Implementation of a Random Wireless Sensor Network in an Irregular Shape Large Building
Bibliographic record
Abstract
Proactive management of large buildings requires continuous and real-time performance monitoring. Wireless Sensor Network (WSN) can be an efficient and flexible solution to provide this sort of large building monitoring. The aim of this paper is to implement a theoretical platform for a random WSN for monitoring applications in an irregular shape large building. The main WSN design objective is to achieve desirable coverage and accuracy while maintaining quality of service, cost, reliability, and scalability at acceptable levels. A major issue of concern is the connectivity of WSN, this is particularly important in sensor networks, where achieving a common application objective may require communication among all the nodes. Cooja is a flexible Java-based network simulator designed to model and simulate WSNs. Cooja is used in this paper to implement a random WSN for monitoring applications for the Sacred Mosque situated in Mecca. The area used for simulation is the second floor of the building. The simulation model consists of one stationary base station and a number of wireless motes that varies from 10 to 50 motes. In each group of simulation the transmission range varied from 20m to 100m with a step of 20m making a total of 25 simulation runs. Energy levels, network lifetime and delay are metrics taken into consideration in these experiments. The output of the experiments can be used to obtain an optimal solution for given operational conditions.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".