ESCVAD: An Energy-Saving Routing Protocol Based on Voronoi Adaptive Clustering for Wireless Sensor Networks

计算机科学 路由协议 聚类分析 无线传感器网络 计算机网络 沃罗诺图 能源消耗 区域路由协议 无线路由协议 分布式计算 分层路由 布线(电子设计自动化) 工程类 数学 电气工程 机器学习 几何学
作者
Ning Ma,Hang Zhang,Hang Hu,Yuan Qin
出处
期刊:IEEE Internet of Things Journal [Institute of Electrical and Electronics Engineers]
卷期号:9 (11): 9071-9085 被引量:76
标识
DOI:10.1109/jiot.2021.3120744
摘要

An excellent routing protocol is important for wireless sensor network (WSN) construction and efficient data transmission. With the continuous expansion of the application scenarios and scopes of the Internet of Things, the existing WSN routing protocols are no longer suitable for the complex network structure and the huge demands of communications. Aiming at these issues of existing routing protocols, such as short network lifetime caused by high energy consumption and uneven distribution of surviving nodes, this article proposes an energy-saving clustering protocol based on adaptive Voronoi dividing, named energy-saving clustering by Voronoi adaptive dividing (ESCVAD) protocol. The innovation of ESCVAD protocol lies in the adaptive clustering algorithm based on Voronoi dividing and cluster head election optimization algorithm based on distance and energy comprehensive weighting. The advantage of proposed algorithms is effectively to balance the energy consumption between cluster head nodes and cluster member nodes. The simulation results show that, compared with the traditional routing protocols, such as low energy adaptive clustering hierarchy (LEACH) protocol and stable energy protocol (SEP), the proposed ESCVAD protocol can effectively reduce the clustering frequency and cluster head electing frequency, so as to reduce signaling interaction frequency, finally result in the energy consumption down and the network lifetime up. Among the six protocols for comparation, ESCVAD has the best network lifetime and energy efficiency.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Lucas的应助被liu1900ab采纳,获得10
刚刚
saw完成签到,获得积分10
刚刚
1秒前
1秒前
c程序语言发布了新的文献求助10
3秒前
hh发布了新的文献求助80
3秒前
殿书发布了新的文献求助10
4秒前
songsong完成签到 ,获得积分10
5秒前
秀秀秀发布了新的文献求助10
6秒前
xiaolizi发布了新的文献求助10
6秒前
北冰洋煮咖啡完成签到,获得积分10
7秒前
ly发布了新的文献求助10
7秒前
科研通AI6.4的应助被master采纳,获得10
7秒前
香蕉觅云的应助被hkh采纳,获得10
8秒前
11秒前
12秒前
李健的应助被刻苦珊珊采纳,获得10
12秒前
12秒前
英姑的应助被ray采纳,获得30
15秒前
15秒前
讨厌夏天发布了新的文献求助20
15秒前
15秒前
18秒前
菥1016完成签到,获得积分10
18秒前
man发布了新的文献求助10
18秒前
CodeCraft的应助被Awen采纳,获得10
18秒前
听雨白陌发布了新的文献求助10
19秒前
丘比特的应助被llll采纳,获得10
19秒前
20秒前
DH发布了新的文献求助10
20秒前
板栗完成签到,获得积分10
21秒前
CodeCraft的应助被顺利毕业就好采纳,获得10
21秒前
NexusExplorer的应助被ly采纳,获得10
22秒前
科研通AI6.2的应助被lee采纳,获得10
22秒前
lilily12376完成签到,获得积分10
22秒前
无极微光的应助被李文达采纳,获得20
22秒前
22秒前
冬冬完成签到,获得积分10
23秒前
liu1900ab完成签到,获得积分10
23秒前
Akim的应助被正直三颜采纳,获得10
23秒前
高分求助中
(应助此贴封号)通过应助OA文献获取积分 10000
Rosenblum, Global Change Biology 800
The Student's Guide to Social Neuroscience 600
Computational Chemical Reaction Engineering: Modeling, Simulation, and Design with MATLAB 600
Organizational Behavior 510
Management and the Arts 510
A Will for the Machine: Computerization, Automation, and the Arts in South Africa 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 计算机科学 工程类 纳米技术 内科学 物理 有机化学 化学工程 生物化学 复合材料 光电子学 细胞生物学 心理学 量子力学 催化作用 物理化学 电极
热门帖子
关注 科研通微信公众号,转发送积分 7811081
求助须知:如何正确求助?哪些是违规求助? 9342785
关于积分的说明 20514212
捐赠科研通 7403993
什么是DOI,文献DOI怎么找? 3329655
关于科研通互助平台的介绍 2476408
邀请新用户注册赠送积分活动 2348584