Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration.

Setia Budi, Paulo de Souza, Greg Timms, Vishv Malhotra, Paul Turner

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Mobile platform sampling for designing environmental sensor networks.Environmental monitoring and assessment · 2018
Article

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.

Setia BudiSchool of Engineering and ICT, University of Tasmania, Private Bag 87, Hobart, TAS 7001, Australia. Setia.Budi@utas.edu.au.

Paulo de SouzaCommonwealth Scientific and Industrial Research Organisation, 15 College Road, Sandy Bay, TAS 7005, Australia. Paulo.Desouza@csiro.au.

Greg TimmsCommonwealth Scientific and Industrial Research Organisation, 15 College Road, Sandy Bay, TAS 7005, Australia. Greg.Timms@csiro.au.

Vishv MalhotraSchool of Engineering and ICT, University of Tasmania, Private Bag 87, Hobart, TAS 7001, Australia. Vishv.Malhotra@utas.edu.au.

Paul TurnerSchool of Engineering and ICT, University of Tasmania, Private Bag 87, Hobart, TAS 7001, Australia. Paul.Turner@utas.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of sensor nodes in the Region of Interest (RoI). Data quality issues are introduced to simulate their impact on the performance of the ESN. Spatial Regression Test (SRT) is also utilised to promote robustness in data quality of the designed ESN. The proposed method provides high network representativeness (fit for purpose) with minimum sensor redundancy (cost), and ensures robustness by enabling the network to continue to achieve its objectives when some sensors fail.

Indexed as

data qualityenvironmental sensor networksevolutionary algorithmgap fillingnoise detectionoptimisationsensor networks deploymentsensor networks designspatial regression test

Identifiers

PMID26633392

DOI10.3390/s151229765

PMCPMC4721686

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

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.