Performance of ZigBee networks in the presence of broadband electromagnetic noise
Notice bibliographique
Résumé
This research project was conducted at TRLabs and the sensor network testbed (SENETBED) at the University of Manitoba, for an international sponsor, Vansco/Parker Inc. The project aimed to determine the impact of electromagnetic noise on the communications performance of a ZigBee sensor network, embedded within a large industrial excavator. The project consisted of five phases. Phase 1 has three deliverables: (i) determined the requirements and chose a wireless technology to replace the wireline technology in the deployment of a sensor control system for industrial machinery; (ii) modeled broadband electromagnetic noise using contemporary fractal theory; and (iii) designed a novel emulation environment for testing the performance of a wireless network under noise. Phase 2 addressed an improved experimental setup, and provided preliminary Packet Error Rate (PER) vs. Signal-to-Noise Ratio (SNR) results, showing the impact of fractal generated noise on ZigBee communications. Phase 3 analyzed the process of modulating a monofractal, and found that the modulated monofractal changed character to a multifractal; however, a small frequency range can be found where the signal is approximately monofractal. Phase 4, (i) captured electromagnetic noise emanated from the starter motor of a large industrial tractor; (ii) performed fractal measurements on the data; and (iii) found that the noise exhibited fractal and multifractal characteristics, verifying that fractal theory is indeed a good model of broadband electromagnetic noise. This paper summarizes the first four phases of this research project, to provide continuity and readability. The results and contributions of Phase 5, the final instalment of this project, include: (i) the SNR was measured more accurately by using a written C#-program, which programmatically captured the ZigBee and noise signals' traces from a spectrum analyzer, and calculated the signal and noise powers directly from the amplitudes. The direct method of SNR measurement improved the accuracy of the PER vs. SNR result; (ii) The SNR measurements were also improved by changing the location of the actual measurements. The SNR measured at the receiver was used to obtain PER vs. SNR data, while the SNR measured at the transmitter SNR was used to obtained maximum node separation data; (iii) noise emulation was improved by injecting the noise into the channel at the receiver; and (iv) the injected noise was tuned to match the character of the captured starter motor noise. In Phase 5, we found that a better model of noise injection was to assume that the noise was uniformly and equally distributed in space, so that the incremental impacts of the noise throughout the channel could be modeled by injecting the noise at the receiver.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».