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A Survey of Beacon-Enabled IEEE 802.15.4 MAC Protocols in Wireless Sensor Networks

2014· article· en· W2108926575 on OpenAlexaff
Mounib Khanafer, Mouhcine Guennoun, Hussein T. Mouftah

Bibliographic record

VenueIEEE Communications Surveys & Tutorials · 2014
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer networkInter-Access Point ProtocolComputer scienceNetwork allocation vectorIEEE 802.11sWireless distribution systemIEEE 802.15Multiple Access with Collision Avoidance for WirelessIEEE 802.1XWireless sensor networkService setIEEE 802.11e-2005Media access controlIEEE 802.11uNeuRFonWireless networkIEEE 802.11Key distribution in wireless sensor networksWirelessWi-Fi arrayTelecommunicationsWireless mesh network

Abstract

fetched live from OpenAlex

IEEE 802.15.4 is the de facto standard for Wireless Sensor Networks (WSNs) that outlines the specifications of the PHY layer and MAC sub-layer in these networks. The MAC protocol is needed to orchestrate sensor nodes access to the wireless communication medium. Although distinguished by a set of strengths that contributed to its popularity in various WSNs, IEEE 802.15.4 MAC suffers from several limitations that play a role in deteriorating its performance. Also, from a practical perspective, 80.15.4-based networks are usually deployed in the vicinity of other wireless networks that operate in the same ISM band. This means that 802.15.4 MAC should be ready to cope with interference from other networks. These facts have motivated efforts to devise improved IEEE 802.15.4 MAC protocols for WSNs. In this paper we provide a survey for these protocols and highlight the methodologies they follow to enhance the performance of the IEEE 802.15.4 MAC protocol.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.310
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations118
Published2014
Admission routes1
Has abstractyes

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