Energy Aware Trajectory Computation of Mobile Data Collectors in Hierarchical Sensor Networks
Bibliographic record
Abstract
In hierarchical sensor networks, higher powered relay nodes can be deployed to act as cluster heads. The lifetime of such a network is primarily determined by the lifetime of the relay nodes. Recently, it has been shown that the use of a Mobile Data Collector (MDC), which moves through the sensor network to collect data, can improve the performance of the network in a number of ways. We propose a model for hierarchical networks using a MDC, where the MDC travels along a pre-determined trajectory at a known speed, and collects data from each relay node. In this model, the relay nodes need to buffer their data until they can be uploaded to the MDC. To reduce the buffer size, as well as the delay for the data communication, it is desirable to minimize the length of the trajectory. In our model the MDC "visits" a relay node, to collect data, at any point within the permitted transmission distance of the relay node. There is a trade-off in this approach, as a large permitted transmission distance means that the relay node will expend higher energy, and hence, have a reduced lifetime. In this paper, we present two formulations for computing the minimal trajectory of the MDC. Our approaches allow us to control the transmission distances of the relay nodes and hence, achieve the lifetime objectives. Minimizing the length of the trajectory reduces the buffer size requirements at each node as well as the delay between successive visits.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| 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".