Evidence map›Paper›PMID 35898044›Full record

ReviewSensors (Basel, Switzerland)2022

Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview.

Ahmed A Al-Saedi, Veselka Boeva, Emiliano Casalicchio, Peter Exner

Abstract readReview
In one paragraph

Review 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 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.

Ahmed A Al-SaediDepartment of Computer Science, Blekinge Institute of Technology, SE-371 79 Karlskrona, Sweden.ORCID 0000-0001-6061-0861
Veselka BoevaDepartment of Computer Science, Blekinge Institute of Technology, SE-371 79 Karlskrona, Sweden.ORCID 0000-0003-3128-191X
Emiliano CasalicchioDepartment of Computer Science, Blekinge Institute of Technology, SE-371 79 Karlskrona, Sweden.ORCID 0000-0002-3118-5058
Peter ExnerSony, R&D Center Europe, SE-221 88 Lund, Sweden.ORCID 0000-0002-9629-786X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

Indexed as

Computer Communication NetworksWireless TechnologyArtificial IntelligenceHumansartificial intelligencecontext-awarenessedge computingwireless sensor network

Identifiers

PMID35898044
PMCPMC9371178

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.