A Scalable Sensor Management Architecture Using BDI Model for Pervasive Surveillance
Why this work is in the frame
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Bibliographic record
Abstract
Recent world events have amplified the need for improved safety and security to contend with natural and man-made threats. The universality and unpredictability of such threats have stimulated intense interest in smart pervasive surveillance systems. They are built by adopting smart sensor networks that cover large areas and can perform self-contained assessments of situations in the environment. However, such systems rely on a massive number of sensors with diverse capabilities but limited resources, e.g., power, processing, and storage. Thus, successful management of tasks hinges on the systems architecture. Sensor management architectures (SMAs) coordinate the sensor nodes and their resources in a manner that improves system control and situation awareness. This paper introduces a scalable and flexible SMA for many sensor management (SM) applications, particularly, pervasive surveillance. This novel SMA is called the extended hybrid architecture for SM (E-HASM), an architecture that combines the advantages of the holonic, federated, and market-based paradigms. The E-HASM models each node as an intelligent sensor by using the beliefs, desires, and intentions model and defines the interaction and cooperation among the nodes. The simulation results illustrate the performance of the E-HASM over a variety of security threats, background targets, and network sizes. The results prove that the proposed architecture is significantly more scalable and flexible than centralized architectures.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it