Localized delay‐bounded and energy‐efficient data aggregation in wireless sensor and actor networks
Bibliographic record
Abstract
ABSTRACT In data aggregation, sensor measurements from the whole sensory field or a sub‐field are collected as a single report at an actor by using aggregate functions such as sum, average, maximum, minimum, count, deviation, and so on. We propose a localized delay‐bounded and energy‐efficient data aggregation (DEDA) protocol for request‐driven wireless sensor networks with IEEE 802.11 carrier sense multiple access with collision avoidance run at media access control layer. This protocol uses a novel two‐stage delay model, which measures end‐to‐end delay by using either hop count or degree sum along a routing path depending on traffic intensity. It models the network as a unit disk graph (UDG) and constructs a localized minimal spanning tree (LMST) sub‐graph. Using only edges from LMST, it builds a shortest‐path (thus energy‐efficient) tree rooted at the actor for data aggregation. The tree is used without modification if it generates acceptable delay, compared with a given delay bound. Otherwise, it is adjusted by replacing LMST sub‐paths with UDG edges. The adjustment is done locally on the fly, according to the desired progress value computed at each node. We further propose to integrate DEDA with a localized sensor activity scheduling algorithm and a localized connected dominating set algorithm, yielding two DEDA variants, to improve its energy efficiency and delay reliability. Through an extensive set of simulation, we evaluate the performance of DEDA with various network parameters. Our simulation results indicate that DEDA far outperforms the only existing competing protocol. Copyright © 2011 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".