Agent-based peer-to-peer layered architecture for data transfer in wireless sensor networks
Bibliographic record
Abstract
Recently, there has been a growing interest in the potential use of Wireless Sensor Networks (WSNs) in many applications such as smart environments, disaster management, combat field reconnaissance, and security surveillance. Therefore, to realize their potential, there is a need of an architecture that facilities the deployment of a network that is optimized in terms of energy, query and network configuration. This paper focuses on developing agent-based peer-to-peer layered system architecture for data transfer in WSNs. The architecture has three layers: application, database and network. At each layer, agents interact as peers; however, agents at base- station are computation intensive and agents at sensor nodes require very limited energy and computing resources. The application layer is the highest layer where peers exchange data requests and results. The database layer is the middle layer where peers exchange query execution plans and the query results. The network layer is the lowest layer where peers exchange the routing information and sensor data. The proposed system is implemented in Java and mica2 motes.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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".