Reducing the Positional Error of Connectivity-Based Positioning Algorithms Through Cooperation Between Neighbors
Bibliographic record
Abstract
The information available to connectivity-based positioning algorithms is the radio range of sensor devices and the position estimates of neighbors and neighbors of neighbors. This information creates special graph theoretic structures which impose new constraints on the positions of sensor devices. The new constraints sometimes lead to a feasible set of positions with disconnected regions. These properties can be used to reduce the set of feasible positions for a node. In this paper, a new fully distributed positioning algorithm, called Orbit, which exploits these properties is presented for mobile sensor networks. The algorithm uses additional constraints and trims disconnected regions. These new constraints are generated through cooperation between neighbors. The performance of Orbit is examined for many communication and mobility models, including a probabilistic communication model generated from radio experiments. Computer simulation experiments demonstrate that Orbit outperforms a recently proposed positioning algorithm in terms of positional accuracy under different models with a wide range of parameter values. Orbit is implemented on resource limited sensor devices. This implementation demonstrates the feasibility of the algorithm for sensor devices. The algorithm is tested on deployments of the sensor devices in a field and the results are comparable to those from the simulation experiments.
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".