Evidence map›Paper›PMID 36616797›Full record

ReviewSensors (Basel, Switzerland)2022

An Overview of Fog Data Analytics for IoT Applications.

Jitendra Bhatia, Kiran Italiya, Kuldeepsinh Jadeja, Malaram Kumhar, Uttam Chauhan, Sudeep Tanwar, Madhuri Bhavsar, Ravi Sharma, Daniela Lucia Manea, Marina Verdes and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Evolution of Hybrid Intelligence and Its Application in Evidence-Based Medicine: A Review.Medical science monitor : international medical journal of experimental and clinical research · 2023
    Review
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

11 authors.

Jitendra BhatiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India.
Kiran ItaliyaAmazon, Bangalore 560055, India.
Kuldeepsinh JadejaSchool of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287, USA.
Malaram KumharDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India.ORCID 0000-0001-5142-3933
Uttam ChauhanDepartment of Computer Engineering, Vishwakarma Government Engineering College, Gujarat Technological University, Ahmedabad 382424, India.
Sudeep TanwarDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India.ORCID 0000-0002-1776-4651
Madhuri BhavsarDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India.
Ravi SharmaCentre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, Dehradun 248001, India.
Daniela Lucia ManeaFaculty of Civil Engineering, Technical University of Cluj-Napoca, Constantin Daicoviciu Street, No. 15, 400020 Cluj-Napoca, Romania.
Marina VerdesDepartment of Building Services, Faculty of Civil Engineering and Building Services, Technical University of Gheorghe Asachi, 700050 Iași, Romania.
Maria Simona RaboacaFaculty of Civil Engineering, Technical University of Cluj-Napoca, Constantin Daicoviciu Street, No. 15, 400020 Cluj-Napoca, Romania.ORCID 0000-0002-7277-4377

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid growth in the data and processing over the cloud, it has become easier to access those data. On the other hand, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable to some extent. Fog computing is one of the promising solutions for handling the big data produced by the IoT, which are often security-critical and time-sensitive. Massive IoT data analytics by a fog computing structure is emerging and requires extensive research for more proficient knowledge and smart decisions. Though an advancement in big data analytics is taking place, it does not consider fog data analytics. However, there are many challenges, including heterogeneity, security, accessibility, resource sharing, network communication overhead, the real-time data processing of complex data, etc. This paper explores various research challenges and their solution using the next-generation fog data analytics and IoT networks. We also performed an experimental analysis based on fog computing and cloud architecture. The result shows that fog computing outperforms the cloud in terms of network utilization and latency. Finally, the paper is concluded with future trends.

Indexed as

artificial intelligenceblockchaindata analyticsfog computingInternet of things

Identifiers

PMID36616797
PMCPMC9824595

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.