Trust-Driven Multi-Criteria Optimization for UAV-Assisted IoT Networks
Bibliographic record
Abstract
Trustworthy communication is essential for the success of sixth-generation (6G) networks, enabling seamless and reliable interactions across diverse connected Internet of things (IoT) devices and systems. Uncrewed aerial vehicles (UAVs) are expected to play a pivotal role in this ecosystem by facilitating low-latency communication and promoting efficient resource utilization through flexible deployment strategies. In this context, we propose a trust-driven framework for UAV-assisted mobile edge computing (MEC). Initially, UAVs are deployed using the K-means clustering algorithm to maximize coverage. We then formulate an optimization problem that simultaneously targets several conflicting objectives: (i) maximize the trust level of serving UAVs; (ii) minimize computational latency for IoT devices tasks; (iii) maximize the number of served IoT devices; and (iv) balance the trade-off between maximizing trust and minimizing service provisioning cost. UAV trustworthiness is modeled as a composite metric encompassing success rate, security score, communication stability, computational resource availability, and energy sufficiency. To solve this problem, we develop a penalty-guided optimization (PGO) algorithm that guides relaxed binary decision variables toward integer solutions, followed by refinement using sequential quadratic programming. Simulations demonstrate that the PGO algorithm outperforms baseline relaxation methods and achieves performance close to the optimal branch-and-bound approach, while significantly reducing computational complexity. Additionally, unequally weighted trust metrics yield better UAV trust levels as compared to equal weighting, highlighting the advantage of adaptive trust modeling. The proposed UAV trust evaluation framework and deployment strategy thus collectively lead to enhanced network accessibility, reliability, and utility in aerial IoT networks.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".