The Benefits of Edge Computing in Healthcare, Smart Cities, and IoT (original) (raw)
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Edge computing for Internet of Things: A survey, e-healthcare case study and future direction
Journal of Network and Computer Applications, 2019
The world has recently witnessed the emergence of huge technological growth in the field of data transmission and smart living through various modes of information and communication technology. For example, edge computing has taken a leading role to embark upon the problems related to the internetwork bandwidth minimization and service latency reduction. Inclusion of small microcontroller chips, smart sensors and actuators in the existing socioeconomic sectors have paved the Internet of Things (IoT) to act upon the dissemination of smart services to the end users. Thus, a strong need of understating of the industrial elements of edge computing has become necessary that can share the mutual goal while assimilating with the IoT. This paper advocates the crucial role of industrial standards and elements of the edge computing for the dissemination of overwhelming augmented user experience with conjunction with the IoT. First, we present the taxonomical classification and review the industrial aspects that can benefit from both of the IoT and edge computing scenario, then discuss about each of the taxonomical components in detail. Second, we present two practically implemented use cases that have recently employed the edge-IoT paradigm together to solve urban smart living problems. Third, we propose a novel edge-IoT based architecture for e-healthcare i.e. EH-IoT and developed a demo test-bed. The test results showed promising results towards minimizing dependency over IoT cloud analytics or storage facility. We conclude with discussion on the various parameters such as, architecture, requirement capability, functional issues, and selection criteria, related to the survival of edge-IoT ecosystem incorporation.
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 ...
Internet of Medical Things and Edge Computing for Improving Healthcare in Smart Cities
Mathematical Problems in Engineering, 2022
To build prosperous smart cities, adequate infrastructure must be provided. Smart cities contain intelligent things to enhance lives and save people’s lives. The Internet of medical things (IoM) and edge computing are part of these things. Healthcare services are essential services that should benefit from the infrastructure of smart cities. Increasing the quality of services (QoS) required increased connectivity and supercomputing. Supercomputing is represented by connecting the IoM with high processing devices close to these healthcare service devices called edge processing. Healthcare application requires low network latencies; therefore, edge computing must be necessary. Edge computing enables reduced latency and energy efficiency, scalability, and bandwidth. In this study, we review the most important algorithms used in the resource allocation management process at the MEC, which are the DPSO, ACO, and basic PSO. Our experiments have proven that the DPSO is the better and appro...
IEEE Access
Smart city advancements are driving massive transformations of healthcare, the largest global industry. The drivers include increasing demands for ubiquitous, preventive, and personalized healthcare, to be provided to the public at reduced risks and costs. Mobile cloud computing could potentially meet the future healthcare demands by enabling anytime, anywhere capture and analyses of patients' data. However, network latency, bandwidth, and reliability are among the many challenges hindering the realization of next-generation healthcare. This paper proposes a ubiquitous healthcare framework, UbeHealth, that leverages edge computing, deep learning, big data, high-performance computing (HPC), and the Internet of Things (IoT) to address the aforementioned challenges. The framework enables an enhanced network quality of service using its three main components and four layers. Deep learning, big data, and HPC are used to predict network traffic, which in turn are used by the Cloudlet and network layers to optimize data rates, data caching, and routing decisions. Application protocols of the traffic flows are classified, enabling the network layer to meet applications' communication requirements better and to detect malicious traffic and anomalous data. Clustering is used to identify the different kinds of data originating from the same application protocols. A proof of concept UbeHealth system has been developed based on the framework. A detailed literature review is used to capture the design requirements for the proposed system. The system is described in detail including the algorithmic implementation of the three components and four layers. Three widely used data sets are used to evaluate the UbeHealth system.
Edge computing in smart health care systems: Review, challenges, and research directions
Transactions on Emerging Telecommunications Technologies
Today, patients are demanding a newer and more sophisticated health care system, one that is more personalized and matches the speed of modern life. For the latency and energy efficiency requirements to be met for a real-time collection and analysis of health data, an edge computing environment is the answer, combined with 5G speeds and modern computing techniques. Previous health care surveys have focused on new fog architecture and sensor types, which leaves untouched the aspect of optimal computing techniques, such as encryption, authentication, and classification that are used on the devices deployed in an edge computing architecture. This paper aims first to survey the current and emerging edge computing architectures and techniques for health care applications, as well as to identify requirements and challenges of devices for various use cases. Edge computing application primarily focuses on the classification of health data involving vital sign monitoring and fall detection. Other low-latency applications perform specific symptom monitoring for diseases, such as gait abnormalities in Parkinson's disease patients. We also present our exhaustive review on edge computing data operations that include transmission, encryption, authentication, classification, reduction, and prediction. Even with these advantages, edge computing has some associated challenges, including requirements for sophisticated privacy and data reduction methods to allow comparable performance to their Cloud-based counterparts, but with lower computational complexity. Future research directions in edge computing for health care have been identified to offer a higher quality of life for users if addressed.
