HASM: A hybrid architecture for sensor management in a distributed surveillance context
Bibliographic record
Abstract
The increased popularity of the proactive security paradigm aggravated the need for pervasive surveillance systems. These systems are built using smart sensor networks which cover large areas of civilian concentration. Such networks need intelligent management systems to control the large number of sensor nodes and the large amount of data. Sensor Management Frameworks (SMF) aim to coordinate the sensor nodes in a manner that improves the process of system control and situation awareness. Large number of non-functional merits, i.e., autonomy, scalability, inter-operability, and others, can characterize SMFs. This paper provides a scalable and adaptable control architecture that is applicable in a variety of sensor management applications with a focus on tactical surveillance. The proposed Hybrid Architecture for Sensor Management (HASM) mixes the advantages of the holonic and federated paradigms. Experimental results illustrate the performance of the proposed architecture and show that the proposed paradigm is highly scalable compared to the centralized one.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".