RSSI-Based Distributed Self-Localization for Wireless Sensor Networks Used in Precision Agriculture
Bibliographic record
Abstract
In this paper, we propose a received signal strength indication-based distributed Bayesian localization algorithm based on message passing to solve the approximate inference problem. The algorithm is designed for precision agriculture applications, such as pest management and pH sensing in large farms, where greater power efficiency besides communication and computational scalability is needed but location accuracy requirements are less demanding. Communication overhead, which is a key limitation of popular non-Bayesian and Bayesian distributed techniques, is avoided by a message passing schedule, in which outgoing message by each node does not depend on the destination node, and therefore is a fixed size. Fast convergence is achieved by: 1) eliminating the setup phase linked with spanning tree construction, which is frequent in belief propagation schemes and 2) the parallel nature of the updates, since no message needs to be exchanged among nodes during each update, which is called the coupled variables phenomenon in non-Bayesian techniques and accounts for a significant amount of communication overhead. These features make the proposed algorithm highly compatible with realistic wireless sensor network (WSN) deployments, e.g., ZigBee, that are based upon the ad hoc on-demand distance vector, where route request and route reply packets are flooded in the network during route discovery phase.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".