Evidence map›Paper›PMID 30939722›Full record

ReviewSensors (Basel, Switzerland)2019

Wireless Sensor Networks for Big Data Systems.

Beom-Su Kim, Ki-Il Kim, Babar Shah, Francis Chow, Kyong Hoon Kim

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Data Gathering Techniques in WSN: A Cross-Layer View.Sensors (Basel, Switzerland) · 2022
    Review
  3. Review
  4. Article
  5. Article
  6. 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.

Beom-Su KimDepartment of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea. bumsou10@naver.com.
Ki-Il KimDepartment of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea. kikim@cnu.ac.kr.ORCID 0000-0002-8366-3533
Babar ShahCollege of Technological Innovation, Zayed University, Abu Dhabi 144534, UAE. babar.shah@zu.ac.ae.
Francis ChowUniversity College, Zayed University, Abu Dhabi 144534, UAE. Francis.Chow@zu.ac.ae.
Kyong Hoon KimDepartment of Informatics, Gyeongsang National University, Jinju 52828, Korea. khkim@gnu.ac.kr.

Funding

National Research Foundation of Korea 2018R1D1A1B07043731Zayed University R18027
6 · The paper itself

Abstract

Before discovering meaningful knowledge from big data systems, it is first necessary to build a data-gathering infrastructure. Among many feasible data sources, wireless sensor networks (WSNs) are rich big data sources: a large amount of data is generated by various sensor nodes in large-scale networks. However, unlike typical wireless networks, WSNs have serious deficiencies in terms of data reliability and communication owing to the limited capabilities of the nodes. Moreover, a considerable amount of sensed data are of no interest, meaningless, and redundant when a large number of sensor nodes is densely deployed. Many studies address the existing problems and propose methods to overcome the limitations when constructing big data systems with WSN. However, a published paper that provides deep insight into this research area remains lacking. To address this gap in the literature, we present a comprehensive survey that investigates state-of-the-art research work on introducing WSN in big data systems. Potential applications and technical challenges of networks and infrastructure are presented and explained in accordance with the research areas and objectives. Finally, open issues are presented to discuss promising directions for further research.

Indexed as

big datadata processinginfrastructurewireless sensor networks

Identifiers

PMID30939722
PMCPMC6480280

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.