Testing of Event Detection Lateness in Wireless Sensor Networks
Bibliographic record
Abstract
A wireless sensor network (WSNs) technology determines physical properties of the environment through detection of unusual events. The sensor consists of small mini computers powered by batteries and use radio communication for the exchange of information it gathers. The sensors are able to collect the data and exchange the information with all the nodes that were affected and triggered during the event detection. Although the technology has been in use for a period of time it has been noted that there is lateness of the event detection by the technology. Therefore the essence of this paper is to describe the factors affecting the lateness detection of events by WSNs. From the experiment it is evident that varying distance of the sensor nodes to the base station and the transmission power affect the packet data transmission efficiency and thus lateness detection of events. Power level at 5 dBm (maximum level) ensures 90% of the event detected is communicated effectively to the base station whereas minimum power level at -10 dBm allows ineffective detection of events. At -10dBm the base station node could not detect a transmitted data packet at distances beyond 30 feet. Thus the application of any sensor node with the transmitting power at -10 dBm will only be effective at the distance less than 10ft. Therefore, distance of the sensor nodes and the transmission power of the WSNs affect the data packet delivery performance and hence lateness of event detection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 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".