Evidence map›Paper›PMID 33535483›Full record

ArticleSensors (Basel, Switzerland)2021

Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements.

Carlos Egas Acosta, Felipe Gil-Castiñeira, Enrique Costa-Montenegro, Jorge Sá Silva

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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.

  1. Article
  2. Article
  3. 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.

Carlos Egas AcostaGI-IoE Internet of Every Things Research Group, Escuela Politécnica Nacional, Quito E11-253, Ecuador.ORCID 0000-0002-3540-9768
Felipe Gil-CastiñeiraatlanTTic Research Center for Telecommunication Technologies, Universidade de Vigo, Information Technologies Group, 36310 Vigo, Spain.ORCID 0000-0002-5164-0855
Enrique Costa-MontenegroatlanTTic Research Center for Telecommunication Technologies, Universidade de Vigo, Information Technologies Group, 36310 Vigo, Spain.
Jorge Sá SilvaDepartment of Electrical and Computer Engineering, INESC Coimbra, University of Coimbra, P-3030 Coimbra, Portugal.

Funding

Xunta de Galicia GRC2018/053
6 · The paper itself

Abstract

End-to-end reliability for Wireless Sensor Network communications is usually provided by upper stack layers. Furthermore, most of the studies have been related to star, mesh, and tree topologies. However, they rarely consider the requirements of the multi-hop linear wireless sensor networks, with thousands of nodes, which are universally used for monitoring applications. Therefore, they are characterized by long delays and high energy consumption. In this paper, we propose an energy efficient link level routing algorithm that provides end-to-end reliability into multi-hop wireless sensor networks with a linear structure. The algorithm uses implicit acknowledgement to provide reliability and connectivity with energy efficiency, low latency, and fault tolerance in linear wireless sensor networks. The proposal is validated through tests with real hardware. The energy consumption and the delay are also mathematically modeled and analyzed. The test results show that our algorithm decreases the energy consumption and minimizes the delays when compared with other proposals that also apply the explicit knowledge technique and routing protocols with explicit confirmations, maintaining the same characteristics in terms of reliability and connectivity.

Indexed as

access protocolsindustrial communicationlinear networkswireless sensor networks

Identifiers

PMID33535483
PMCPMC7867064

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.