Indoor positioning of mobile devices with agile iBeacon deployment
Bibliographic record
Abstract
Position of mobile devices and their users provides a great amount of added value and opportunities. The penetration of tracking devices with sensory such as GPS devices, accelerators and specifically smart phones has impacted human lives extensively. Nowadays, many applications on smart phones and mobile devices exploit different techniques and inputs for positioning. Wireless positioning is generally divided into two categories: outdoor positioning and indoor positioning, depending on not only where they are used but also how they work. Powerful as it is, indoor positing is still a challenging problem because satellite-based approaches do not work properly inside buildings. Therefore, for indoor positioning, we need to use other technologies creatively. iBeacon, the focus of this paper, is a new technology which provides a higher level of location awareness in indoor positioning. iBeacon is a built-in, cross-platform technology for Android and iOS devices, which utilizes Bluetooth Low Energy (BLE) for long-last services. This technology has significant advantages compared to other types of indoor positioning technologies, such as less expensive hardware, less energy consumption, needless to internet connection, and being capable of receiving notifications in background. This technology will provide huge benefits for future location awareness applications. It will change the way retailers, event organizers, and educational institutions communicate with people indoors. In this paper, we aim to provide a more accurate, cost efficient approach to indoor positioning of mobile devices using iBeacon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".