Reliability Aware Medical Resource Allocation for Health Care Industrial Internet of Things (IIoT) Using Tabu Search and Alo Algorithm

2021 ◽  
Vol 11 (12) ◽  
pp. 3090-3095
Author(s):  
Ramesh Chandran ◽  
N. Gayathri ◽  
S. Rakeshkumar
Keyword(s):  
Resource Allocation ◽  
Data Storage ◽  
Resource Constraints ◽  
Industrial Applications ◽  
Industrial Plant ◽  
Healthcare Industry ◽  
Medical Resource ◽  
Medical Resources ◽  
Smart Healthcare ◽  
Industrial Iot

The medical data integrating system allows the hospital’s resource constraints to be more effectively utilized. Moreover, by improving the resource management and allocation method, the hospital’s operations may be more organized, and the effectiveness of healthcare can be improved without breaking the medical agreements. Significant catastrophes frequently result in a scarcity of important medical resources, hence resource allocation must be optimized to enhance the performance of relief operations. The two main requirements for healthcare industrial applications are timeliness and reliability. Therefore, in the architecture of a smart healthcare industry these two criteria should be thought carefully. A well-known approach for the security and timeliness in the intelligent healthcare industry is to utilize hybrid IoT and Cloud technologies. Yet it is not enough to protect their hard deadlines for tight time-sensitive applications utilizing cloud. A potential way to cope with efficiency and latency criteria for strict time-sensitive applications is the deployment of intermediate processing layer IoT that can be linked between healthcare industrial plant and cloud. The purpose of this article is to develop a healthcare Industrial IoT system that include a medical resource allocation scheme for dividing a certain amount of workload between those multiple computing layers which are dependable and time consuming. IOT is integration of microprocessors and controller Workload partitioning can give us important design decisions to specify how many computing resources are needed in cooperation with IoT to develop a local private cloud. Ant lion optimization (ALO) and TABU Look for the right route. The simplest method of deciding the distance to a destination is to choose an OLSR routing protocol depending on the meaning or measure it requires. The method proposed in the distribution and data storage of medical resources is very efficient.

scholarly journals A Novel Blockchain Framework for Industrial IoT Edge Computing

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2061 ◽  
Author(s):  
Xuesong Xu ◽  
Zhi Zeng ◽  
Shengjie Yang ◽  
Hongyan Shao
Keyword(s):  
Data Storage ◽  
Resource Constraints ◽  
Rapid Development ◽  
Edge Computing ◽  
Consensus Algorithm ◽  
Resource Constrained ◽  
Blockchain Technology ◽  
Industrial Internet ◽  
Industrial Iot ◽  
And Performance

With the rapid development of industrial internet of thing (IIoT), the distributed topology of IIoT and resource constraints of edge computing conduct new challenges to traditional data storage, transmission, and security protection. A distributed trust and allocated ledger of blockchain technology are suitable for the distributed IIoT, which also becomes an effective method for edge computing applications. This paper proposes a resource constrained Layered Lightweight Blockchain Framework (LLBF) and implementation mechanism. The framework consists of a resource constrained layer (RCL) and a resource extended layer (REL) blockchain used in IIoT. We redesign the block structure and size to suit to IIoT edge computing devices. A lightweight consensus algorithm and a dynamic trust right algorithm is developed to improve the throughput of blockchain and reduce the number of transactions validated in new blocks respectively. Through a high throughput management to guarantee the transaction load balance of blockchain. Finally, we conducted kinds of blockchain simulation and performance experiments, the outcome indicated that the method have a good performance in IIoT edge application.

scholarly journals The Current Status of Cancer Control in China

2018 ◽  
Vol 4 (Supplement 2) ◽  
pp. 248s-248s
Author(s):  
Y. Wang
Keyword(s):  
Resource Allocation ◽  
Treatment Guideline ◽  
Cancer Control ◽  
Current Status ◽  
Chinese Government ◽  
Medical Resource ◽  
Medical Resources ◽  
Financial Investment ◽  
Cancer Burden ◽  
Class A

