Utilizing elevator for wireless sensor data collection in high-rise structure monitoring
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it