Energy efficient optimization of wireless embedded sensor networks
Bibliographic record
Abstract
The energy optimization techniques developed for conventional ad hoc networks do not appropriately address the unique features of the wireless embedded sensor networks (WESNs). In the WESN environment, only reducing the overall energy consumption is not considered enough to maximize the life span of the entire network, but maintaining full network connectivity for a sufficiently long period of time is also an important design goal due to the energy constraints of each node. The wireless radio is a major energy user and is often the focus of energy conservation mechanisms, since the nodes communicate in a shared medium (air interface). The medium access control (MAC) layer of the communication protocol stack arbitrates access to the communications link by manipulating the sleep, listen, transmit, and receive states of the radio transceivers. The bursty traffic networks experience long periods of inactivity interrupted by unplanned and often short lived periods of high traffic loads. Currently available MAC protocols cannot meet application fidelity requirements of the bursty traffic networks since they are designed either for networks with periodic traffic or are not sufficiently traffic-adaptive, thereby introducing large multi-hop latency delays to realize network connectivity, overprovision during light traffic conditions, and slow ramp up at the initiation of a high traffic episode. This paper presents enhancements made to the energy efficient MAC protocol which is especially designed for the bursty traffic networks and in the process targets some available communication techniques used in the WESNs for discussion and comparison.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".