Evidence map›Paper›PMID 31349738›Full record

ArticleSensors (Basel, Switzerland)2019

Performance Enriching Channel Allocation Algorithm for Vehicle-to-Vehicle City, Highway and Rural Network.

Mohammed Abdulhakim Al-Absi, Ahmed Abdulhakim Al-Absi, Hoon Jae Lee

Abstract read
In one paragraph

Article 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 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.

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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.

Mohammed Abdulhakim Al-AbsiDepartment of Computer Engineering, Graduate School, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Korea.
Ahmed Abdulhakim Al-AbsiDepartment of Smart Computing, Kyungdong University 46 4-gil, Bongpo, Gosung, Gangwon-do 24764, Korea.
Hoon Jae LeeDivision of Information and Communication Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Korea. hjlee@dongseo.ac.kr.

Funding

Institute for Information and communications Technology Promotion No.2018-0-00245National Research Foundation of Korea NRF2016R1D1A1B01011908
6 · The paper itself

Abstract

Future safety applications require the timely delivery of messages between vehicles. The 802.11p has been standardized as the standard Medium Access Control (MAC) protocol for vehicular communication. The 802.11p uses Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) as MAC. CSMA/CA induces unbounded channel access delay. As a result, it induces high collision. To reduce collision, distributed MAC is required for channel allocation. Many existing approaches have adopted Time Division Multiple Access (TDMA) based MAC design for channel allocation. However, these models are not efficient at utilizing bandwidth. Cognitive radio technique is been adopted by various existing approach for channel allocation in shared channel network to maximize system throughput. However, it induces MAC overhead, and channel allocation on a shared channel network is considered to be an NP-hard problem. This work addresses the above issues. Here we present distributed MAC design PECA (Performance Enriching Channel Allocation) for channel allocation in a shared channel network. The PECA model maximizes the system throughput and reduces the collision, which is experimentally proven. Experiments are conducted to evaluate the performance in terms of throughput, collision and successful packet transmission considering a highly congested vehicular ad-hoc network. Experiments are carried out to show the adaptiveness of proposed MAC design considering different environments such City, Highway and Rural (CHR).

Indexed as

DSRCILPMACradio propagationV2IV2VVANET

Identifiers

PMID31349738
PMCPMC6695757

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.