An Architecture for Mobile Sensor Network Control Using IMS and Reconfigurable Hardware
Bibliographic record
Abstract
Mobile sensors networks are used in several domains to perform various tasks related to monitoring, recording, and affecting the conditions of an environment. Interacting with mobile sensor networks deployed in public networks requires an application capable of securely exchanging data in the network, generating mobile sensor network requests and processing mobile sensor network responses. Embedded communications devices are an ideal platform through which mobile sensor network applications could be implemented due to their portability and preexisting facilities to establish network connections. The IP Multimedia Subsystem (IMS) is a communications framework permitting the exchange of data from communications devices across divergent networks. No research has been performed combining embedded devices and mobile sensor networks using public network facilities like IMS. This paper proposes an architecture permitting end users to perform mobile sensor network operations using IMS and embedded communications devices. The architecture is based on a reconfigurable hardware implementation of SIP to exchange data through an IMS network to query mobile sensor data, publish data to valid subscribers and control nodes. SIP is used to manage the sessions in which data are accessed by arbitrary users registered with a sensor network service.
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 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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".