STRONG CONNECTIVITY IN SENSOR NETWORKS WITH GIVEN NUMBER OF DIRECTIONAL ANTENNAE OF BOUNDED ANGLE
Bibliographic record
Abstract
Traditional approaches to connectivity in sensor networks are based on the omnidirectional antenna model which relies on the assumption that the sensors send and receive in all directions. Current technologies make possible the utilization of sensors with directional antenna capabilities whereby the sensors send and/or receive along a sector of a predefined angle (or beam-width). Although several researchers in the scientific literature have investigated the impact of directional antennae on network throughput, energy consumption, as well as security very little is known concerning the effect of directional antennae on its connectivity. In this paper, we introduce for the first time a new sensor model with each sensor being able to transmit in any one of k directions, for some fixed k, and explore the algorithmic limits and potential of such a directional antenna model. More specifically, given a set of n sensors in the plane, we consider the problem of establishing a strongly connected ad hoc network from these sensors using directional antennae. In particular, we prove that given such set of sensors, each equipped with k, 1 ≤ k ≤ 5, directional antennae with any angle of transmission, these antennae can be oriented in such a way that the resulting communication structure is a strongly connected digraph spanning all n sensors. Moreover, the transmission range of the antennae is at most [Formula: see text] times the optimal range (a range necessary to establish a connected network on the same set of sensors using omnidirectional antennae). The algorithm which constructs this orientation runs in O(n) time provided a minimum spanning tree on the set of sensors is given. We show that our solution can be used to give a tradeoff on the range and angle when each sensor has one antenna. Further, we also prove that for two antennae it is NP-hard to decide whether such an orientation exists if both the transmission angle and range are small for each antennae.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".