P2P Multi-agent Data Transfer and Aggregation in Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) enable pervasive, ubiquitous, and seamless communication with the physical world. This paper presents P2P multi-agent data transfer and aggregation system architecture in WSNs. The architecture includes four types of agents: interface, query, routing, and data acquisition agents. The interface agent interacts with the users to fulfil their interests. The routing agent is responsible for energy efficient data transfer. The query agent facilitates the collaboration between the interface and routing agents, and is responsible for creating optimized plans to achieve their desired goals. Both interface and query agents are placed at the resource-enriched base station because they require computation intensive operations. The data acquisition agent is responsible to acquire, filter, and format the sensor data. Sensor nodes have limited energy and computing resources. Thus, we create proxy agents for query and routing agents to act as peer agents with similar capabilities. The proxy query agents receive query execution plans from query agent, and proxy routing agents receive routing plans from routing agent. This paper provides the agents' architecture and design that enable them to coordinate and communicate with each other to transfer and aggregate data in WSNs
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".