Energy-Aware Co-Operative (ECO) Relay-Based Packet Transmission in Wireless Networks
Bibliographic record
Abstract
In infrastructure wireless networks, nodes at the edge of the coverage area need to spend more energy to transmit their packets than those close to the Access Point (AP). Less energy is required if intermediate nodes can be used to forward data. To enable this, intermediate nodes need an incentive and there must be a mechanism for selecting these nodes. In this paper we propose Energy-aware Co-Operative (ECO) relaying for selecting relays to forward packets. The technique is based on the idea of Relative Energy Usage (REU), which reflects the proportion of energy that a node saves by forwarding its packets through relays. A node which saves more energy by using relays is more likely to be chosen as a relay. Conversely, nodes are only permitted to use relays proportionate to the amount of energy they themselves have spent as relays. We compare our scheme with direct transmission, minimum energy path (MnEP), and maximum residual energy path (MxRE). We show that ECO can transmit 50% more data than direct transmission, while using less energy on average. Although MnEP and MxRE can also transmit more data than direct transmission, they do so at severe energy cost to a small number of nodes, doubling the average energy usage, making them ill-suited to commercial networks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".