DDRP: An efficient data‐driven routing protocol for wireless sensor networks with mobile sinks
Bibliographic record
Abstract
SUMMARY Introduction of mobile sinks into a wireless sensor network can largely improve the network performance. However, sink mobility can cause unexpected changes of network topology, which may bring excessive protocol overhead for route maintenance and may offset the benefit from using mobile sinks. In this paper, we propose an efficient data‐driven routing protocol (DDRP) to address this problem. The design objective is to effectively reduce the protocol overhead for data gathering in wireless sensor networks with mobile sinks. DDRP exploits the broadcast feature of wireless medium for route learning. Specifically, each data packet carries an additional option recording the known distance from the sender of the packet to target mobile sink. The overhearing of transmission of such a data packet will gratuitously provide each listener a route to a mobile sink. Continuous such route‐learning among nodes will provide fresh route information to more and more nodes in the network. When no route to mobile sink is known, random walk routing simply is adopted for data packet forwarding. Simulation results show that DDRP can achieve much lower protocol overhead and longer network lifetime as compared with existing work while preserving high packet delivery ratio. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".