A hybrid approach using mobile element and hierarchical clustering for data collection in WSNs
Bibliographic record
Abstract
How to minimize the energy dissipation and extend the lifetime of wireless sensor networks (WSNs) is still an active research topic nowadays. Hierarchical routing based on node clustering is an effective method, while using mobile elements (MEs) to gather data can prevent huge energy consumption of the sensors from long-distance transmission. Considering that both methods have pros and cons, this paper presents a hybrid approach, called Node Density based Clustering and Mobile Collection (NDCM), to combine the hierarchical routing and ME data collection in WSNs. A number of Cluster Heads (CHs) first gather information from the cluster members and then the ME visits these CHs to collect data. A new CH selection scheme based on the node density is proposed. Thus, a node at the center of an area where nodes are densely deployed is more likely to be a CH, which can improve the efficiency of both intra-cluster routing and ME data collection. We also introduce a simple Random Clustering and Mobile Collection (RCM) scheme according to which a number of CHs are selected randomly throughout the network. In addition, the nodes which are covered by the radio range of the ME, called Virtual Heads (VHs), can also send/relay packets directly to the ME. The different mobility schemes are compared through extensive simulations and the results show that the proposed hybrid NDCM scheme leads to remarkable improvement in network lifetime and convenient trade off between the network energy saving and packet latency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".