JLPR: Joint range-based localization using trilateration and packet routing in Wireless Sensor Networks with mobile sinks
Bibliographic record
Abstract
Location-awareness plays an important role in Wireless Sensor Networks (WSNs) by aiding in tasks such as packet routing, event mapping, and energy savings. The use of Global Positioning System (GPS) on sensor nodes is not always viable due to a number of issues, e.g., power constraints. Location estimation solves the problem of computing sensor node positions by using information from devices that can house a GPS module, e.g., a mobile sink. However, GPS error may affect the accuracy of position estimations. In this paper, we propose a range-based scheme that uses trilateration and also handles GPS error. The proposed approach takes advantage of sink beacons used in packet routing. These beacons are used as position packets in order to estimate the position of nodes while reducing network overhead. However, a mobile sink poses issues when used as a source of position packets. Therefore, we propose Position Distance, Circle Limit and a Hybrid version of the algorithms in order to decide on the best position packets from the sink to be used in trilateration. An extensive set of performance evaluation experiments is conducted and results show that the proposed algorithms can improve position estimation accuracy, while sustaining acceptable packet delivery ratio and reducing network overhead.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".