CMESR: A hybrid approach for data collection and sensor redeployment using mobile element in WSNs
Bibliographic record
Abstract
The data collection schemes in wireless sensor network (WSNs), have been intensively studied in the decade yet still a very active area. A number of schemes, such as the multihop data forwarding and the mobile element (ME) gathering, have been proposed. In the majority of the literatures, the full sensing field coverage and network connectivity are usually ignored or assumed to be true. However, such conditions may not be satisfied in reality due to the random deployment of the sensor nodes (SNs) over the target area. In this paper we jointly consider the network connectivity, field coverage and data collection, and propose a hybrid approach called cluster-based mobile element scanning and redeployment (CMESR) to achieve these goals simultaneously. CMESR combines the intra-cluster data gathering and the ME harvesting to collect data and redeploy SNs adaptively. Furthermore, the mechanisms of low-overhead multihop routing, redundant sensing and transmission avoidance, dynamic cluster-head selection and SN redeployment are provided. The performance of CMESR is simulated extensively and compared to other cluster-based schemes such as LEACH, BCDCP and PEGASIS. The results have shown that using CMESR, not only the energy consumption is significantly reduced and balanced among the SNs, but also the latency is minimized. The 100% field coverage and network connectivity can also be ensured by using the ME to redeploy SNs adaptively.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".