{"title":"基于链路质量和能效优化的简化集群路由方案,提高无线体域网络的生存期","authors":"V. Irine Shyja , G. Ranganathan , V. Bindhu","doi":"10.1016/j.nancom.2023.100465","DOIUrl":null,"url":null,"abstract":"<div><p>Monitoring of patient’s health in the medical industry can be enabled using wireless body area networks<span><span><span> (WBANs), which are already used for various purposes, including assisting in human safety. It is imperative to use better power management strategies since the body sensors are small and the battery cannot hold a charge for a long time. Due to the vast amounts of information generated by medical sensors, resource-constrained networks face a significant challenge when guaranteeing the specified quality of service (QoS). Moreover, the WBAN regularly meets the primary hassle of QoS degradation because of congestion WBAN structure can easily compromise heterogeneous and complex networks. Either inappropriate data collection or using energy effectively to transmit medical data without the expense of travel and length has become an important one. To address this issue, the present research work ‘Link Quality and Energy Efficient Optimal Clustering-Multipath (LEOC-MP)’ scheme tries to explore an answer. The main goals of the LEOC-MP (Optimal Link Quality and Energy Efficient Optimal Clustering-Multipath) system are to guarantee node-to-node link quality, lengthen network life, and compute high-performing cluster heads to guarantee reliable </span>multi path data transfer. This work was executed in three phases. First, an optimal simplified </span>clustering technique<span> for data collection from body sensors using an improved pelican optimization (ICO) algorithm is introduced. Next, multiple design constraints for node rank computation, energy efficiency, link quality, path loss, distance, and delay are used. Besides, an Auto-Regressive Probabilistic Neural Network (AR-PNN) is introduced to optimize those design constraints and compute the cluster head (CH) of each cluster. Multipath firing is then performed using a moderated puffer-fish optimization (MPO) algorithm that finds the closest optimal and shortest node to transmit optimal drug data. The work is simulated using an NS-3 environment, and the results are obtained. The outcome of this work is analyzed with existing methodologies, and the results prove that the present work consistently outperforms the existing methodologies.</span></span></p></div>","PeriodicalId":54336,"journal":{"name":"Nano Communication Networks","volume":"37 ","pages":"Article 100465"},"PeriodicalIF":2.9000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Link quality and energy efficient optimal simplified cluster based routing scheme to enhance lifetime for wireless body area networks\",\"authors\":\"V. Irine Shyja , G. Ranganathan , V. Bindhu\",\"doi\":\"10.1016/j.nancom.2023.100465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Monitoring of patient’s health in the medical industry can be enabled using wireless body area networks<span><span><span> (WBANs), which are already used for various purposes, including assisting in human safety. It is imperative to use better power management strategies since the body sensors are small and the battery cannot hold a charge for a long time. Due to the vast amounts of information generated by medical sensors, resource-constrained networks face a significant challenge when guaranteeing the specified quality of service (QoS). Moreover, the WBAN regularly meets the primary hassle of QoS degradation because of congestion WBAN structure can easily compromise heterogeneous and complex networks. Either inappropriate data collection or using energy effectively to transmit medical data without the expense of travel and length has become an important one. To address this issue, the present research work ‘Link Quality and Energy Efficient Optimal Clustering-Multipath (LEOC-MP)’ scheme tries to explore an answer. The main goals of the LEOC-MP (Optimal Link Quality and Energy Efficient Optimal Clustering-Multipath) system are to guarantee node-to-node link quality, lengthen network life, and compute high-performing cluster heads to guarantee reliable </span>multi path data transfer. This work was executed in three phases. First, an optimal simplified </span>clustering technique<span> for data collection from body sensors using an improved pelican optimization (ICO) algorithm is introduced. Next, multiple design constraints for node rank computation, energy efficiency, link quality, path loss, distance, and delay are used. Besides, an Auto-Regressive Probabilistic Neural Network (AR-PNN) is introduced to optimize those design constraints and compute the cluster head (CH) of each cluster. Multipath firing is then performed using a moderated puffer-fish optimization (MPO) algorithm that finds the closest optimal and shortest node to transmit optimal drug data. The work is simulated using an NS-3 environment, and the results are obtained. 