arrow
返回

PEGASIS: Power-efficient GAthering in sensor information systems

delete2024-09-20
delete1.3K
PRE
AI
L
Lindsey, S *
R
Raghavendra, CS
DOI:10.1109/aero.2002.1035242delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
Sensor webs consisting of nodes with limited battery power and wireless communications are deployed to collect useful information from the field. Gathering sensed information in an energy efficient manner is critical to operate the sensor network for a long period of time. In [3] a data collection problem is defined where, in a round of communication, each sensor node has a packet to be sent to the distant base station. If each node transmits its sensed data directly to the base station then it will deplete its power quickly. The LEACH protocol presented in [3] is an elegant solution where clusters are formed to fuse data before transmitting to the base station. By randomizing the cluster heads chosen to transmit to the base station, LEACH achieves a factor of 8 improvement compared to direct transmissions, as measured in terms of when nodes die. In this paper, we propose PEGASIS (Power-Efficient GAthering in Sensor Information Systems), a near optimal chain-based protocol that is an improvement over LEACH. In PEGASIS, each node communicates only with a close neighbor and takes turns transmitting to the base station, thus reducing the amount of energy spent per round. Simulation results show that PEGASIS performs better than LEACH by about 100 to 300% when 1%, 20%, 50%, and 100% of nodes die for different network sizes and topologies.
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

I
IEEE Aerospace Conference Proceedings
IF:
0
论文数:
5
被引数:
0

机构

暂无机构信息
引用论文

引用论文

Smart Probabilistic Fingerprinting for Indoor Localization over Fog Computing Platforms
err2016-10-01
err0
errOAAI
errAndrea Sciarrone; Claudio Fiandrino; Igor Bisio; Fabio Lavagetto; Dzmitry Kliazovich; Pascal Bouvry
err分享
err收藏
Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip
err2021-03-03
err0
errOAAI
errElena Goi; Xi Chen; Qiming Zhang; Benjamin P. Cumming; Steffen Schoenhardt; Haitao Luan; Min Gu
err分享
err收藏
A reduced data bandwidth integrated electrode driver for visual intracortical neural stimulation in 0.35μm high voltage CMOS
err2013-04-01
err0
PREAI
errJean-Michel Redouté; Damien Browne; David Fitrio; Arthur Lowery; Lindsay Kleeman
err分享
err收藏