Evidence map›Paper›PMID 29757236›Full record

ArticleSensors (Basel, Switzerland)2018

An Effective Delay Reduction Approach through a Portion of Nodes with a Larger Duty Cycle for Industrial WSNs.

Minrui Wu, Yanhui Wu, Chuyao Liu, Zhiping Cai, Neal N Xiong, Anfeng Liu, Ming Ma

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Minrui WuSchool of Information Science and Engineering, Central South University, Changsha 410083, China. mywmr@163.com.
Yanhui WuCollege of computer and information engineering, Hunan University of Commerce, Changsha 410205, China. csuwyh@163.com.
Chuyao LiuSchool of Software, Central South University, Changsha 410075, China. liuchuyao@csu.edu.cn.
Zhiping CaiCollege of Computer, National University of Defense Technology, Changsha 410073, China. zpcai@nudt.edu.cn.
Neal N XiongDepartment of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA. xiongnaixue@gmail.com.ORCID 0000-0002-0394-4635
Anfeng LiuSchool of Information Science and Engineering, Central South University, Changsha 410083, China. afengliu@mail.csu.edu.cn.ORCID 0000-0001-5190-4761
Ming MaDepartment of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA. minma@cs.stonybrook.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For Industrial Wireless Sensor Networks (IWSNs), sending data with timely style to the stink (or control center, CC) that is monitored by sensor nodes is a challenging issue. However, in order to save energy, wireless sensor networks based on a duty cycle are widely used in the industrial field, which can bring great delay to data transmission. We observe that if the duty cycle of a small number of nodes in the network is set to 1, the sleep delay caused by the duty cycle can be effectively reduced. Thus, in this paper, a novel Portion of Nodes with Larger Duty Cycle (PNLDC) scheme is proposed to reduce delay and optimize energy efficiency for IWSNs. In the PNLDC scheme, a portion of nodes are selected to set their duty cycle to 1, and the proportion of nodes with the duty cycle of 1 is determined according to the energy abundance of the area in which the node is located. The more the residual energy in the region, the greater the proportion of the selected nodes. Because there are a certain proportion of nodes with the duty cycle of 1 in the network, the PNLDC scheme can effectively reduce delay in IWSNs. The performance analysis and experimental results show that the proposed scheme significantly reduces the delay for forwarding data by 8.9~26.4% and delay for detection by 2.1~24.6% without reducing the network lifetime when compared with the fixed duty cycle method. Meanwhile, compared with the dynamic duty cycle strategy, the proposed scheme has certain advantages in terms of energy utilization and delay reduction.

Indexed as

duty cycleenergy efficientindustrial wireless sensor networkslifetimesleep delay

Identifiers

PMID29757236
PMCPMC5982409

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.