Optimal cluster head localization for cluster-based wireless sensor network using free-space optical technology and genetic algorithm optimization

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-08-28 DOI:10.1007/s12652-024-04849-0
Yousef E. M. Hamouda
{"title":"Optimal cluster head localization for cluster-based wireless sensor network using free-space optical technology and genetic algorithm optimization","authors":"Yousef E. M. Hamouda","doi":"10.1007/s12652-024-04849-0","DOIUrl":null,"url":null,"abstract":"<p>Free Space Optical (FSO) is a wireless communication technology that is distinguished from other communication systems by several advantages including license free of operating spectrum, high data rate, low installation cost, and fast deployment. FSO is employed in many applications including Internet and mobile services links. Nevertheless, FSO link quality is affected by weather conditions including fog, rain, and snow. The main challenge of the FSO channel is the dynamic fluctuating of these weather conditions which degrade the link quality and reduces the data rate. Therefore, the development of robust FSO link topology is a crucial issue to overcome the bad and severe weather conditions. Cluster-based Wireless Sensor Network (WSN) arranges the network into groups called clusters where one Cluster Head (CH) is selected to manage the communication activities inside the group. CHs localization is the main challenge in cluster-based WSN. The key objective of this research is to develop cluster-based WSN that employs the FSO links to interconnect the CHs to each other. Optimal Cluster Head Localization (OCHL) algorithm is developed to optimally determined the locations of CHs so that the network diversity and coverage area of CHs are improved. Genetic Algorithm (GA) technique is used to obtain a near-optimal solution for the proposed fitness function. Simulation results show that the proposed OCHL algorithm improves the network diversity and coverage area of cluster-based WSN. The weighting parameter of the proposed fitness function can be adjusted to control the effects of covered areas, and link diversity in the fitness function. Additionally, increasing the number of CHs leads to improve the covered area and link diversity. Furthermore, with growing of the number of GA iterations, a better solution for the proposed optimization problem is obtained. Moreover, the Bit Error Rate and Signal to Noise Ratio of FSO links are evaluated based on the rain rate, snow rate, fog, transmitted power, transmitter and receiver aperture diameters, FSO communication range, and weighting parameter. The results demonstrate that the normalized covered area in case of using the proposed OCHL algorithm outperforms as compared to NFCA and LEACH algorithms with 12.95 and 8.52% rise, respectively. In addition, the proposed OCHL algorithm enhances the normalized link diversity by 14.15 and 19.21%, compared with NFCA and LEACH algorithms, respectively.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Humanized Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12652-024-04849-0","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0

Abstract

Free Space Optical (FSO) is a wireless communication technology that is distinguished from other communication systems by several advantages including license free of operating spectrum, high data rate, low installation cost, and fast deployment. FSO is employed in many applications including Internet and mobile services links. Nevertheless, FSO link quality is affected by weather conditions including fog, rain, and snow. The main challenge of the FSO channel is the dynamic fluctuating of these weather conditions which degrade the link quality and reduces the data rate. Therefore, the development of robust FSO link topology is a crucial issue to overcome the bad and severe weather conditions. Cluster-based Wireless Sensor Network (WSN) arranges the network into groups called clusters where one Cluster Head (CH) is selected to manage the communication activities inside the group. CHs localization is the main challenge in cluster-based WSN. The key objective of this research is to develop cluster-based WSN that employs the FSO links to interconnect the CHs to each other. Optimal Cluster Head Localization (OCHL) algorithm is developed to optimally determined the locations of CHs so that the network diversity and coverage area of CHs are improved. Genetic Algorithm (GA) technique is used to obtain a near-optimal solution for the proposed fitness function. Simulation results show that the proposed OCHL algorithm improves the network diversity and coverage area of cluster-based WSN. The weighting parameter of the proposed fitness function can be adjusted to control the effects of covered areas, and link diversity in the fitness function. Additionally, increasing the number of CHs leads to improve the covered area and link diversity. Furthermore, with growing of the number of GA iterations, a better solution for the proposed optimization problem is obtained. Moreover, the Bit Error Rate and Signal to Noise Ratio of FSO links are evaluated based on the rain rate, snow rate, fog, transmitted power, transmitter and receiver aperture diameters, FSO communication range, and weighting parameter. The results demonstrate that the normalized covered area in case of using the proposed OCHL algorithm outperforms as compared to NFCA and LEACH algorithms with 12.95 and 8.52% rise, respectively. In addition, the proposed OCHL algorithm enhances the normalized link diversity by 14.15 and 19.21%, compared with NFCA and LEACH algorithms, respectively.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用自由空间光学技术和遗传算法优化基于集群的无线传感器网络的最优簇头定位
自由空间光学(FSO)是一种无线通信技术,它有别于其他通信系统,具有免许可频谱、数据传输率高、安装成本低和部署速度快等优点。FSO 被广泛应用于互联网和移动服务链路等领域。然而,FSO 链路质量受雾、雨和雪等天气条件的影响。FSO 信道面临的主要挑战是这些天气条件的动态变化,它们会降低链路质量并降低数据传输速率。因此,开发稳健的 FSO 链路拓扑是克服恶劣天气条件的关键问题。基于簇的无线传感器网络(WSN)将网络划分为一个个称为 "簇 "的组,并选择一个簇头(CH)来管理组内的通信活动。CH 的定位是基于集群的 WSN 所面临的主要挑战。本研究的主要目标是开发基于簇的 WSN,利用 FSO 链路实现 CH 之间的相互连接。本研究开发了最优簇头定位(OCHL)算法,以优化确定 CHs 的位置,从而提高网络多样性和 CHs 的覆盖范围。该算法采用遗传算法(GA)技术,为所提出的适应度函数获得接近最优的解决方案。仿真结果表明,所提出的 OCHL 算法改善了基于集群的 WSN 的网络多样性和覆盖范围。可以通过调整拟合函数的权重参数来控制覆盖区域和链路多样性对拟合函数的影响。此外,CHs 数量的增加也会提高覆盖面积和链路多样性。此外,随着 GA 迭代次数的增加,提议的优化问题会得到更好的解决方案。此外,还根据雨率、雪率、雾、发射功率、发射器和接收器孔径、FSO 通信范围以及加权参数,评估了 FSO 链路的比特误码率和信噪比。结果表明,与 NFCA 算法和 LEACH 算法相比,使用建议的 OCHL 算法的归一化覆盖面积分别提高了 12.95% 和 8.52%。此外,与 NFCA 算法和 LEACH 算法相比,拟议的 OCHL 算法分别提高了 14.15% 和 19.21% 的归一化链路多样性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
期刊最新文献
Predicting the unconfined compressive strength of stabilized soil using random forest coupled with meta-heuristic algorithms Expressive sign language system for deaf kids with MPEG-4 approach of virtual human character MEDCO: an efficient protocol for data compression in wireless body sensor network A multi-objective gene selection for cancer diagnosis using particle swarm optimization and mutual information Partial policy hidden medical data access control method based on CP-ABE
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1