Denial of Service Defence for Resource Availability in Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor networks (WSN) over the years have become one of the most promising networking solutions with exciting new applications for the near future. Its deployment has been enhanced by its small, inexpensive, and smart sensor nodes, which are easily deployed, depending on its application and coverage area. Common applications include its use for military operations, monitoring environmental conditions (such as volcano detection, agriculture, and management), distributed control systems, healthcare, and the detection of radioactive sources. Notwithstanding its promising attributes, security in WSN is a big challenge and remains an ongoing research trend. Deployed sensor nodes are vulnerable to various security attacks due to its architecture, hostile deployment location, and insecure routing protocol. Furthermore, the sensor nodes in WSNs are characterized by their resource constraints, such as, limited energy, low bandwidth, short communication range, limited processing, and storage capacity, which have made the sensor nodes an easy target. Therefore, in this paper, we present a review of denial of service attacks that affect resource availability in WSN and their countermeasure by presenting a taxonomy. Future research directions and open research issues are also discussed.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".