Towards Battery-Free Internet of Things (IoT) Sensors: Far-Field Wireless Power Transfer and Harmonic Backscattering
Bibliographic record
Abstract
RESUME Notre vie tend a etre plus agreable, plus facile et plus efficace grâce a l'evolution rapide de la technologie de l'Internet des objets (IoT). La clef de voute de cette technologie repose essentiellement sur la quantite de capteurs IoT interconnectes, que l’on est en mesure de deployer dans notre environnement. Malheureusement, l’electronique conventionnelle fonctionnant sur piles ou relie au reseau electrique ne peut pas constituer une solution durable en raison des aspects de cout, de faisabilite et d'impact environnemental. Pendant ce temps, le changement climatique du a la consommation excessive de combustibles fossiles continue de s'aggraver. Il devient donc urgent de trouver une solution pour l’alimentation electrique des capteurs IoT geographiquement repartis a grande echelle, afin de simultanement soutenir la mise en oeuvre de nombreux capteurs IoT tout en limitant leur poids environnemental. L'energie radiofrequence (RF) ambiante, qui sert de support a l'information sans fil, est non seulement capitale pour notre societe, mais aussi omnipresente dans les zones urbaines et suburbaines. Elle permet de realiser des communications et des detections sans fil. Cependant, l'energie RF ambiante est majoritairement « gaspillee » car seule une toute petite partie de la puissance transmise est effectivement recu ou « consommee » par le destinataire. C'est pourquoi le recyclage de l'energie RF ambiante est une solution prometteuse pour alimenter les capteurs IoT. Pour certains capteurs IoT consommant une puissance plus elevee, l’apport d'energie sans fil pourra similairement se faire par des centrales electriques specialisees, suivant le meme schema d’alimentation sans fil. Pour utiliser et recuperer cette energie RF, cette these presente deux techniques principales : la recuperation/reception de puissance sans fil en champ lointain (wireless power transfer: WPT) et la retrodiffusion d'harmoniques. Le chapitre 2 aborde les differents mecanismes de conversion de frequence entre le WPT en champ lointain et la retrodiffusion d'harmoniques. La recuperation de WPT en champ lointain consiste a convertir l'energie RF en puissance continue. En revanche, la retrodiffusion d'harmoniques a pour but de convertir l'energie RF dans une autre frequence, dans la plupart des cas, la composante harmonique de rang 2. A titre d'etape preliminaire de recherche et d'etude de faisabilite, une cartographie de la densite de l'energie RF ambiante dans les zones centrales de l'ile de Montreal est resumee au chapitre 3. Contrairement aux mesures traditionnelles precedentes effectuees a des endroits fixes, cette mesure dynamique a ete realisee le long des rues, des routes, des avenues et des autoroutes pour couvrir une large zone.----------ABSTRACT Our life is becoming more convenient, efficient, and intelligent with the aid of fast-evolving Internet of Things (IoT) technology. One essential foundation of IoT technology is the development of numerous interrelated IoT sensors that are distributed extensively in our environment. However, conventional batteries/cords-based powering solutions are certainly not an acceptable long-term solution, considering the incurred cost, feasibility, most of all, environmental impact. Meanwhile, climate change due to excessive consumption of fossil fuels is worsening day by day. Therefore, a transformative powering solution for such large-scale and geographically scattered IoT sensors is of extreme importance in support of such extensive IoT sensors implementation while simultaneously mitigating its environmental burden. Serving as a critical information carrier, ambient radiofrequency (RF) energy is pervasive in urban and suburban areas to realize wireless communication and sensing. However, part of ambient RF energy is dissipated due to path loss if not fully consumed by end-users. Hence, recycling the wasted ambient RF energy to power IoT sensors is a promising solution. The concept of harnessing wireless energy for powering IoT sensors requiring a higher power supply is also feasible through the dedicated wireless power delivery from specialized power stations, which can be an effective supplement. To realize the RF power scavenging, this thesis research introduces two mainstream techniques: far-field wireless power transfer (WPT) and harmonic backscattering. Chapter 2 discusses the different frequency conversion mechanisms applied for far-field or ambient WPT harvesting and harmonic backscattering. Far-field WPT harvesting converts RF energy into dc power (zeroth harmonic). In contrast, harmonic backscattering upconverts RF energy into its harmonics, in most cases, the second harmonic component. As a preliminary research step and a feasibility study, a survey of ambient RF energy density in the core areas on Montreal Island is summarized in Chapter 3. Different from the previously published traditional measurements at fixed locations, this dynamic measurement is carried out along streets, roads, avenues, and highways to cover a large area. Also, a stationary measurement in Downtown Montreal is to reveal whether human activities are able to bring visible change to ambient RF energy levels. This work demonstrates how much ambient RF energy is available in free space and acts as a significant reference for researchers and engineers designing ambient RF energy harvesting circuits/systems for practical applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".