Evidence map›Paper›PMID 33379222›Full record

ArticleSensors (Basel, Switzerland)2020

Dynamic Transmit Profile Selection in Dense Wireless Networks.

Łukasz Kułacz, Adrian Kliks, Paweł Kryszkiewicz, Bartosz Bossy

Abstract readLetter
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Łukasz KułaczInstitute of Radiocommunications, Poznan University of Technology, 61-131 Poznan, Poland.ORCID 0000-0002-3434-1917
Adrian KliksInstitute of Radiocommunications, Poznan University of Technology, 61-131 Poznan, Poland.ORCID 0000-0001-6766-7836
Paweł KryszkiewiczInstitute of Radiocommunications, Poznan University of Technology, 61-131 Poznan, Poland.ORCID 0000-0001-9054-9416
Bartosz BossyInstitute of Radiocommunications, Poznan University of Technology, 61-131 Poznan, Poland.ORCID 0000-0002-5979-8235

Funding

Narodowe Centrum Nauki 2016/23/B/ST7/03937
6 · The paper itself

Abstract

The development of wireless networks can be characterized by both the increased number of deployed network nodes as well as their greater heterogeneity. As a consequence, the distance between the neighboring nodes decreases significantly, the density of such a wireless network is very high, and it brings to the mind the analogy to the human brain and nervous system, where a highly simplified scheme of information delivery is applied. Motivated by this similarity, in this paper, we study the possibility of the application of various transmission profiles in order to optimize the overall energy consumption in such dense wireless networks. The transmission profile specifies the radio access and energy consumption of the wireless transceiver (network node), and is characterized by the tuple of parameters, e.g., the total transmit power or minimal required signal-to-noise ratio (SNR). In the considered multi-hop network, we assume that each node can be set to the most promising transmission profile to achieve some predefined goals, such as (sensor) network reliability or transmission energy efficiency. We have proposed the new graph-based routing algorithm in such a dense wireless network, where total power consumption of message delivery is minimized by multihop and multimode transmission. The theoretical definition of the prospective transmission schemes is supported by the analysis of the results of the simulation experiments.

Indexed as

adaptive transmissionbrain-inspired communicationsdense wireless networkenergy consumption

Identifiers

PMID33379222
PMCPMC7795309

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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.