Selection and Ranking of Fog Computing-Based IoT for Monitoring of Health Using the Analytic Network Approach (original) (raw)
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Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring
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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.
Criticality and Utility-aware Fog Computing System for Remote Health Monitoring
2021
Growing remote health monitoring system allows constant monitoring of the patient’s condition and performance of preventive and control check-ups outside medical facilities. However, the real-time smart-healthcare application poses a delay constraint that has to be solved efficiently. Fog computing is emerging as an efficient solution for such real-time applications. Moreover, different medical centers are getting attracted to the growing IoT-based remote healthcare system in order to make a profit by hiring Fog computing resources. However, there is a need for an efficient algorithmic model for allocation of limited fog computing resources in the criticality-aware smart-healthcare system considering the profit of medical centers. Thus, the objective of this work is to maximize the system utility calculated as a linear combination of the profit of the medical center and the loss of patients. To measure profit, we propose a flat-pricing-based model. Further, we propose a swapping-bas...
IJERT-Survey on Fog Computing in Healthcare Monitoring
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/survey-on-fog-computing-in-healthcare-monitoring https://www.ijert.org/research/survey-on-fog-computing-in-healthcare-monitoring-IJERTV10IS050285.pdf At this moment in time, the requirement for medical assistance has been increasing. With a lot of patients coming in and a lesser ratio of doctors present there is a need to manage patient data in an organized manner and notify the doctor as and when required. The problem arises when a single doctor would have to monitor a patient's standard test results. It will render to be a tedious process even if the patients test results are normal. IoT plays a major role in healthcare as IoT devices can be tagged with sensors and used for real-time remote monitoring of patients. IoT generates bulk of data that can be refined using the cloud but for instantaneous monitoring, delays are caused when the data is to be transferred from the cloud to the application which is not admissible. Hence we have come up with a simple and affordable response that will possibly provide good insight into the patient's test results by using FOG as a median. Notification systems and machine learning algorithms are used to reduce the difficulties that emerge. These algorithms aim to improve the prediction process by relying on various inputs.
On the Deployment of Healthcare Applications over Fog Computing Infrastructure
2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)
Fog computing is considered as the most promising enhancement of the traditional cloud computing paradigm in order to handle potential issues introduced by the emerging Interned of Things (IoT) framework at the network edge. The heterogeneous nature, the extensive distribution and the hefty number of deployed IoT nodes will disrupt existing functional models, creating confusion. However, IoT will facilitate the rise of new applications, with automated healthcare monitoring platforms being amongst them. This paper presents the pillars of design for such applications, along with the evaluation of a working prototype that collects ECG traces from a tailormade device and utilizes the patient's smartphone as a Fog gateway for securely sharing them to other authorized entities. This prototype will allow patients to share information to their physicians, monitor their health status independently and notify the authorities rapidly in emergency situations. Historical data will also be available for further analysis, towards identifying patterns that may improve medical diagnoses in the foreseeable future.
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.
Future Generation Computer Systems, 2018
Current developments in ICTs such as in Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) allow us to develop healthcare solutions with more intelligent and prediction capabilities both for daily life (home/office) and in-hospitals. In most of IoT-based healthcare systems, especially at smart homes or hospitals, a bridging point (i.e., gateway) is needed between sensor infrastructure network and the Internet. The gateway at the edge of the network often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways at the edge of the network to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., presenting thus a Smart e-Health Gateway. We then propose to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, our Fog-assisted system architecture can cope with many challenges in ubiquitous healthcare systems such as mobility, energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways can enable massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a prototype of a Smart
Leveraging Fog Computing for Healthcare IoT
Fog Computing in the Internet of Things, 2017
Developments in technology have shifted the focus of medical practice from treating a disease to prevention. Currently, a significant enhancement in healthcare is expected to be achieved through the Internet of Things (IoT). There are various wearable IoT devices that track physiological signs and signals in the market already. These devices usually connect to the Internet directly or through a local smart phone or a gateway. Home based and in hospital patients can be continuously monitored with wearable and implantable sensors and actuators. In most cases, these sensors and actuators are resource constrained to perform computing and operate for longer periods. The use of traditional gateways to connect to the Internet provides only connectivity and limited network services. With the introduction of the Fog computing layer, closer to the sensor network, data analytics and adaptive services can be realized in remote healthcare monitoring. This chapter focuses on a smart e-health gateway implementation for use in the Fog computing layer, connecting a network of such gateways, both in home and in hospital use. To show the application of the services, simple healthcare scenarios are presented. The features of the gateway in our Fog implementation are discussed and evaluated.
Industrial quality healthcare services using Internet of Things and fog computing approach
Measurement: Sensors
Healthcare in general refers to services provided for preservation or enhancement of health through anticipation, finding, treatment, recovery, or healing of disease, illness, injury, or any physical and mental impairment in humans. Quality of Services in Healthcare Systems may be characterised broadly through parameters such as accuracy of data, speed of decision-making, timely treatment, security of data, real-time monitoring and controlling of the health systems, failure handling and quality of life. In this research work, an attempt is made to achieve the Quality of Services in Healthcare through IoT and Fog Computing, considering optimization of one or more of these parameters. An integrated hardware system with software programs is developed for EHS safety using microcontroller. The Processing Packages are designed to communicate through the Internet of Things (IoT) based on the Accident Reduction Model (ARM) and Augmented Data Recognition (ADR) system.When ECG signals are sensed through IoT and sent to the cloud for the analysis purpose, delays are caused by the time results reach the medical staff. This delay in decision making is due to the transmission delay and processing delays. To overcome these issues, Fog Computing can be used.
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
A Novel Edge-Computing-Based Framework for an Intelligent Smart Healthcare System in Smart Cities
Sustainability
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 ...