SensorsConnect Framework: World-Wide Web for Internet of Things
Bibliographic record
Abstract
The widespread adoption of the Internet of Things (IoT) has led to a surge in smart sensing devices connected to the Internet. While IoT enables machines, embedded systems, and appliances to access the Internet, they do not interact with it as humans do through the World Wide Web (WWW). Unlike humans, IoT devices lack a unified framework like the WWW for collaboration and data sharing. This is primarily due to 1) separate infrastructure often required for IoT security and privacy and 2) challenges of limited connectivity, device heterogeneity, and evolving technology. This paper presents SensorsConnect, a system that connects IoT devices in a WWW-like framework, enabling real-time sensing data searches across a broad IoT context. It defines the architecture, core processes, and major challenges of SensorsConnect, along with strategies to address these challenges. A motivating scenario illustrates its potential impact in real-life situations, such as finding a drive-thru coffee shop or crossing a country border. Using real-time road status and service occupancy data, SensorsConnect enhances Google Maps service recommendations, not only minimizing customer wait times but also distributing workload more evenly across service points. Performance evaluation shows that SensorsConnect reduces average service times by 46% at drive-thru locations and 31% at border crossings compared to Google Maps. This promising application could improve public access to live data, supporting real-time decisions and enhancing quality of life.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".