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Record W3146746004 · doi:10.1109/iwqos.2011.5931350

Utilizing elevator for wireless sensor data collection in high-rise structure monitoring

2011· article· en· W3146746004 on OpenAlexaff
Feng Wang, Jiangchuan Liu, Dan Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElevator Systems and Control
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsElevatorBase stationExploitWireless sensor networkComputer scienceNetwork packetStairsData collectionComputer networkScheduling (production processes)Real-time computingWirelessTelecommunicationsEngineeringComputer security

Abstract

fetched live from OpenAlex

Recently wireless sensor networks have been widely suggested for Structural Health Monitoring. In such applications, diverse sensor nodes are deployed in a building structure, collecting ambient data such as temperature and strain from various locations and reporting them to a central base station for processing and diagnosing. For today's high-rise structures (e.g., the Guangzhou New TV Tower, a project that we have participated in, peaks at 600m above ground), the extensive vertical dimension creates enormous challenges toward sensor data collection, beyond those addressed in state-of-the-art motelike systems. For example, with a straightforward base station placement, a huge amount of data will accumulate as being relayed to the base station. As such, the sensor nodes close to the base station would quickly run out of energy for relaying the traffic. The accumulated traffic would also saturate the wireless medium, introducing significant interferences and collisions. The extensive height of these building structures, however, make elevators an indispensable component. This motivates us to develop EleSense, a novel high-rise structure monitoring framework that explores using elevators. In EleSense, an elevator is attached with the base station and collects data when it moves across different floors to serve passengers, which can effectively reduce the traffic accumulation and the collection delay. To maximally exploit the benefit, we take a unique angle with the cross-layer design. We present an abstraction of the high-rise structure monitoring problem that exploits elevators, and model it as a joint optimization across link scheduling, packet routing and end-to-end delivery. We propose a centralized algorithm to solve it optimally. We further propose a distributed implementation to accommodate the hardware capability of a sensor node and address other practical issues. We evaluate EleSense through ns-2 simulations and with real configurations from the Guangzhou New TV Tower. The results show that EleSense has a throughput gain of 30.4% to 200.6% over the case without elevators. We also observe a gain of 40.5% to 127.5% over a straightforward 802.11 MAC scheme without the cross-layer optimization. Moreover, EleSense can significantly reduce the communication costs while maintaining excellent fairness with reliable data delivering.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.235
Teacher spread0.196 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2011
Admission routes1
Has abstractyes

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