Localization of wireless sensor network using Bees Optimization Algorithm
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) are an emerging technology that draws a significant amount of research attention. Localization of the nodes in these networks plays a key enabling role in WSN applications. In this paper, the use of Bees Optimization Algorithm (BOA) for localizing the nodes of the wireless sensor networks is investigated. BOA is population-based search algorithm that performs a neighborhood search combined with random search. It is inspired by the natural foraging behavior of honey bees. The summation of the squared range error between the node and the anchors is used as the objective function to be minimized in this work. Different simulation tests with different topologies are conducted based on normal random distribution for time of arrival (TOA) measurements and log-normal distribution for received signal strength (RSS) measurements. The simulation test results showed effectiveness of the proposed approach in case of TOA measurements when compared with the lower variance achievable by any unbiased location estimator.
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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".