Sensing as a Service Middleware Architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Internet of Things (IoT) is a concept that envisions the world as a smart space in which physical objects embedded with sensors, actuators, and network connectivity can communicate and react to their surroundings. However, IoT devices and consumers of data from these IoT devices can be owned by different entities which makes IoT data sharing challenging. Sensing as a Service is a concept that is influenced by the cloud computing term Every Thing as a Service. The proposed Sensing as a Service middleware enables consumers to access data generated by IoT devices owned by other entities. Consumers are charged for the amount of sensor data used. This paper addresses the architectural design of a cloud-based Sensing as Service middleware where IoT applications (consumers) can collect, and analyze sensor data through the middleware API. We propose multitenancy algorithms to make effective use of computing resources. In addition, we propose a SQL-Like language that can be used by IoT applications for sensing service discovery, and sensor stream analytics. The evaluation of the middleware implementation shows the effectiveness of the algorithms.
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.
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 | 0.001 | 0.001 |
| 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 it