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Record W2003300496 · doi:10.5555/514151.514167

Low power rendezvous in embedded wireless networks

2000· article· en· W2003300496 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMobile Ad Hoc Networking and Computing · 2000
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRendezvousComputer scienceComputer networkSleep modeNode (physics)WirelessServerPower (physics)Real-time computingPower consumptionTelecommunications

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.216
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it