Rollout Algorithm for Target Search in a Wireless Sensor Network
Bibliographic record
Abstract
A mobile, autonomous searcher is tasked with finding the source node of a broadcast message in a randomly deployed network of location-agnostic wireless sensor nodes. Messages are assumed to propagate by flooding, with random node-to-node delays. In networks of this type, the hop count of the broadcast message, given the distance from the source node, can be approximated by a simple parametric distribution. The mobile searcher can interrogate a nearby sensor node to obtain, with a given success probability, the hop count of the broadcast message. We model the search as an infinite-horizon, undiscounted cost, online POMDP and solve it approximately through policy rollout. The cost-to-go at the rollout horizon is approximated by a heuristic based on an optimal search plan in which path constraints and assumptions about future information gain are relaxed. This cost can be computed efficiently, which is essential for the application of Monte Carlo methods, such as rollout, to stochastic planning problems. Finally, we demonstrate that our rollout approach outperforms a popular method of target search based on a myopic, mutual information utility.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".