Evidence map›Paper›PMID 38400368›Full record

ArticleSensors (Basel, Switzerland)2024

An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network.

Nour Zaarour, Nadir Hakem, Nahi Kandil

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Nour ZaarourTelebec Underground Communications Research Laboratory, University of Quebec at Abitibi-Temiscamingue, 675, 1e Avenue, Val-d'Or, QC J9P 1Y3, Canada.ORCID 0009-0006-9556-4360
Nadir HakemTelebec Underground Communications Research Laboratory, University of Quebec at Abitibi-Temiscamingue, 675, 1e Avenue, Val-d'Or, QC J9P 1Y3, Canada.
Nahi KandilTelebec Underground Communications Research Laboratory, University of Quebec at Abitibi-Temiscamingue, 675, 1e Avenue, Val-d'Or, QC J9P 1Y3, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sensor localization remains a crucial function within the context of wireless sensor networks (WSNs) and is a delicate concern that has attracted many researchers' attention. Undoubtedly, a good distance estimation between different wireless sensors allows us to estimate their accurate locations in the network well. In this article, we present a simple but very effective anchor-free localization scheme for wireless sensor networks called the contextual received signal strength approach (CRSSA) localization scheme. We use the received signal strength (RSS) values and the contextual network connectivity within an anchor-free WSN. We present and thoroughly analyze a novel joint estimation methodology for determining the range, path loss exponent (PLE), and inter-node distances in a composite fading model that addresses small-scale multipath fading and large-scale path loss shadowing effects. We formulate analytical expressions for key parameters, the node's communication range and the PLE value, as functions of the sensor's number, the network's connectivity, and the network density. Once these parameters are estimated, we estimate the inter-node distances and the positions of nodes, with relatively high accuracy, based on the assumed propagation model in a two-dimensional anchor-free WSN. The effectiveness of the CRSSA is evaluated through extensive simulations assuring its estimation accuracy in anchor-free localization.

Indexed as

anchor-freeinter-node estimationlocalizationnetwork connectivity contextRSSsmall/large-scale fading channelwireless sensor network

Identifiers

PMID38400368
PMCPMC10893292

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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.