Background and context: With the rapid economic growth and aging population, China is now facing the challenge of cancer burden. China National Cancer Registry data displayed that >4 million people were diagnosed with cancer in 2012, which accounted for 20% of global cancer incidence and 25% global cancer deaths occurred in China. Lung, gastrointestinal, breast cancers are the top 3 most common caused cancer diseases for Chinese people. Cancer brings huge economic loss both to Chinese economy and individuals. The total cancer cost has been raised 4 times more than in 2003, which was estimated to >400 billion Chinese Yuan. From 1970s, Chinese government attached importance to cancer control and conducted cancer prevention and control planning and strategy, but the medical resource allocation for cancer is still inequality in the country. Aim: China Anti-Cancer Association (CACA) investigates a research on how cancer control medical resource allocates in China and provides suggestion on cancer control policy development. Strategy/Tactics: Through collecting the data from 55 cancer hospitals across the country from 2013 to 2015 to analyze. Program/Policy process: The results show that oncology doctors and nurses slightly increase in consecutive 3 years, but the outpatient and inpatient visits grow in the average annual rate of 10%, the oncology professionals supply falls short of patient's demand. In terms of geography distribution, the cancer control medical resources are rich in municipality, capital cities and coastline cities. Northeast, southwest, and northwest part of China have weak medical resources to prevent and cure cancers. The oncology professional is constituted by clinical surgery, internal medical doctor, radiation therapist, and others. Clinical surgery is estimated to account for 40% of all professionals in cancer hospitals. The oncology professionals have higher education and academic background in third-grade class A hospitals. Inpatient and outpatient visits are much more in provincial cancer hospitals, rather than in prefectural-level cancer hospitals. Outcomes: Based on the research, CACA suggests that government should realize the importance of cancer control medical resource allocation, including increasing the number of oncology professionals, strengthening the professionals' academic training in secondary class A hospitals and prefectural-level cancer hospitals, and providing more financial investment to the parts where have insufficient medical resources. In addition, CACA advocates to taking the actions on tobacco control, HPV and HBV vaccine injection, changing healthy lifestyle, implementing the strategy of early detection, diagnose, and treatment, and formulating standardized and precision cancer treatment guideline, to ease the cancer burden so as to improve people's healthy life in China.

scholarly journals Biases in COVID-19 Medical Resource Dilemmas

2021 ◽  
Vol 12 ◽  
Author(s):  
Georgia Michailidou
Keyword(s):  
Resource Allocation ◽  
Low Income ◽  
Choice Experiment ◽  
Medical Resource ◽  
Optimal Resource Allocation ◽  
Medical Resources ◽  
Optimal Resource ◽  
Demographic Attributes ◽  
Social Biases ◽  
To Receive

Accruing evidence suggest that COVID-19 is more fatal for males and minorities than other sub-populations. In this paper, we study medical dilemmas pertaining to the allocation of medical resources to evaluate whether existing social biases correspond to the demographic disparities of the pandemic. We develop and implement a choice experiment in which participants decide how to allocate scarce medical resources among COVID-19 patients with diverse demographic attributes. We find that participants violate optimal resource allocation significantly more often for the benefit of females. Males are almost half as likely to receive lifesaving resources even if these are medically more beneficial for them. We also find that participants are less likely to assign resources to patients with high compared to low income. Last, we find no evidence of patients' race affecting allocation preferences.

scholarly journals Spatial analysis and evaluation of medical resource allocation in China based on geographic big data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sida Wan ◽  
Yanming Chen ◽  
Yijia Xiao ◽  
Qiqi Zhao ◽  
Manchun Li ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Spatial Heterogeneity ◽  
Spatial Clustering ◽  
Evaluation Model ◽  
Medical Resource ◽  
Economic Sectors ◽  
Medical Resources ◽  
Chinese Cities ◽  
Balanced Development

