Power-Aware 3D Position-based Routing Algorithms for Ad Hoc Networks
Bibliographic record
Abstract
A crucial problem in ad hoc networks is finding an efficient and correct route between a source and a destination; however for many networks, a more important problem is providing an energy efficient route because of, for example, the limited battery life of the wireless nodes. Most previous routing protocols make the routing decision without taking into account the energy budget of the nodes. In addition, when using a fixed transmission power, nodes may waste power by transmitting with more power than is needed for correct reception. In position- based routing algorithms, the nodes use the geographical position of the nodes to make the routing decisions. In this paper we present several localized power-aware 3D position-based routing algorithms that increase the life-time of the network by maximizing the life time of the nodes. These new algorithms use the idea of replacing the constant transmission power of the node with an adjusted transmission power during two stages - first a lower power while discovering the neighboring nodes, and, if needed, a second higher transmission power during the routing process. We evaluate our algorithms and compare their power savings with the current power-aware routing algorithms. The simulation results show a significant improvement in the energy saving (up to 50%).
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".