Integrated Placement and Routing of Relay Nodes for Fault-Tolerant Hierarchical Sensor Networks
Bibliographic record
Abstract
Two-tiered sensor networks have gained popularity in recent years, due to their ability to facilitate load-balanced data gathering, fault-tolerance as well as increased network connectivity and coverage. Using higher-powered relay nodes as cluster heads can lead to further improvements in network performance. It is important to determine an appropriate placement scheme for such relay nodes, in order to achieve specified coverage and connectivity requirements with as few relay nodes as possible. A significant amount of work has been done in this area in recent years. However, existing placement strategies typically do not consider energy dissipation due to routing and are not capable of optimizing the routing scheme and placement concurrently. In this paper, we propose an integrated integer linear program (ILP) formulation that determines the minimum number of relay nodes, along with their locations and a suitable communication strategy such that i) all sensor nodes are able to connect to at least k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> relay nodes, ii) the upper tier relay node network is at least K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> -connected and iii) the network has a guaranteed lifetime. We also present an intersection based scheme for creating the initial set of potential relay node positions, which are used by our ILP, and evaluate its performance under different conditions. Experimental results on networks with hundreds of sensor nodes show that our approach leads to significant improvements over existing energy-unaware placement schemes.
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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".