Physical Topology Discovery Scheme for Wireless Sensor Networks Using Random Walk Process
Bibliographic record
Abstract
Wireless sensor networks (WSNs) are widely considered as the most important information gathering platform in enabling Internet of Things (IoT). In order to evolve the traditional WSNs for low-power and low-loss IoT applications, time slotted channel hopping (TSCH) MAC protocol has been proposed to tackle the single channel and inefficient medium access drawbacks through improved network topology awareness. However, the problem of maintaining the physical topology of a WSN at the server end remains unresolved. In this paper, we propose a novel physical topology discovery scheme for WSNs by exploitation of random walk process and iterative multilateration localization algorithm. Explicitly, information specific to the sensor nodes, including IDs and neighbor tables, are collected in the random walk process. The physical topology is then reconstructed at the server end based on the collected information and the iterative multilateration localization algorithm. Simulation results indicate that the average location offset between the established topology and the ground- truth topology can be as low as 1.26m.
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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".