Preemptive Epidemic Information Transmission Model Using Nonreplication Edge Node Connectivity in Health Care Networks.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Big Data Pub Date : 2024-04-01 Epub Date: 2023-04-19 DOI:10.1089/big.2022.0278
Chandu Thota, Constandinos X Mavromoustakis, George Mastorakis
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Abstract

The reliability in medical data organization and transmission is eased with the inheritance of information and communication technologies in recent years. The growth of digital communication and sharing medium imposes the necessity for optimizing the accessibility and transmission of sensitive medical data to the end-users. In this article, the Preemptive Information Transmission Model (PITM) is introduced for improving the promptness in medical data delivery. This transmission model is designed to acquire the least communication in an epidemic region for seamless information availability. The proposed model makes use of a noncyclic connection procedure and preemptive forwarding inside and outside the epidemic region. The first is responsible for replication-less connection maximization ensuring better availability of the edge nodes. The connection replications are reduced using the pruning tree classifiers based on the communication time and delivery balancing factor. The later process is responsible for the reliable forwarding of the acquired data using a conditional selection of the infrastructure units. Both the processes of PITM are accountable for improving the delivery of observed medical data, over better transmissions, communication time, and achieving fewer delays.

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在医疗网络中使用无复制边缘节点连接的抢先式流行病信息传输模型
近年来,随着信息和通信技术的发展,医疗数据组织和传输的可靠性得到了提高。随着数字通信和共享媒介的发展,有必要优化敏感医疗数据对终端用户的访问和传输。本文介绍了抢先信息传输模型(PITM),以提高医疗数据传输的及时性。该传输模型旨在获取疫区内最少的通信量,以实现信息的无缝可用性。所提出的模型利用非循环连接程序和疫区内外的抢先转发。前者负责无复制连接的最大化,确保边缘节点更好的可用性。根据通信时间和传输平衡因素,使用剪枝树分类器减少连接复制。后一个流程负责通过有条件地选择基础设施单元,可靠地转发获取的数据。PITM 的这两个过程都负责改进所观察到的医疗数据的传输,以获得更好的传输效果、更短的通信时间和更少的延迟。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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