Sensor allocation to multiple applications in shared wireless sensor networks
Bibliographic record
Abstract
A sensor grid is an integration of two technologies: wireless sensor networks and the grid. The sensors deployed in a WSN monitor a phenomenon of interest. The information gained from the WSN is processed in the grid and is used by the users of applications. Multipurpose WSNs have become very popular where the deployed WSNs support more than one application. The research in this extended abstract focuses on WSNs supporting multiple applications. In this work, we focus on allocation, which is a process of determining the sensor nodes that will be selected for executing the requests corresponding to an application. Scheduling, which determines the order in which the application requests submitted to the WSN are executed is performed to improve the mean response time to the users of the applications. Our previous works propose various scheduling algorithms for WSNs hosting multiple applications. In this research, various static and dynamic allocation algorithms are proposed with an attempt to balance the energy consumption amongst the sensor nodes and hence improve the network lifetime of the WSN. Network lifetime is the time when the energy of any sensor node in the WSN falls below a predefined threshold. The proposed algorithms use varying degree of information about the energy consumption at the major energy consuming components of the sensor nodes: the CPU component and the radio component. Simulation experiments are performed to evaluate the performance of the proposed algorithms. This extended abstract presents the preliminary results obtained from the experimentation done so far. The simulation experiments demonstrate that by performing dynamic allocation and by using information about the total energy consumption at the sensor nodes, the lifetime of the WSN can be significantly improved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".