Design and Implementation of a Sensor Network Based Location Determination Service for use in Home Networks
Bibliographic record
Abstract
Location determination is a fundamental component enabling context aware home applications. Ultrasound, floor sensors and computer vision, among others, have been proposed for location determination, each with its own benefits and limitations. In this paper we discuss the design, implementation and evaluation of a location determination system based on sensor networks. Our implementation includes three components: the location determination system which adapts and extends Motetrack for in-home use, a location storage system, and a middleware interface to allow home applications to access both current and historical location information. Our system uses Crossbow Mica2 and Mica2Dot sensors to provide 28th, 50th, 85th and 97th percentile location errors of under 1,1.5,2 and 3 meters, respectively. This will support most home services which typically require only "room level" accuracy. Using sensor nodes in our location determination scheme provides a low cost solution and eliminates dependence on the availability of existing, in-home equipment to perform the location determination computations
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".