Unmanned Aerial ad Hoc Networks: Simulation-Based Evaluation of Entity Mobility Models’ Impact on Routing Performance
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Bibliographic record
Abstract
An unmanned aerial ad hoc network (UAANET) is a special type of mobile ad hoc network (MANET). For these networks, researchers rely mostly on simulations to evaluate their proposed networking protocols. Hence, it is of great importance that the simulation environment of a UAANET replicates as much as possible the reality of UAVs. One major component of that environment is the movement pattern of the UAVs. This means that the mobility model used in simulations has to be thoroughly understood in terms of its impact on the performance of the network. In this paper, we investigate how mobility models affect the performance of UAANET in simulations in order to come up with conclusions/recommendations that provide a benchmark for future UAANET simulations. To that end, we first propose a few metrics to evaluate the mobility models. Then, we present five random entity mobility models that allow nodes to move almost freely and independently from one another and evaluate four carefully-chosen MANET/UAANET routing protocols: ad hoc on-demand distance vector (AODV), optimized link state routing (OLSR), reactive-geographic hybrid routing (RGR) and geographic routing protocol (GRP). In addition, flooding is also evaluated. The results show a wide variation of the protocol performance over different mobility models. These performance differences can be explained by the mobility model characteristics, and we discuss these effects. The results of our analysis show that: (i) the enhanced Gauss–Markov (EGM) mobility model is best suited for UAANET; (ii) OLSR, a table-driven proactive routing protocol, and GRP, a position-based geographic protocol, are the protocols most sensitive to the change of mobility models; (iii) RGR, a reactive-geographic hybrid routing protocol, is best suited for UAANET.
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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 | 0.003 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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