Abstract Background Spatial allocation of medical resources is closely related to people’s health. Thus, it is important to evaluate the abundance of medical resources regionally and explore the spatial heterogeneity of medical resource allocation. Methods Using medical geographic big data, this study analyzed 369 Chinese cities and constructed a medical resource evaluation model based on the grading of medical institutions using the Delphi method. It evaluated China’s medical resources at three levels (economic sectors, economic zones, and provinces) and discussed their spatial clustering patterns. Geographically weighted regression was used to explore the correlations between the evaluation results and population and gross domestic product (GDP). Results The spatial heterogeneity of medical resource allocation in China was significant, and the following general regularities were observed: 1) The abundance and balance of medical resources were typically better in the east than in the west, and in coastal areas compared to inland ones. 2) The average primacy ratio of medical resources in Chinese cities by province was 2.30. The spatial distribution of medical resources in the provinces was unbalanced, showing high concentrations in the primate cities. 3) The allocation of medical resources at the provincial level in China was summarized as following a single-growth pole pattern supplemented by bipolar circular allocation and balanced allocation patterns. The agglomeration patterns of medical resources in typical cities were categorized into single-center and balanced development patterns. GDP was highly correlated to the medical evaluation results, while demographic factors showed, low correlations. Large cities and their surrounding areas exhibited obvious response characteristics. Conclusions These findings provide policy-relevant guidance for improving the spatial imbalance of medical resources, strengthening regional public health systems, and promoting government coordination efforts for medical resource allocation at different levels to improve the overall functioning of the medical and health service system and bolster its balanced and synergistic development.

scholarly journals Edge Computing – “An Enabling Technology for Industrial IoT (IIoT) Devices” – Exploring Its Challenges and Security Issues

2020 ◽  
Vol 8 (6S4) ◽  
pp. 6-8
Keyword(s):  
Data Storage ◽  
Data Analytics ◽  
High Volume ◽  
Edge Computing ◽  
Security And Privacy ◽  
Future Research ◽  
Efficient Manner ◽  
Smart Healthcare ◽  
Industrial Iot ◽  
Iot Devices

Internet of Things (IoT) is latest technology 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 previously used applications, an application that is IoT enabled such as environmental monitoring, application for navigation and smart healthcare systems being developed with different requirements such as portability, fast and real-time response etc. However, the typical architecture of cloud system cannot fulfill these requirements as the 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 devices that is nearer to data collection sources than the cloud storage. This makes applications computationally intelligent and location notified. But edge computing suffers from many challenges related to security and privacy when it is been applied to data analytics in association with IoT devices. The literature collected till date still deficient in detail review on the advancements in security and safe data analytics techniques used in edge computing. This paper, first introduce the various concepts and characteristics related to edge computing, and then we try to propose solutions for performing data analytics in a secured and efficient manner, thereafter reviewing the underlying some security attacks in the field of edge computing. Based on our literature survey, we have highlighted current open issues and some future research areas in this field

scholarly journals Regional Disparity and Patients Mobility: Benefits and Spillover Effects of the Spatial Network Structure of the Health Services in China

2021 ◽  
Vol 18 (3) ◽  
pp. 1096
Author(s):  
Liping Fu ◽  
Kaibo Xu ◽  
Feng Liu ◽  
Lu Liang ◽  
Zhengmin Wang
Keyword(s):  
Health Economic ◽  
Health Services ◽  
Network Structure ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Health Economy ◽  
Spatial Network ◽  
Spatial Effects ◽  
Medical Resource ◽  
Medical Resources

