A blockchain-based secure path planning in UAVs communication network
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are one of the most popular and effective systems in various industrial applications such as surveillance, security, and infrastructure inspection. It is gradually becoming an essential part of navigation as a consequence of high progress in military and civilian missions. Path planning of UAVs in military and civilian missions or in unknown and restricted environments is one of the biggest problems facing the operation of UAVs. This problem is not only searching for a path from an initial point to the final but also linked to find an optimal among all possible paths and provides collision avoidance. By examining the best path for UAVs, there is a need for the consideration of various other issues such as security and privacy, turning angle, overtake speed of obstacle, etc. The fundamental problem of UAVs is finding an optimal and secure route in a challenging environment. To overcome these challenges, many researchers have used optimization techniques such as ant colony, particle swarm, artificial bee colony, etc. with planning and coordination. In this paper, a blockchain-based solution is used to secure and authenticate UAVs. Hence, we propose a blockchain-based method that uses a genetic algorithm, which solves both constrained and unconstrained optimization problems. The purpose of this technique is to locate the best possible flight path for the UAVs in a three-dimensional setting. In a genetic algorithm, each iteration is designed to surpass the previous one in terms of improvement. To achieve an ideal route, solving the travelling salesman problem is a crucial step in the proposed approach. Consequently, the blockchain technology offers a reliable wireless communication and a dependable network for UAVs path planning, guaranteeing efficient service. Simulation results demonstrate the impact of the proposed scheme. They show that a genetic algorithm is suitable for optimal path planning for UAVs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".