Modeling of pollutant distribution based on mobile sensor networks.

Yong Wang, Yingbin Wang, Xiangli Zhang, Dianhong Wang, Jun Yan

Abstract read

In one paragraph

Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yong WangSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.

Yingbin WangSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.

Xiangli ZhangSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.

Dianhong WangSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China.

Jun YanSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, China. yanjun@cug.edu.cn.

Funding

National Natural Science Foundation of China 61973283National Natural Science Foundation of China (CN) 61771436

6 · The paper itself

Abstract

Pollution monitoring based on wireless sensor networks is becoming highly attractive. This paper presents an effective pollutant distribution modeling approach using a mobile sensor network. As for mobile nodes, energy consumption and link quality between nodes are two key factors. In the proposed approach, we present an autonomous sensing model and an energy-driven motion control scheme, which can make a good trade-off between energy efficiency and modeling accuracy. A comprehensive set of simulations demonstrate that our approach can model the pollutant distribution with less iteration times and higher accuracy. In particular, even for a relatively complex concentration field, the similarity between the reconstructed model and the pollutant distribution can reach 95% through about 20 iterations using 25 mobile sensor nodes. Moreover, we validated the feasibility of the proposed approach through an actual monitoring of water pollutant distribution.

Indexed as

Environmental PollutantsWireless TechnologyData CollectionMotionRemote Sensing TechnologyEnvironmental PollutantsEnergy efficiencyMobile sensor networksMotion controlPollution monitoring

Identifiers

PMID31965502

DOI10.1007/s11356-020-07684-w

What OpenQuestion holds

Textmetadata

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Registered trials

None linked

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.