Security vulnerabilities and countermeasures against jamming attacks in Wireless Sensor Networks: A survey
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
Wireless sensor network is a collection of several nodes in a network capable of sharing information in wireless medium. Due to the presence of wireless medium it is highly vulnerable to network attacks. WSN has many applications ranging from monitoring of environment to highly restricted military and surveillance where security is a major requirement. The most common and dangerous attack which can be proved harmful for WSN is jamming attack. In jamming an adversary can limit the capabilities of WSN communication by interfering with RF signals using certain jamming devices. Securing a WSN is one of the major concerns of network safety. In this paper, we have discussed various jamming techniques and types of jammers. We have also listed some countermeasures of jamming which, if used, depending on the application can significantly reduce the chances and effects of jamming. Some limitations and challenges of WSN are also explained. Starting from the introduction about the WSN, our work will cover all the pros and cons of the jammers and countermeasures which can help an interested researcher to gain some knowledge about the topic.
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.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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