An Energy Efficient and Reliable In-Network Data Aggregation Scheme for WSN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data aggregation can reduce the data transmission between the nodes, and thus save the energy and extend the life of the network. Many related researches on in-network data aggregation take the generalized maximum functions. For the cases that the original packets of N nodes aggregated into M (1 <; M <; N) packets, it is a challenge to improve the energy efficiency and reduce the transmission delay under the transmission reliability guarantee. In this paper, a novel ring-based in-network data aggregation scheme is proposed to this problem. The network is partitioned into rings and the data aggregation is executed ring by ring from outside to inside. To ensure transmission reliability, the source or intermediate aggregating node unicasts multiple aggregated packet copies to its next hop node in the inner ring with the maximum residual energy. The reliability is higher with the more unicasting packet copies. However, more sending packets copies will lead to more additional energy cost. Besides, nodes close to the sink tend to relay more size of data packets and the energy is depleted more quickly than nodes far to the sink. Meanwhile, the nodes close to the sink need to relay the aggregated packets, which contain more information. If the number of packet copies is too small, the packets loss will greatly worse the transmission reliability. Based on this, the number of unicasting packet copies is adaptively adjusted through fuzzy logic. The proposed scheme adaptively unicasts variable number of aggregated packets copies continuously in a window according to the request transmission reliability and the imbalance of nodes energy cost. Our analysis and simulation results show the effectiveness of the proposed scheme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.003 | 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 it