Evidence map›Paper›PMID 36502034›Full record

ArticleSensors (Basel, Switzerland)2022

SF-Partition-Based Clustering and Relaying Scheme for Resolving Near-Far Unfairness in IoT Multihop LoRa Networks.

Dick Mugerwa, Youngju Nam, Hyunseok Choi, Yongje Shin, Euisin Lee

Abstract read
In one paragraph

Article 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 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

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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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.

Dick MugerwaSchool of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.ORCID 0000-0001-8578-8818
Youngju NamSchool of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.ORCID 0000-0003-3971-0715
Hyunseok ChoiSchool of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.ORCID 0000-0001-9410-7244
Yongje ShinResearch Institute for Computer and Information Communication, Chungbuk National University, Cheongju 28644, Republic of Korea.
Euisin LeeSchool of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.ORCID 0000-0002-0422-0647

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long range (LoRa) is one of the most successful low-power wide-area networking technologies because it is ideally suited for long-distance, low-bit rate, and low-power communications in the unlicensed sub-GHz spectrum utilized for Internet of things (IoT) networks. The effectiveness of LoRa depends on the link budget (i.e., spreading factor (SF), bandwidth (BW), and transmission power (TX)). Due to the near-far effect, the allocation of a link budget to LoRa devices (LDs) in large coverage regions is unfair between them depending on their distance to the GW. Thus, more transmission opportunities are given to some LDs to the detriment of other LD's opportunities. Numerous studies have been conducted to address the prevalent near-far fairness problem. Due to the absence of a tractable analytical model for fairness in the LoRa network, however, it is still difficult to solve this problem completely. Thus, we propose an SF-partition-based clustering and relaying (SFPCR) scheme to achieve enormous LD connectivity with fairness in IoT multihop LoRa networks. For the SF partition, the SFPCR scheme determines the suitable partitioning threshold point for bridging packet delivery success probability gaps between SF regions, namely, the lower SF zone (LSFZ) and the higher SF zone (HSFZ). To avoid long-distance transmissions to the GW, the HSFZ constructs a density-based subspace clustering that generates clusters of arbitrary shape for adjacent LDs and selects cluster headers by using a binary score representation. To support reliable data transmissions to the GW by multihop communications, the LSFZ offers a relay LD selection that ideally chooses the best relay LD to extend uplink transmissions from LDs in the HSFZ. Through simulations, we show that the proposed SFPCR scheme exhibits the highest success probability of 65.7%, followed by the FSRC scheme at 44.6%, the mesh scheme at 34.2%, and lastly the cluster-based scheme at 29.4%, and it conserves the energy of LDs compared with the existing schemes.

Indexed as

Internet of ThingsCluster AnalysisCommunicationPolysaccharide-Lyaseshyaluronate lyasePolysaccharide-Lyaseshigher spreading factor zone (HSFZ)long range (LoRa)lower spreading factor zone (LSFZ)spreading factor (SF)

Identifiers

PMID36502034
PMCPMC9737811

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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.