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Record W2090470655 · doi:10.1155/2012/851426

IP-Enabled Wireless Sensor Network

2012· article· en· W2090470655 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

VenueInternational Journal of Distributed Sensor Networks · 2012
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceComputer networkWireless sensor networkWireless networkWirelessTelecommunications

Abstract

fetched live from OpenAlex

In wireless sensor networks (WSNs), the use of the IP technology has always been considered inadequate due to its high overhead in processing and memory usage. However, the use of IP technology in WSNs can provide basis for transparent communication among sensor nodes and seamless interconnection between WSNs and the Internet-based infrastructure network, thus eliminating the need for translation gateways or similar devices that are used in current WSNs for this purpose. Besides, many devices in a WSN make network autoconfiguration and statelessness highly desirable for the network operator. For this, IPv6 has ready solutions. Moreover, the large number of devices poses the need for a large address space, which is well met by IPv6. Application of IP technology to IEEE 802.15.4 will be a very important research direction. To accelerate the adoption of these new emerging techniques, a number of important issues must be addressed such as the routing and security of IP-enabled wireless sensor network and applications. In this special issue, we have invited a few papers that address such issues.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.000
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 head0.011
GPT teacher head0.240
Teacher spread0.230 · 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