Limited Resource Management of Sensor Networks
Bibliographic record
Abstract
Sensor networks consist of a number of sensors that measure or estimate a single quantity or a number of quantities. For example, sensors might measure temperature, pressure, concentration of chemical substances, relative humidity, sound, distance to an object, or the position of the sensor itself. Specific applications of networks are varying, including pollution monitoring, undersea exploration, mine detection, navigation and disaster warnings. Air quality networks are used to measure and monitor a wide variety of physical conditions and pollutants. Different pollutants have diverse effects on health and the related risks to population depend on both the pollutants and the exposure. High density monitoring networks have traditionally been limited by the cost of the instruments used. It has been identified that, in the long run, the network maintenance considerations may turn out to be at least as significant as the establishment considerations, especially with networks that are based on low-cost instruments. The limited resource and maintenance considerations seem, however, to have attracted relatively little attention in the literature when it comes to how recalibration-type tasks should be deployed. In this thesis, the aim is to construct an approach that attempts to detect sensor malfunctions, and to allow network operators to deploy the limited maintenance resources in an optimal fashion. As maintenance resources, we consider the recalibration-type costs. The approach will then issue alarms for each node separately and therefore, generate an overall maintenance schedule that, ideally, corresponds to the specified resources. Since the environment of the sensors as well as the characteristics of the local natural variations might have different types of trends, we also construct a scheme that allows the sensors to adapt to changes in the environment but avoids adaptation to sensor malfunctions. We consider the ozone and nitrogen dioxide data from Houston and Metro Vancouver air quality networks, as well as the ozone data from the preliminary the Aeroqual Ltd. measurement campaign in May-September, 2012. The results suggest that the developed approach is basically a feasible one and, given further development, a limited resource maintenance can be scheduled based on the approach.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".