Grid-based deployment for wireless sensor networks in outdoor environment monitoring applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) overcome the difficulties of other monitoring systems, as they require no human attendance on site, provide real-time interaction with events, and maintain cost and power efficient operations. However, further efficiencies are required especially in the case of Outdoor Environment Monitoring (OEM) applications due to their harsh operational conditions, huge targeted areas, limited energy budget, and required Three-Dimensional (3D) setups. A fundamental issue in defeating these practical challenges is the deployment planning of the WSNs. The deployment plan is a key factor of many intrinsic properties of OEM networks, summarized in connectivity, lifetime, fault-tolerance, and cost-effectiveness. In this thesis, we investigate the problem of WSNs deployments that address these properties in order to overcome the unique challenges and circumstances in OEM applications. A natural solution to this problem is to have multiple relay nodes that reserve more energy for sensing, and provide vast coverage area. Furthermore, assuming a subset of these relay nodes are mobile can contribute in repairing the network connectivity problems and recovering faulty nodes, in addition to granting balanced load distributions, and hence prolonging the network lifetime. We investigate this promising research direction by proposing a 3D grid-based deployment planning for heterogeneous WSNs in which Sensor Nodes (SNs) and Relay Nodes (RNs) are efficiently deployed on grid vertices. Towards this efficiency, we analyze and characterize the grid connectivity property in the 3D space. Afterward, we design optimization schemes for the placement of SNs and RNs on the 3D grid models. Based on theoretical analysis and extensive simulations, the proposed schemes show a significant enhancement in terms of network connectivity and lifetime in OEM applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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 it