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Record W2076078478 · doi:10.1155/2010/103406

Design and Implementation of a Testbed for IEEE 802.15.4 (Zigbee) Performance Measurements

2010· article· en· W2076078478 on OpenAlexaff
Patrick R. Casey, Kemal Tepe, Narayan C. Kar

Bibliographic record

VenueEURASIP Journal on Wireless Communications and Networking · 2010
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTestbedComputer scienceNeuRFonNetwork packetBit error ratePhysical layerWireless sensor networkWirelessIEEE 802.15Real-time computingComputer networkBandwidth (computing)PHYEmbedded systemWireless networkTelecommunicationsKey distribution in wireless sensor networksChannel (broadcasting)

Abstract

fetched live from OpenAlex

IEEE 802.15.4, commonly known as ZigBee, is a Media Access Control (MAC) and physical layer standard specifically designed for short range wireless communication where low rate, low power, and low bandwidth are required. This makes ZigBee an ideal choice when it comes to sensor networks for monitoring data collection and/or triggering process responses. However, these very characteristics bring into question ZigBee's ability to perform reliably in harsh environments. This paper thoroughly explains the experimental testbed setup and execution to demonstrate ZigBee's performance in several practical applications. This testbed is capable of measuring the minimum, maximum, and average received signal strength indicator (RSSI), bit error rate (BER), packet error rate (PER), packet loss rate (PLR), and the bit error locations. Results show that ZigBee has the potential capabilities to be used in all four tested environments.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.308
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations20
Published2010
Admission routes1
Has abstractyes

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