Agent Based Approach to Minimize Energy Consumption for Border Nodes in Wireless Sensor Network
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Bibliographic record
Abstract
This paper presents an agent-based system to minimize the energy consumption for border nodes in sensor-MAC (S-MAC), a cluster based contention protocol. The S-MAC protocol is based on unique feature; it conserves battery power at nodes by powering off nodes that are not actively transmitting or receiving packets. In doing so, nodes also turn off their radios. Inspired by the energy conservation mechanism of the S-MAC, we unmitigated our efforts to augment the node life time in sensor network. Border nodes act as shared nodes between virtual clusters. Virtual clusters are formed on the basis of sleep/listen schedule of nodes. Towards this end, we propose a multi-agent system that allows nodes to join cluster where they experience minimum energy drain. This system includes two types of agents: stationary and mobile agents. A prototype implementation and simulation results compared with S-MAC are presented.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it