A Lightweight Authentication and Resource Optimization Scheme for Secure Internet of Drones in Critical Applications
Bibliographic record
Abstract
Internet of Drones (IoDs) is one among the trending technologies that interconnects the unmanned aerial vehicles (UAVs) or drones to perform confidential operations, which is used in critical applications.The increasing demands for drone capture the attention of both the industrial and academic sectors.The other applications where drones are employed such as traffic, environment, and natural calamity management, Internet of Things (IoT), and smart cities.Alternatively, data transmission among drones becomes a risky process due to the security threats at the time of sensitive message exchange between several applications.So, it is essential to develop a highly protective security prototype to secure the confidential data transmission among the network devices, such as sensors, UAVs, Access Points ( APs), and the Server.Thus, we developed the Design of Lightweight Authentication (LA) Mechanism with Resource Optimization (DLARO) in IoD to guarantee stable and reliable communication.The experimentation DLARO-IoD is performed in platform called NS2, also it offers maximum security and efficiency than another earlier research, such as RUAM-IoD, RAMP-IoD, SLAP-IoD, and BDTC-IoD.The DLARO-IoD method improves IoD networks by using LA and better resource allocation.DLARO-IoD is better for efficiency, security, and reliable communication in IoD applications.Performance analysis involves energy efficiency, packet loss rate, communication cost, malicious detection rate, throughput, data success rate, computational time, and overhead.During comparative analysis, we illustrate that the suggested DLARO-IoD accomplishes maximum security with minimum energy utilization, and the communication cost and computational time are lower compared with the other authentication methods, and it highly suitable for the IoDbased critical applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".