Suppr超能文献

绿色无线体域网通信:能量感知链路高效路由方法。

Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach.

机构信息

School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81300, Malaysia.

College of Applied Studies and Community Services, King Saud University, Riyadh 11564, Saudi Arabia.

出版信息

Sensors (Basel). 2018 Sep 26;18(10):3237. doi: 10.3390/s18103237.

Abstract

Recent technological advancement in wireless communication has led to the invention of wireless body area networks (WBANs), a cutting-edge technology in healthcare applications. WBANs interconnect with intelligent and miniaturized biomedical sensor nodes placed on human body to an unattended monitoring of physiological parameters of the patient. These sensors are equipped with limited resources in terms of computation, storage, and battery power. The data communication in WBANs is a resource hungry process, especially in terms of energy. One of the most significant challenges in this network is to design energy efficient next-hop node selection framework. Therefore, this paper presents a green communication framework focusing on an energy aware link efficient routing approach for WBANs (ELR-W). Firstly, a link efficiency-oriented network model is presented considering beaconing information and network initialization process. Secondly, a path cost calculation model is derived focusing on energy aware link efficiency. A complete operational framework ELR-W is developed considering energy aware next-hop link selection by utilizing the network and path cost model. The comparative performance evaluation attests the energy-oriented benefit of the proposed framework as compared to the state-of-the-art techniques. It reveals a significant enhancement in body area networking in terms of various energy-oriented metrics under medical environments.

摘要

最近无线通信技术的进步导致了无线体域网(WBAN)的发明,这是医疗应用中的一项前沿技术。WBAN 与放置在人体上的智能和微型化生物医学传感器节点互联,实现对患者生理参数的无人监测。这些传感器在计算、存储和电池功率方面的资源有限。WBAN 中的数据通信是一个资源密集型过程,尤其是在能量方面。该网络面临的最大挑战之一是设计节能的下一跳节点选择框架。因此,本文提出了一种绿色通信框架,重点关注面向能量感知的链路高效路由方法(ELR-W)。首先,提出了一种面向链路效率的网络模型,考虑了信标信息和网络初始化过程。其次,导出了一个专注于能量感知链路效率的路径成本计算模型。通过利用网络和路径成本模型,考虑能量感知的下一跳链路选择,开发了一个完整的 ELR-W 操作框架。与最先进的技术相比,比较性能评估证明了所提出框架在能源方面的优势。它揭示了在医疗环境下,各种面向能源的指标下,体域网的显著增强。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a720/6210318/a07da80cac50/sensors-18-03237-g001.jpg

相似文献

1
3
A comprehensive survey of energy-aware routing protocols in wireless body area sensor networks.
J Med Syst. 2016 Sep;40(9):201. doi: 10.1007/s10916-016-0556-8. Epub 2016 Jul 28.
5
Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN.
Sensors (Basel). 2020 Nov 3;20(21):6267. doi: 10.3390/s20216267.
6
7
Wireless body area network for health monitoring.
J Med Eng Technol. 2019 Feb;43(2):124-132. doi: 10.1080/03091902.2019.1620354. Epub 2019 Jun 18.
9
Energy Efficiency and Reliability Considerations in Wireless Body Area Networks: A Survey.
Comput Math Methods Med. 2022 Jan 17;2022:1090131. doi: 10.1155/2022/1090131. eCollection 2022.

引用本文的文献

2
Lyme rashes disease classification using deep feature fusion technique.
Skin Res Technol. 2023 Nov;29(11):e13519. doi: 10.1111/srt.13519.
3
Sensors for Context-Aware Smart Healthcare: A Security Perspective.
Sensors (Basel). 2021 Oct 17;21(20):6886. doi: 10.3390/s21206886.
4
EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT.
J Healthc Eng. 2021 May 6;2021:9988038. doi: 10.1155/2021/9988038. eCollection 2021.
5
Multiconstraint-Aware Routing Mechanism for Wireless Body Sensor Networks.
J Healthc Eng. 2021 Mar 31;2021:5560809. doi: 10.1155/2021/5560809. eCollection 2021.
6
Secure and energy-efficient framework using Internet of Medical Things for e-healthcare.
J Infect Public Health. 2020 Oct;13(10):1567-1575. doi: 10.1016/j.jiph.2020.06.027. Epub 2020 Jul 15.
8
10
A Survey of Routing Protocols in WBAN for Healthcare Applications.
Sensors (Basel). 2019 Apr 5;19(7):1638. doi: 10.3390/s19071638.

本文引用的文献

1
TraPy-MAC: Traffic Priority Aware Medium Access Control Protocol for Wireless Body Area Network.
J Med Syst. 2017 Jun;41(6):93. doi: 10.1007/s10916-017-0739-y. Epub 2017 May 2.
2
A Survey on Mobility Support in Wireless Body Area Networks.
Sensors (Basel). 2017 Apr 7;17(4):797. doi: 10.3390/s17040797.
3
A comprehensive survey of energy-aware routing protocols in wireless body area sensor networks.
J Med Syst. 2016 Sep;40(9):201. doi: 10.1007/s10916-016-0556-8. Epub 2016 Jul 28.
5
An Efficient Next Hop Selection Algorithm for Multi-Hop Body Area Networks.
PLoS One. 2016 Jan 15;11(1):e0146464. doi: 10.1371/journal.pone.0146464. eCollection 2016.
6
National health expenditure projections, 2014-24: spending growth faster than recent trends.
Health Aff (Millwood). 2015 Aug;34(8):1407-17. doi: 10.1377/hlthaff.2015.0600.
7
Experimental Path Loss Models for In-Body Communications Within 2.36-2.5 GHz.
IEEE J Biomed Health Inform. 2015 May;19(3):930-7. doi: 10.1109/JBHI.2015.2418757. Epub 2015 Apr 1.
8
9
Impact of indoor environment on path loss in body area networks.
Sensors (Basel). 2014 Oct 20;14(10):19551-60. doi: 10.3390/s141019551.
10
A survey of body sensor networks.
Sensors (Basel). 2013 Apr 24;13(5):5406-47. doi: 10.3390/s130505406.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验