reduces the cost of various ailments through fast and rigorous data analysis. The prediction of diseases through machine-learning techniques based on symptoms has become a promising concept. There may also be a situation where real-time analysis is required. In such a latency-sensitive situation, fog computing plays ...
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Cloud computing delivers services through the Internet and enables the deployment of a diversity of apps to provide services to many businesses. At present, the low scalability of these cloud frameworks is their primary obstacle. As a result, they are unable to satisfy the demands of centralized computer systems, which are based on the Internet of Things (IoT). Applications such as disease surveillance and tracking and monitoring systems, which are highly latency sensitive, demand the computation of the Big Data communicated to centralized databases and from databases to cloud data centers, resulting in system performance loss. Recent concepts, such as fog and edge computing, offer novel approaches to data processing by relocating the processing power and other resources closer to the end user, thereby reducing latency and maximizing energy efficiency. Existing fog models, on the other hand, have a number of limitations and tend to prioritize either the precision of their findings o...
Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring
Medical & Biological Engineering & Computing
The new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed.
Fog Assisted and IoT Based Real-Time Health Monitoring System Implementation
2021
With the proliferation of IoT devices in medical healthcare systems, IoT based health-monitoring systems and applications have brought about a ground-breaking breakthrough in modern healthcare facilities, medical data processing, and analysis. Meeting the challenge of co-operative and distributed IoT based healthcare systems; the problems like latency, network congestion, and data traffic in the systems can be overcome by fog computing, a decentralized cloud computing platform. In this study, we enhanced such an IoT-enabled realtime patient health monitoring system by exploiting the fog computing concept for extracting sensor data, visualizing them at reduced cost and power, storing at local storage, monitoring, and interacting remotely. Using three different sensor devices that extract health data, we developed a new type of fog computing interface using a combination of Raspberry Pi and Arduino. A dedicated local server called fog server was implemented as well for the storage and...
Procedia Computer Science, 2020
Fog Computing is a new computing paradigm which is grown ever since it is being used. It is aimed at bringing the computations close to data sources from healthcare centers. IoT driven Fog Computing is developed in the healthcare industry that can expedite facilities and services among the mass population and help in saving billions of lives. The new computing platform, founded as fog computing paradigm may help to ease latency while transmitting and communicating signals with remote servers, which can accelerate medical services in spatial-temporal dimensions. The latency reduction is one of the necessary features of computing platforms which can enable completing the healthcare operations, especially in large-size medical projects and in relation to providing sensitive and intensive services. Reducing the cost of delivering data to the cloud is one of the research objectives.
COMPREHENSIVE REVIEW ON IOT BASED HEALTH ARCHITECTURES AND FOG, CLOUD COMPUTING
Traditional health care systems are replaced by use of high precision sensors and IOT enabled medical devices. The m-health system is subset of E-health system .It has gained more popularity over E-Health system due to extensive use of smart phone. Both systems are useful in measurement of physiological as well as chronic health parameters. Microcontroller system processes patient's real time health data and send over cloud or fog. Cloud computing facilitates rapid on demand access to shared pool of virtual computing resources, servers, networks. Fog computing can be considered as extension of cloud computing as it provides low latency, low bandwidth with increased level of data security and privacy. Data breach is major issue in health care system that can be solved using special privacy acts, and special algorithms. In this paper, we are doing comprehensive analysis of IOT based m-health system and Ehealth system, cloud ,fog computing as well as data security issues
World Academy of Research in Science and Engineering , 2020
The concept of the Internet of Things (IoT), revolves around connecting a data network that extends beyond the standard computers and smart devices, into simpler everyday objects, such as lighting, air conditioning, medical equipment, and street signs, which allow them to be remotely monitored and controlled. Even though this concept of having a network of intelligent devices has been discussed for as early as the 1980s, it hasn't seen much traction and implementation until recent years, thus, still making it an unfamiliar concept to many. With having a bunch of remotely controllable and accessible different everyday objects, this concept can be used in emergency situations, in order to reduce the impact of such occurrences, and hasten recovery. This study focuses to explore the different possible approaches in using IoT for hospital and healthcare management and specific examples of possible real-life scenario applications. By doing so, the paper was able to present possible parameters of measurement between different IoT healthcare networks to compare and determine the best possible implementation to use.
International Journal of Electrical and Computer Engineering (IJECE), 2019
Global Burden of Disease Report, released in Sept 2017, shows that Cardiovascular Diseases caused 1.7 million deaths (17.8%) in 2016 and it is the leading cause of deaths in India [1]. According to the Indian Heart Association, 25% of all heart attacks happen under the age of 40. In most cases, the initial heart attacks are often ignored. Even post-diagnosis, as per government data [2], 50% of heart attack cases reach the hospital in more than 400 minutes against the ideal window time of 180 minutes; post which damage is irreversible. The delay is often attributed to delay in reaching a hospital or receiving primary aid. In India, traffic conditions also add to the grimace of the situation. Although the government is taking various measures; a holistic solution is required to minimize the delay at each of the steps like accessing the patient situation, contacting the Medical aid or making available the nearest aid possible. In this paper, we aim at providing the holistic solution using the Internet of Things technology (IOT) along with data analytics. IoT enables real-time capturing and computation of medical data from smart sensors built-in wearable devices. The amalgamation of Internet-based services with Medical Things (Smart sensors) enhance the chances of survival of patients. The proposed system analyses the inputs collected from the sensors fit with the patients prone to cardiovascular diseases to ascertain the emergency situation. In addition, to these data, the system also considers age, maximum and minimum heart rate. Based on computational results received from the input parameters, the system triggers the alert to emergency contacts such as the close relatives of the patient, doctors, the hospitals and nearby ambulance. The proposed system combines with the optimized navigation platform to guide the medical assistance to find the fastest route.
IRJET- Intelligence Health Gateway Based on Internet-of-Things for Ubiquitous Healthcare Systems
IRJET, 2020
The increase in popularity for wearable technologies has opened the door for an Internet of Things (IoT) solution to healthcare. One of the most prevalent healthcare problems today is the poor survival rate of out-of-hospital sudden cardiac arrests. The objective of this study is to present a multisensory system using fog-enabled IoT that can collect physical activity heart rates and body temperature. For this study, we implemented an embedded sensory system with a Low Energy (LE) high speed raspberry pi module will collect ECG, BP, Heart rate and body temperature data collect and storage inevitable. Fog Computing (FC) is an environment where data are stored and pre-processed before transmitting them to the cloud, having a number of advantages like scalable real-time services, fault detection and isolation, enhanced security and privacy, etc. In this work, we present a fog-enabled IoT platform used for sensory data collection, presenting several metrics that can be used as the basis for a Management-Platform-as-a-Service, able to efficiently monitor with the use of signal processing and machine learning techniques for sensor data analytics for sudden cardiac arrest and or heart attack prediction on the IoT platform and predict potential failures and transfer the decision and data to cloud storage and get all details can monition in android apps.