Evidence map›Paper›PMID 39943334›Full record

ArticleSensors (Basel, Switzerland)2025

Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN.

Hafiza Zunera Abdul Sattar, Huma Ghafoor, Insoo Koo

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

3 authors.

Hafiza Zunera Abdul SattarSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
Huma GhafoorSchool of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.ORCID 0000-0002-4640-4233
Insoo KooDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 77024, Republic of Korea.ORCID 0000-0001-7476-8782

Funding

National Research Foundation of Korea 2021RIS-003National University of Sciences and Technology NUST-22-41-65
6 · The paper itself

Abstract

Establishing a safe and stable routing path for a source-destination pair is necessary regardless of the weather conditions. The reason for this is that vehicles can improve safety on the road by exchanging messages and updating each other on the current conditions of both roads and vehicles. This paper intends to solve the problem of when foggy roads make it difficult for drivers to travel, especially when people encounter emergency situations and have no other option but to drive in foggy weather. Although the literature offers few solutions to the problem, no one has considered integrating software-defined networking into vehicular networks for foggy roads to create an optimal routing path. Moreover, it is of significance to mention that vehicles in adverse weather conditions travel following each other and maintaining a constant safety distance, which leads to the formation of a platoon. Considering this, we propose a heterogeneous communication protocol in a software-defined vehicular network to establish an optimal routing path using platoons on foggy highways. Different cases were tested to show how platoons behave in high connectivity and sparsity, achieving a maximum delivery ratio of 99%, a delay of 2 ms, an overhead of 55%, and an acceptable number of hops compared to reference schemes.

Indexed as

local controllers (LCs)main controller (MC)platoonssoftware-defined vehicular network (SDVN)vehicle-to-infrastructure (V2I)vehicle-to-vehicle (V2V)

Identifiers

PMID39943334
PMCPMC11820606

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.