A Cost Model for Storing and Retrieving Data in Wireless Sensor Networks
Bibliographic record
Abstract
Many applications require storing data in Wireless Sensor Networks (WSNs). For example, in environmental monitoring applications. WSN may archive sensor data for retrieval at periodic intervals. In contrast to conventional network data storage, storing data in WSNs is challenging because of the limited power, memory, and communication bandwidth of WSNs. This paper identifies the critical parameters of WSNs and proposes a cost model for data storage and retrieval. This paper also proposes a distributed algorithm that utilizes the cost model to intelligently distribute excess data from sensor nodes over WSN. The proposed algorithm chooses for remote storage nodes with the most extra memory and the least communication cost. The algorithm also adapts dynamically to changes in the storage requirements of sensor nodes. The benefits of the cost model are experimentally evaluated by using the algorithm in a simulated WSN. Results from the experiments show that as much as 30% more data may be stored in WSN at a slightly higher communication cost when a node's data is distributed across WSN instead of only being stored locally in the node.
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 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 | 0.002 | 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 | 0.001 | 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".