Protecting Routing Data in WSNs with use of IOTA Tangle
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
Routing in wireless sensor networks (WSNs) are based on multi-hop communication in which the messages pass through multiple sensor nodes, and hence routing algorithms must rely on trust relationships between neighboring nodes. The open access nature of WSNs leads to the possibility of nodes becoming compromised and consequently being turned into malicious objects. One such attack on WSNs is the Sybil attack, in which an attacker can take control of a legitimate node or enter a malicious node into the network and create fake identities. Consequently, they can change the behavior of the WSN, such as its routing schema to cause loops or wrong directions to manipulate data and consume the energy of the network, or even target cluster heads. In this paper, we present a novel technique based on IOTA Tangle, a distributed ledger technology, for the detection and prevention of Sybil attacks by protecting routing data. A transaction history on IOTA is maintained for detecting malicious node injection, and IOTA currency is used as a reputation score to prevent malicious nodes and protect the routing table. Even if an attacker gains access to the network, all routing data can be tracked in IOTA Tangle that will alert the base station about this attack. The technique has been simulated and evaluated using a proof-of-concept prototype.
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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.007 |
| 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