Low power rendezvous in embedded wireless networks
Bibliographic record
Abstract
In the future, wireless networking will be embedded into a wide variety of common, everyday objects [1]. In many embedded networking situations, the communicating nodes will be very small and battery powered. For this reason, it is crucial that power consumption is as low as possible. A technique for reducing power consumption is to place nodes into a sleep mode whenever possible, and have them occasionally awaken to interact with other nodes. This type of action is referred to as a node rendezvous, and can be used in a variety of different ways.In this paper we consider power-efficient service rendezvous in embedded wireless networks with external triggering. We first define two basic rendezvous mechanisms, namely, server beaconing and client beaconing. We show that server beaconing is preferred when the client arrival rate is below a parameter dependent threshold. Above this level, the use of client beaconing results in lower power consumption. We also consider a hybrid technique whereby server nodes independently select the beaconing mode so that total power consumption is reduced over a wide range of system parameter values. The operation of the client nodes is transparent to this selection.We also introduce the use of adaptive server beaconing. In a static server beaconing system, the optimum beaconing rate is an increasing function of the client loading level. It is shown that by adapting the server beacon rate in an intelligent way, total power consumption can be greatly reduced over a large range of traffic loading conditions. A very simple method is introduced for performing this adaptation.Several other innovations are discussed which can be used to reduce power consumption in embedded networks. We investigate the use of an AC mains-powered rendezvous server for power reduction, and we discuss a distributed power reduction technique referred to as client beacon proxying. It is shown that by performing rendezvous in an intelligent manner, total power consumption may be greatly reduced in many situations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".