Edge computing: A technological advancement in Internet of Things and cloud computing
INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “TECHNOLOGY IN AGRICULTURE, ENERGY AND ECOLOGY” (TAEE2022)
Internet of Things (IoT) is emerging technologies these days which generates high volume of data. Efficient use of data analytics techniques on discrete data using Cloud Computing provides significant and precise information. In view of the traditional applications, an IoT application such as environmental monitoring, smart navigation and smart healthcare comes with separate requirements such as mobility, quick and real-time response etc. However, the typical cloud computing architecture cannot satisfy and fulfil these requirements due to processing of the data being distributed across the world remotely from physical location of installed IoT devices. Hence, the concept of edge computing emerged to perform data storage and processing at the extreme end of the networks, which is closer to data collection sources than the cloud storage. This makes applications computationally efficient and location-aware. But edge computing brings many security and privacy challenges when applied to data analytics in association with IoT devices.
Deployment of Edge Computing for Smart Healthcare Systems on Cloud Computing Platform
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024
Improving the productivity of health services is a prime concern for authorities all over the country. Yet, offering patients with scalable healthcare services while cutting costs is a complicated matter. Edge computing technologies and next-generation mobile computing infrastructure are one of the most effective alternatives for integrating efficient healthcare since they can provide real-time and cost-effective patient monitoring systems. Edge computing enables doctors to deliver care to remote areas where connectivity could not be great and skilled healthcare personnel may not be available. The proposed paper discuss the past research work and contribution in the field of smart Healthcare. The Paper further depicts general edge computing architecture for smart Healthcare system and trade-offs of Edge computing. The paper also discuss use cases of smart Healthcare system based on edge computing advantages of Smart- health and the challenges associated with the deployment
Improving Latency with Edge Computing and IoT in Health Applications: A Review
International Journal for Research in Applied Science & Engineering Technology, 2021
World is developing at a quick pace as is data. The boom of smart phones and Internet of Things (IoT) enable more users to access data as well as computation power in real-time. The constraints of current distributed computing is a subject of more prevalent concern. In response to these rising difficulties in computing, as new requirements and challenges spring up day by day, especially in cloud computing scenario, computing paradigms that can meet these challenges are sought after. In order to expand the efficiency and to decrease the quantity of the data to be sent to the cloud for processing, numerous solutions have been proposed for the edge-centric network. In this paper, an elaborate study of computing architectures proposed for edge paradigms is carried out and application areas in the Internet of Things are ascertained. I. INTRODUCTION Computing architectures have gone through quite a few changes between centralized and decentralized models in the recent past. Cloud being the present centralized paradigm, majority of web content is served through a main data center and researchers rent their private servers from cloud for testing or experimenting. In addition Cloud offers an expedient way for both small and big businesses to obtain computing sources from a service provider instead of putting up their own data center. Usage of Cloud services like Google Cloud, AWS, Azure and VMware have become so popular in recent past. However, the new trend towards a soaring emphasis on edge devices and edge computing paradigms cannot go unnoticed. On the other side, many research organizations have given a startling prediction about the future computing scenario , that is, more than 20 billions of devices will be connected to the Internet by 2020. Thus more and more internet-enabled devices will result in generating huge amounts of data than ever. The Cisco white paper states that, IoT devices will generate 600 zettabytes of data by 2020[46]. Even though Cloud computing can be utilized in a pay-per-usage way through centrally managed resources, high latency and privacy issues are seen as solemn challenges. Hence, there is an inclination towards a decentralized solution of Cloud computing framework and the recent trend of computing paradigms lead to realization of shifting computing to the edge of the network. Since the connected devices prove to be potent in terms of computational capabilities and also battery power, they can be used for IoT applications. The requirements of low latencies and less bandwidth utilization of the data-intensive applications in the Internet of Things (IoT) are met. Most of the Cloud datacenters are centralized and located far from the nearness of the edge devices, and the latency-sensitive real-time service requests suffer due to large round-trip delays, degradation in service quality and network congestion. Such issues can be resolved efficiently by means of latest edge computing paradigms. Although the notions of edge and fog computing have been conceptualized before now, a universal understanding of these paradigms in practice is lacking. The rest of this paper is organized as follows: Section II highlights the earlier work found in literature. Section III discusses about the various edge paradigms based on their architectures. Section IV presents a comparison scenario of edge computing, fog computing and Mobile Cloud Computing (MCC) derived from an analysis of performance parameters. Section V identifies application areas for edge paradigms in the field of IoT and highlights unresolved deployment issues. Finally, Section VI concludes the paper.
Edge Intelligence and Internet of Things in Healthcare: A Survey
IEEE Access
With the advent of new technologies and the fast pace of human life, patients today require a sophisticated and advanced smart healthcare framework that is tailored to suit their individual health requirements. Along with 5G and state-of-the-art smart Internet of Things (IoT) sensors, edge computing provides intelligent, real-time healthcare solutions that satisfy energy consumption and latency criteria. Earlier surveys on smart healthcare systems were centered on cloud and fog computing architectures, security, and authentication, and the types of sensors and devices used in edge computing frameworks. They did not focus on the healthcare IoT applications deployed within edge computing architectures. The first purpose of this study is to analyze the existing and evolving edge computing architectures and techniques for smart healthcare and recognize the demands and challenges of different application scenarios. We examine edge intelligence that targets health data classification with the tracking and identification of vital signs using state-of-the-art deep learning techniques. This study also presents a comprehensive analysis of the use of cutting-edge artificial intelligence-based classification and prediction techniques employed for edge intelligence. Even with its many advantages, edge intelligence poses challenges related to computational complexity and security. To offer a higher quality of life to patients, potential research recommendations for improving edge computing services for healthcare are identified in this study. This study also offers a brief overview of the general usage of IoT solutions in edge platforms for medical treatment and healthcare.