Data Dissemination in Wireless Sensor Networks Using Software Agents
Bibliographic record
Abstract
This paper presents an agent based system to increase the life time of node in Wireless Sensor Networks. In wireless sensor network nodes deployment of nodes is random and on large scale. This kind of deployment gives birth to massive sensory data which is redundant in nature. Routing of such kind of unnecessary data not only saturates network resources, but also consumes immense nodes energy. We unadulterated our efforts to enhance the node life time in sensor network by introducing mobile agents. Mobile agents are used to reduce the communication cost, especially over low bandwidth links, by moving the processing function to the data rather than bringing the data to a central processor (sink). Toward this end, we propose our agent based directed diffusion approach. Furthermore, to have better understanding in evaluating the performance of both approaches, we present detailed analytical model of data dissemination for both. The results of our simulation show that agent based directed diffusion provides better performance than directed diffusion in terms of energy consumption and bandwidth saturation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".