PMCAR:面向延迟敏感型医疗应用的IoMT主动移动性和拥塞感知路由预测机制,确保新冠疫情下的可靠性

S.Veera pandi, H. PradeepReddyC.
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引用次数: 1

摘要

在物联网(IoT)中加入移动节点(MNs),由于节点的高移动性,进一步增加了网络频繁断开和间歇性连接等挑战。本文旨在提出一种主动移动和感知拥塞的路由预测机制(PMCAR),以寻找从叶子到目的地的无拥塞路由(DODAG-ROOT),该机制考虑了连接到静态节点的nms的数量。本文将PMCAR技术与RPL (OF0)技术进行了比较,RPL (OF0)技术考虑HOP-COUNT来确定从叶子到DODAG-ROOT的路径。作者在MATLAB中对所提出的技术进行了仿真,以展示丢包和能耗方面的好处。在这种大流行的形势下,移动和物联网在预测和预防2019年冠状病毒病(COVID-19)方面发挥着重要作用。在移动设备的帮助下,这次大流行产生的数据正在进行大量的计算。为了将数据通过网络路由到远程位置,需要有适当的路由机制,避免拥塞。本文引入PMCAR机制来实现这一目标。移动物联网(IoMT)是物联网的扩展,由静态嵌入式设备和传感器组成。IoMT包括感知数据并将其传输到DODAG-ROOT的MNs。IoMT中的节点具有低功耗、低内存、低计算能力和低带宽支持的特点。在低功耗无线个人区域网络上为IPV6定义的路由协议遇到了几个挑战,以确保减少数据包丢失,减少延迟,减少能耗和保证服务质量。结果表明,与现有的RPL (OF0)方法相比,该方法有了显著的改进。所提出的路径预测机制可在很大程度上适用于对延迟敏感的医疗应用,特别是在大流行的情况下,涉及的患者数量和从他们收集的数据流向一个中心根进行分析。支持从患者到医生的数据传输,没有太多的延迟和数据包丢失,将使响应或决策更快,这是医疗应用的重要组成部分。新冠疫情下的计算技术需要及时的数据进行计算,没有延迟。IoMT支持各种设备,如移动设备、传感器和可穿戴设备。这些设备专门用于根据预定的时间间隔从不同地理位置收集患者或任何物体的数据。数据的及时传递对精确计算至关重要。因此,有必要建立一个没有延迟和拥塞的路由机制来应对这一疫情。所提出的PMCAR机制保证了数据的可靠传递,可用于即时计算,用于预防和预测决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PMCAR: proactive mobility and congestion aware route prediction mechanism in IoMT for delay sensitive medical applications to ensure reliability in COVID-19 pandemic situation
Inclusion of mobile nodes (MNs) in Internet of Things (IoT) further increases the challenges such as frequent network disconnection and intermittent connectivity because of high mobility rate of nodes. This paper aims to propose a proactive mobility and congestion aware route prediction mechanism (PMCAR) to find the congestion free route from leaf to destination oriented directed acyclic graph root (DODAG-ROOT) which considers number of MNs connected to a static node. This paper compares the proposed technique (PMCAR) with RPL (OF0) which considers the HOP-COUNT to determine the path from leaf to DODAG-ROOT. The authors performed a simulation with the proposed technique in MATLAB to present the benefits in terms of packet loss and energy consumption.,In this pandemic situation, mobile and IoT play major role in predicting and preventing the CoronaVirus Disease of 2019 (COVID-19). Huge amount of computations is happening with the data generated in this pandemic with the help of mobile devices. To route the data to remote locations through the network, it is necessary to have proper routing mechanism without congestion. In this paper, PMCAR mechanism is introduced to achieve the same. Internet of mobile Things (IoMT) is an extension of IoT that consists of static embedded devices and sensors. IoMT includes MNs which sense data and transfer it to the DODAG-ROOT. The nodes in the IoMT are characterised by low power, low memory, low computing power and low bandwidth support. Several challenges are encountered by routing protocols defined for IPV6 over low power wireless personal area networks to ensure reduced packet loss, less delay, less energy consumption and guaranteed quality of service.,The results obtained shows a significant improvement compared to the existing approach such as RPL (OF0). The proposed route prediction mechanism can be applied largely to medical applications which are delay sensitive, particularly in pandemic situations where the number of patients involved and the data gathered from them flows towards a central root for analysis. Support of data transmission from the patients to the doctors without much delay and packet loss will make the response or decisions available more quickly which is a vital part of medical applications.,The computational technologies in this COVID-19 pandemic situation needs timely data for computation without delay. IoMT is enabled with various devices such as mobile, sensors and wearable devices. These devices are dedicated for collecting the data from the patients or any objects from different geographical location based on the predetermined time intervals. Timely delivery of data is essential for accurate computation. So, it is necessary to have a routing mechanism without delay and congestion to handle this pandemic situation. The proposed PMCAR mechanism ensures the reliable delivery of data for immediate computation which can be used to make decisions in preventing and prediction.
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