Background: The distribution of medical resources in China is seriously imbalanced due to imbalanced economic development in the country; unbalanced distribution of medical resources makes patients try to seek better health services. Against this backdrop, this study aims to analyze the spatial network characteristics and spatial effects of China’s health economy, and then find evidence that affects patient mobility. Methods: Data for this study were drawn from the China Health Statistical Yearbooks and China Statistical Books. The gravitational value of China’s health spatial network was calculated to establish a network of gravitational relationships. The social network analysis method was used for centrality analysis and spillover effect analysis. Results: A gravity correlation matrix was constructed among provinces by calculating the gravitational value, indicating the spatial relationships of different provinces in the health economic network. Economically developed provinces, such as Shanghai and Jiangsu, are at the center of the health economic network (centrality degree = 93.333). These provinces also play a strong intermediary role in the network and have connections with other provinces. In the CONCOR analysis, 31 provinces are divided into four blocks. The spillover effect of the blocks indicates provinces with medical resource centers have beneficial effects, while provinces with insufficient resources have obvious spillover effects. Conclusion: There is a significant gap in the geographical distribution of medical resources, and the health economic spatial network structure needs to be improved. Most medical resources are concentrated in economically developed provinces, and these provinces’ positions in the health economic spatial network are becoming more centralized. By contrast, economically underdeveloped regions are at the edge of the network, causing patients to move to provinces with medical resource centers. There are health risks of the increasing pressure to seek medical treatment in developed provinces with abundant medical resources.

Quality of Life and Resource Allocation

1988 ◽  
Vol 23 ◽  
pp. 33-55 ◽  
Author(s):  
Michael Lockwood
Keyword(s):  
Quality Of Life ◽  
Resource Allocation ◽  
Quality Adjusted Life Year ◽  
Medical Resources ◽  
Medical Vocabulary ◽  
Quality Adjusted Life

A new word has recently entered the British medical vocabulary. What it stands for is neither a disease nor a cure. At least, it is not a cure for a disease in the medical sense. But it could, perhaps, be thought of as an intended cure for a medicosociological disease: namely that of haphazard or otherwise ethically inappropriate allocation of scarce medical resources. What I have in mind is the term ‘QALY’, which is an acronym standing for quality adjusted life year. Just what this means and what it is intended to do I shall explain in due course. Let me first, however, set the scene.

Conflict and Hidden Trouble of Distributing Misplay at Memory Resources

2013 ◽  
Vol 423-426 ◽  
pp. 2707-2711
Author(s):  
Hua Shu Yang ◽  
Yi Wang ◽  
Yu Lu Yang ◽  
Xin Long Peng ◽  
Xue Peng Wang ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Control System ◽  
Automatic Control ◽  
Data Storage ◽  
Automatic Control System ◽  
System Function ◽  
Memory Resources

Resources of memory are frequently used times without number for reducing cost of control system. Rewriting in memory would cause occasional hardware conflict when especial data are inputted or are calculated, thereby system faults even accidents are elicited. Origin of occasional malfunction was analysed in automatic control system, necessity of program storage separate from data storage was discussed, and the notion was put forward that resource allocation of memory should be compatible with the system function. So dead halt owing to codes cover each other could be avoided, and reliability of measurement or control system could be effectively improved.

scholarly journals Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

2019 ◽  
Vol 9 (2) ◽  
pp. 608-612
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Data Storage ◽  
Memory Management ◽  
Research Work ◽  
Dynamic Resource Allocation ◽  
Mobile Platform ◽  
Dynamic Resource

Memory management is very essential task for large-scale storage systems; in mobile platform generate storage errors due to insufficient memory as well as additional task overhead. Many existing systems have illustrated different solution for such issues, like load balancing and load rebalancing. Different unusable applications which are already installed in mobile platform user never access frequently but it allocates some memory space on hard device storage. In the proposed research work we describe dynamic resource allocation for mobile platforms using deep learning approach. In Real world mobile systems users may install different kind of applications which required ad-hoc basis. Such applications may be affect to execution performance of system as well space complexity, sometime they also affect another runnable applications performance. To eliminate of such issues, we carried out an approach to allocate runtime resources for data storage for mobile platform. When system connected with cloud data server it store complete file system on remote Virtual Machine (VM) and whenever a single application required which immediately install beginning as remote server to local device. For developed of proposed system we implemented deep learning base Convolutional Neural Network (CNN), algorithm has used with tensorflow environment which reduces the time complexity for data storage as well as extraction respectively.

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