Performance of IEEE 802.15.4 in wireless sensor networks with a mobile sink implementing various mobility strategies
Bibliographic record
Abstract
In this work, we investigate the advantages and challenges of deploying a single mobile sink in IEEE 802.15.4/ZigBee wireless sensor networks (WSNs). The first part of the paper provides an overview of the most recent research on sink mobility in WSNs, placing a special emphasis on different types of sink mobility (random, predictable and controlled) and discussing the application scenarios most suitable for their respective deployment. In the second part of the paper, our OPNET model for simulation of large-scale and ZigBee-based wireless sensor networks is presented. The model enables effective evaluation of random and predictable sink mobility under varying conditions and forms of routing in the underlying ZigBee WSN. The results obtained using this model show that in terms of energy efficiency ZigBeepsilas tree-based routing outperforms ZigBeepsilas mesh routing, both in the case of random and predictable sink mobility. At the same time, under both mobility models, tree-based routing generates longer delays in the delivery of data reporting packets. Furthermore, when compared against each other assuming identical network conditions, random mobility is shown to achieve higher energy efficiency and shorter packet delays than predictable mobility.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".