Empirical Analysis of Path Loss and Distance Estimation in Wireless Networks
Bibliographic record
Abstract
The position of the sensor node stands as a pivotal challenge in WSN applications.The efficacy of wireless communication systems is markedly limited by the attributes of the wireless channel, thereby amplifying the necessity for predicting channel path loss.Consequently, we undertook an empirical assessment of signal strength and path loss in relation to over which the link between Tx and Rx remains uninterrupted while maintaining acceptable path loss.Through the execution of an experiment in outdoor settings to evaluate the distance between nodes.The assessment utilized the LNSM algorithm, which relied on the RSSI values collected by the (transmitting) node's receiving unit.Additionally, factors of the propagation channel, including standard deviation and path loss exponent values, were scrutinized.RSSI values for outdoor environment were recorded and examined for distances spanning from 1 to 90 m.The analysis disclosed an MAE error of 2.03 m and 1.80 m for ranges of 0-65 m and 0-100 m, respectively, alongside an RMSE error of 10 m and 8.71 m for the same distances associated with Zigbee.In contrast, Wi-Fi technology exhibited lower error rates across all distance measurements compared to Zigbee, underscoring its efficacy and dependability even over extended ranges.The MAE error stood at 0.33 m and 0.94 m for distances of (0-65 m) and (0-100 m), respectively, while the RMSE error was measured at 1.34 m and 5.31 m for the same distances.These findings indicate that LNSM is optimal for short distances when using Zigbee, whereas it can be used for longer distances when using Wi-Fi.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".