Collaborative Agents for Data Dissemination in Wireless Sensor Networks
Bibliographic record
Abstract
This paper presents collaborative agents system architecture approach for data dissemination in a wireless sensor network (WSN). This system architecture consists of four layers of agents with different types of functionalities, including the interface layer, regional layer, query layer and data collection layer. At the interface layer, the interface agents interact with the users to fulfill their interests. At the regional layer, the regional agents and the cluster agents communicate with each other using TCP/IP protocol and facilitate collaboration between the agents of the other layers. The regional agent generates the optimized query plan to the cluster agent. At the query layer, the query agents perform data dissemination and efficient in-network processing with the other agents in the same layer; it also captures the required data through the data collection layer that has direct access to sensor nodes. This paper provides the agents’ architecture, design and implementations that enable them to communicate and work together to disseminate and gather data in WSNs.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".