Bibliographic record
Abstract
As nodes appear and disappear in a Wireless Sensor Network (WSN), communication protocols acting in the data link, network and transport layers adapt dynamically to the new network structure. We present an extension of the dynamic nature of WSNs to the web via an adaptive communication protocol called the adaptive Sensor Web Language (SWL). Adaptive SWL provides a web application with a reliable mechanism for automatically tracking and displaying changes in sensor network architecture. New nodes automatically appear in web-based applications. A color fading mechanism is also provided to differentiate sensor nodes which have not communicated within the expected time frame. Two new message types, Request Announce and Announce are added to SWL to support this adaptation. All software layers, including sensor nodes, gateway, base station (including the database) and the web applications were updated. Two web applications were implemented to clearly demonstrate web application adaptation to dynamic WSNs. The Open Geospatial Consortium's (OGC) standard Sensor Observation Service (SOS) was integrated with Google maps to show the spatial context of changing WSN structure. A test network was established using 6 sensor nodes and 10 sensors (6 battery voltage sensors, 2 air temperature sensors and 2 solar radiation sensors), and 1 gateway. Each node was added one by one over 4 hours in the network, then removed one by one from the network over 4 hours. Testing indicates appearance of a node in the web application within about 13 seconds of being added to the WSN with a system latency of 47.5 seconds averaged over 40 tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".