Energy Aware Basestation Placement in Solar Powered Sensor Networks
Bibliographic record
Abstract
Sensor nodes are often used in outdoor locations where they can be operated using solar power. When such a network is deployed, there are usually restrictions in the way that the nodes can be positioned, and this results in a node-dependent attenuation of the usable solar energy. This effect must be taken into account when placing the basestations used to communicate with the sensor nodes. In this paper we consider the minimum-cost placement of data collecting basestation nodes so that outage-free operation of the sensor nodes is obtained. This is done by minimizing the number of basestations required when taking into account the energy costs of sensor node traffic relaying. An optimization is first formulated which gives a lower bound on the number of basestations that are required. Because of the complexity of the problem, an algorithm is proposed which can be used to do placements for practical problem sizes. The algorithm uses the result from an iterated local search as a starting point, and then uses an energy aware local optimization to obtain feasible basestation placements. Results are presented which show that the algorithm performs well for a variety of network scenarios.
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.
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 | 0.002 |
| 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".