Evidence map›Paper›PMID 33271857›Full record

ArticleSensors (Basel, Switzerland)2020

LoRa Architecture for V2X Communication: An Experimental Evaluation with Vehicles on the Move.

Khandaker Foysal Haque, Ahmed Abdelgawad, Venkata Prasanth Yanambaka, Kumar Yelamarthi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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

4 authors.

Khandaker Foysal HaqueCollege of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48859, USA.ORCID 0000-0003-2791-6863
Ahmed AbdelgawadCollege of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48859, USA.
Venkata Prasanth YanambakaCollege of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48859, USA.
Kumar YelamarthiCollege of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48859, USA.ORCID 0000-0002-0072-3909

Funding

National Science Foundation 1542368
6 · The paper itself

Abstract

The industrial development of the last few decades has prompted an increase in the number of vehicles by multiple folds. With the increased number of vehicles on the road, safety has become one of the primary concerns. Inter vehicular communication, specially Vehicle to Everything (V2X) communication can address these pressing issues including autonomous traffic systems and autonomous driving. The reliability and effectiveness of V2X communication greatly depends on communication architecture and the associated wireless technology. Addressing this challenge, a device-to-device (D2D)-based reliable, robust, and energy-efficient V2X communication architecture is proposed with LoRa wireless technology. The proposed system takes a D2D communication approach to reduce the latency by offering direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, rather than routing the data via the LoRa WAN server. Additionally, the proposed architecture offers modularity and compact design, making it ideal for legacy systems without requiring any additional hardware. Testing and analysis suggest the proposed system can communicate reliably with roadside infrastructures and other vehicles at speeds ranging from 15-50 km per hour (kmph). The data packet consists of 12 bytes of metadata and 28 bytes of payload. At 15 kmph, a vehicle sends one data packet every 25.9 m, and at 50 kmph, it sends the same data packet every 53.34 m with reliable transitions.

Indexed as

LoRareliable V2X communicationvehicle to everything (V2X)vehicle to infrastructure (V2I)vehicle to vehicle (V2V)

Identifiers

PMID33271857
PMCPMC7730391

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.