T_CAFE: A Trust based Security approach for Opportunistic IoT
Bibliographic record
Abstract
Internet of things (IoT) is a revolution of the internet where a group of computing devices, sensors, machines or people, having unique identifiers and the ability to transfer data over the network without human intervention, are interconnected. Opportunistic networks (OppNets) are a type of disruption‐tolerant networks, where network topology is not fixed and the devices are connected intermittently. Opportunistic IOT (OppIoT) is a blend of OppNets and IoT networks, where the data are shared among IoT devices and human communities exploiting the opportunistic contact nature of humans. The data is usually transmitted in a broadcast manner, exposing it to all the members of the network. Thus, securing the data transmitted is of utmost importance in OppIoT. This article proposes a trust‐based schemE (called T_CAFE) for securing the network against several attacks like sybil, bad mouthing, good mouthing, black hole and packet fabrication attacks. Using the opportunistic network environment simulator for performing simulations, it is found that the proposed T_CAFE protocol enhances the network security and outperforms routing protocols such as SHBPR, RSASec and ATDTN in terms of legitimate packet delivery, higher probability of message delivery, lower count of dropped messages and lower value of latency in packet delivery.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".