Low-power wireless sensor network with compressed sensing theory
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) have application in a variety of fields including inhospital locations, military purposes, transportation automation, home and industrial automation. WSNs also are used in monitoring synchronous or asynchronous events that require periodic data collection. WSNs consist of a large number small device or Wireless Nodes (WNs) and are responsible for sensing, collecting, processing and monitoring information of real world environments. WSNs consist of a Data Acquisition Network (DAN) and a Data distribution Network (DDN) which monitored and controlled by a management center. The primary limiting factor for the lifetime of a WSN is the power supply. Regarding the applications of WSNs it is often impossible to obtain physical access to replace or charge battery. Therefore we can design low power WSNs. In WSNs, the events are sparse signal compared with the number of sources. That is why; the compressed sensing theory holds promising to reduce power consumption. Compressed Sensing shows that spars signals such as signals of WSNs can be exactly reconstructed from a small number of random linear measurements. Compressed Sensing theory can reduce number of bits information through whole of the network and consequently decrease amount of current that drawn from power supply. With this in mind, we introduce a new mechanism to design low-power WSN with compressed sensing theory. This paper gives a background of compressed sensing theory, and then describes important concepts in wireless sensor networks, and finally our simulation by applying compressed sensing in WSNs theory is described.
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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 | 0.000 | 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 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".