Placement of multiple mobile base stations in wireless sensor networks
Why this work is in the frame
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Bibliographic record
Abstract
Due to energy constraints in individual sensor nodes, extending the lifetime is an essential objective in wireless sensor networks (WSNs). Several proposals have aimed at that objective by designing energy efficient protocols at the physical, medium access, and network layers. While the proposed protocols achieve significant energy savings for individual sensor nodes, they fail to solve topology-related problems; an example of such problems is that sensor nodes around the base station become bottlenecks and deplete their battery energy much faster than other nodes. A natural solution to such a problem is to have multiple mobile base stations so that the load is distributed evenly among all nodes. Only few proposals have followed that direction. In this paper we propose a mobile base station placement scheme for extending the lifetime of the network. In our scheme the life of the network is divided into rounds and base stations are moved to new locations at the beginning of each round. While previous work has focused on placing the base stations at predefined spots (e.g., the work in [1]) or at the boundary of the network (e.g., the work in [2]), we define and solve a more general problem in which a base station can be placed anywhere in the sensing field. We formulate the problem as an Integer Linear Program (ILP) and use an ILP solver (with a constant time limit) to find a near-optimal placement of the base stations and to find routing patterns to deliver collected data to base stations. Our experiments show that our scheme makes significant extension to the lifetime of the network.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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