Energy efficient clustering in sensor networks with mobile agents
Bibliographic record
Abstract
Wireless sensor networks with mobile access points are effective tools for collecting data in a variety of environments. Mobile agents are powerful hardware units with sophisticated transceivers. Low-cost and low-power sensors in the reachback operation contend for the channel to transmit their own data packets to the mobile agent. This data communication should be designed to ensure energy efficiency and low latency. We propose a clustering scheme for wireless sensor networks with reachback mobile agents (C-SENMA). C-SENMA groups sensors into clusters such that nodes communicate only with the nearest clusterhead (CH) and the CH takes the task of data aggregation and communication with the mobile agent. CHs use a low-overhead medium access control (MAC) mechanism, similar to the conventional ALOHA, to contend for the channel. Using results from random geometry theory, we analyze the clustering performance under the realistic MAC algorithm. Our analysis enables us to obtain the optimal average cluster size which minimizes energy consumption. We justify our analysis results by extensive simulations according to various clustering parameters. Furthermore, we study the effect of underlying physical layer characteristics on the amount of energy reduction achievable by the proposed clustering architecture.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".