DV-maxHop: A Fast and Accurate Range-Free Localization Algorithm for Anisotropic Wireless Networks
Bibliographic record
Abstract
Localization awareness is a fundamental requirement in many Internet of Things (IoT) and other wireless sensor applications. The information transmitted by an individual entity or node is of limited use without the knowledge of its location. Research in this area is mostly geared towards multi-hop range-free localization algorithm as that only utilizes connectivity (neighbors) information. This work focuses on anchor-based, range-free localization algorithm, particularly in anisotropic networks. We observe that the pioneer Distance Vector Hop or DV-Hop algorithm, which provides accurate estimation in isotropic networks, can be enhanced to compute localization estimation for anisotropic networks with similar or comparable accuracy. The recently proposed algorithms for anisotropic networks are complex with communication and computational overheads. These algorithms may also be overkill for several location dependent protocols and applications. This paper proposes a scheme, called DV-maxHop, which reaches comparable accuracy quickly utilizing simpler, practical and proven variant of the DV-Hop algorithm. We evaluate the performance of our scheme using extensive simulation on several topologies under the effect of multiple anisotropic factors such as the existence of obstacles, sparse and non-uniform sensor distribution, and irregular radio propagation pattern. Even for isotropic networks, our scheme out-performed recent algorithms with lower computational overheads as well as reduced energy or communication cost due to its faster convergence. We also introduce the formulation and simulation of Multi-objective Optimization to obtain the optimal solution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".