Evidence map›Paper›PMID 40479342›Full record

ArticleMobile networks and applications : MONET2023

Power-Aware Fog Supported IoT Network for Healthcare Infrastructure Using Swarm Intelligence-Based Algorithms.

Hafiz Munsub Ali, Alain Bertrand Bomgni, Syed Ahmad Chan Bukhari, Tahir Hameed, Jun Liu

Abstract read
In one paragraph

Article in Mobile networks and applications : MONET, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Hafiz Munsub AliInformation Systems in Computer Science Department, SUNY at Binghamton, Binghamton, NY USA.ORCID 0000-0003-0688-9106
Alain Bertrand BomgniDepartment of Biomedical Engineering, University of South Dakota, Vermillion, SD USA.
Syed Ahmad Chan BukhariDivision of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John's University, Queens, NY USA.
Tahir HameedGirard School of Business, Merrimack College, North Andover, MA USA.
Jun LiuCollege of Business and Information Systems, Dakota State University, Madison, SD USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Healthcare services become increasingly technology dependent every passing day such as the Internet of Things (IoT), Fog Computing, 5th generation (5G) and beyond communications, etc. They enable the processing and exchange of huge volumes of healthcare data whose integrity and real-time delivery are critical for healthcare services. Optimal power consumption in such essential healthcare infrastructure is critical for the well-being of patients and crucial to reduce the operational cost of healthcare facilities. In this paper, a Fog node has been introduced in an IoT healthcare infrastructure with power consumption as a key deciding factor. This work proposed a mathematical formulation to decide the deployment of two heterogeneous gateways in the healthcare infrastructure. The target of optimization is to minimize transmission power and infrastructure costs. Two swarm intelligence-based algorithms have been used to solve the computationally challenging optimization problem. These evolutionary algorithms are a discrete fireworks algorithm and a discrete artificial bee colony algorithm with an ensemble of local search methods. Their performance is compared against the genetic algorithm. The simulation results demonstrate a saving of up to 33 percent in power consumption in the proposed healthcare infrastructure that can significantly improve healthcare data communications and its operational costs.

Indexed as

Fog nodeGateway placementInteger programmingIoT networkMinimize powerOptimization

Identifiers

PMID40479342
PMCPMC9984132

What OpenQuestion holds

Textmetadata
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.