Maximizing the Lifetime of Two-Tiered Sensor Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent technological advances in the field of micro-electro-mechanical systems (MEMS) have made the development of multi-functional sensor nodes technically and economically feasible. The lifetime of sensor networks is still limited as individual sensor nodes are usually powered by battery. In the past few years, the use of relay nodes in sensor networks has been proposed in literature for balanced data gathering, reduction of transmission range, connectivity and fault tolerance. In hierarchical sensor networks, higher-powered relay nodes can also be used as cluster heads. These relay nodes may form a network among themselves and route data towards the base station. In such a sensor network, the lifetime of the network is directly related to the lifetime of these relay nodes. In this paper, we have proposed an ILP solution for scheduling the data gathering of relay nodes such that the lifetime of the relay node network is maximized. We have compared our formulation with the direct transmit energy model and shown that it can lead to significant improvements. We have also proposed a re-scheduling approach which can further extend the maximized lifetime of the network
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.
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 | 0.001 | 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