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引用次数: 3
摘要
在医疗行业中,可以使用无线身体区域网络(wban)来监测患者的健康状况,无线身体区域网络已经用于各种目的,包括协助人类安全。由于身体传感器很小,电池不能长时间充电,因此必须采用更好的电源管理策略。由于医疗传感器产生的大量信息,资源约束网络在保证指定的服务质量(QoS)方面面临着重大挑战。此外,由于拥塞,WBAN结构容易危及异构和复杂的网络,因此经常遇到QoS退化的主要问题。不适当的数据收集或有效地利用能量传输医疗数据,而不增加旅行和长度的费用已成为一个重要的问题。为了解决这一问题,目前的研究工作“链路质量和能效最优聚类-多路径(LEOC-MP)”方案试图探索一个答案。LEOC-MP (Optimal Link Quality and Energy Efficient Optimal Clustering-Multipath)系统的主要目标是保证节点到节点的链路质量,延长网络寿命,计算高性能簇头以保证可靠的多路径数据传输。这项工作分三个阶段进行。首先,介绍了一种基于改进的鹈鹕优化(ICO)算法的身体传感器数据采集优化简化聚类技术。其次,使用节点等级计算、能效、链路质量、路径损耗、距离和延迟等多个设计约束。此外,引入自回归概率神经网络(AR-PNN)对这些设计约束进行优化,并计算每个聚类的簇头(CH)。然后使用缓和的河豚鱼优化(MPO)算法执行多路径发射,该算法找到最近的最佳和最短的节点来传输最佳药物数据。在NS-3环境下进行了仿真,得到了仿真结果。用现有的方法分析了本工作的结果,结果证明本工作始终优于现有的方法。
Link quality and energy efficient optimal simplified cluster based routing scheme to enhance lifetime for wireless body area networks
Monitoring of patient’s health in the medical industry can be enabled using wireless body area networks (WBANs), which are already used for various purposes, including assisting in human safety. It is imperative to use better power management strategies since the body sensors are small and the battery cannot hold a charge for a long time. Due to the vast amounts of information generated by medical sensors, resource-constrained networks face a significant challenge when guaranteeing the specified quality of service (QoS). Moreover, the WBAN regularly meets the primary hassle of QoS degradation because of congestion WBAN structure can easily compromise heterogeneous and complex networks. Either inappropriate data collection or using energy effectively to transmit medical data without the expense of travel and length has become an important one. To address this issue, the present research work ‘Link Quality and Energy Efficient Optimal Clustering-Multipath (LEOC-MP)’ scheme tries to explore an answer. The main goals of the LEOC-MP (Optimal Link Quality and Energy Efficient Optimal Clustering-Multipath) system are to guarantee node-to-node link quality, lengthen network life, and compute high-performing cluster heads to guarantee reliable multi path data transfer. This work was executed in three phases. First, an optimal simplified clustering technique for data collection from body sensors using an improved pelican optimization (ICO) algorithm is introduced. Next, multiple design constraints for node rank computation, energy efficiency, link quality, path loss, distance, and delay are used. Besides, an Auto-Regressive Probabilistic Neural Network (AR-PNN) is introduced to optimize those design constraints and compute the cluster head (CH) of each cluster. Multipath firing is then performed using a moderated puffer-fish optimization (MPO) algorithm that finds the closest optimal and shortest node to transmit optimal drug data. The work is simulated using an NS-3 environment, and the results are obtained. The outcome of this work is analyzed with existing methodologies, and the results prove that the present work consistently outperforms the existing methodologies.
期刊介绍:
The Nano Communication Networks Journal is an international, archival and multi-disciplinary journal providing a publication vehicle for complete coverage of all topics of interest to those involved in all aspects of nanoscale communication and networking. Theoretical research contributions presenting new techniques, concepts or analyses; applied contributions reporting on experiences and experiments; and tutorial and survey manuscripts are published.
Nano Communication Networks is a part of the COMNET (Computer Networks) family of journals within Elsevier. The family of journals covers all aspects of networking except nanonetworking, which is the scope of this journal.