D5_UAVMule_FastRoute_HelsinkiMedium - Radega1993/the-one-scenario-corpus GitHub Wiki

Scenario D5 — D5_UAVMule_FastRoute_HelsinkiMedium

1. Overview

  • Scenario ID: D5
  • Name: D5_UAVMule_FastRoute_HelsinkiMedium
  • Family: Disaster
  • Settings file: corpus_v1/05_disaster/D5_UAVMule_FastRoute_HelsinkiMedium.settings

Objective

Map-based disaster setting where two fast UAV-like mules traverse long routes to relay sparse contacts.

2. Scenario configuration (core features)

Values below come from analysis/data/features.csv (raw) and the mapping to the 23-core subset.

Feature Value Comment
world_area 63033600 Total simulation area (m^2)
aspect_ratio 0.8933 min(width,height)/max(width,height)
N 62 Total nodes
nrofHostGroups 2 Number of host groups
speed_mean 1.3 Mean configured speed (m/s)
wait_mean 65 Mean pause/wait time (s)
mm_WDM 0 WorkingDayMovement enabled (1/0)
mm_RWP 0 RandomWaypoint enabled (1/0)
mm_MapRoute 1 MapRouteMovement enabled (1/0)
mm_Cluster 0 ClusterMovement enabled (1/0)
mm_Bus 0 BusMovement enabled (1/0)
mm_Linear 0 LinearMovement enabled (1/0)
transmitRange 10 Interface range (m)
bufferSize 50000000 Node buffer (bytes)
transmitSpeed 2000000 Interface speed (bytes/s)
msgTtl 10000 Message TTL
event_interval_mean 112.5 Mean Events1 interval
event_size_mean 65000 Mean Events1 size (bytes)
nrof_event_generators 1 Number of event generators
pattern_burst 0 Burst windows in traffic (1/0)
pattern_hub_target 0 Hub-target traffic pattern (1/0)
workDayLength — Not used in this scenario
ownCarProb — Not used in this scenario
clusterRange_mean — Mean cluster radius if ClusterMovement

3. Mobility model

  • World size: 8400, 7504
  • Base speed range: Not recorded
  • Base wait range: Not recorded

MapRouteMovement for all nodes with a very fast UAV subgroup.

DTN implication

This mobility design creates a constrained-contact disaster regime where connectivity depends on temporal bridges, dense local clusters, or opportunistic relays rather than stable end-to-end paths.

4. Traffic pattern

  • Events.nrof = 1
  • Events1.interval = 45, 180
  • Events1.size = 10k, 120k
  • Group.msgTtl = Not recorded

Traffic is configured as emergency-oriented load with timing/size parameters aligned to this disaster narrative.

DTN implication

Under Epidemic routing, these parameters amplify trade-offs between urgency and congestion: short opportunities improve fast deliveries in contact windows but can sharply increase redundancy or message expiration when partitions persist.

5. Expected network behavior

  • Contact opportunities are heterogeneous and depend on movement structure (clusters/partitions/routes).
  • Delivery is limited when temporal bridges are weak or TTL is very short.
  • Overhead rises quickly when flooding meets dense local contacts.
  • Delay can be bimodal: near-instant inside local contact islands, very high across partitions.

6. Role in the corpus

This scenario represents a specific disaster communication regime inside the corpus, contributing diversity relative to Urban/Campus/Social baselines and complementing other Disaster scenarios with a distinct structural stressor.

7. Distinguishing characteristics

  • Disaster-focused configuration with explicit structural constraints.
  • Mobility/traffic coupling designed to stress store-carry-forward behavior.
  • Relevant for evaluating robustness under disrupted or intermittent connectivity.

8. Correlation with other scenarios (core 23)

Using the 23-core feature space (analysis/data/correlation_pearson_core23.csv):

  • Most similar (top 3):
    • R4_ParkRangers_HelsinkiMedium — r ≈ 0.87
    • V2_TaxiHigh_HelsinkiMedium — r ≈ 0.69
    • V1_TaxiLow_HelsinkiMedium — r ≈ 0.68
  • Most different (top 3) (smallest |r|):
    • U6_OfficeWaitHeavyTail_HelsinkiMedium — r ≈ 0.00
    • U3_MicroMobility_HelsinkiMedium — r ≈ -0.00
    • V4_CarOwnership_0_HelsinkiMedium — r ≈ -0.01

Full pairwise correlations are available in analysis/reports/correlation_core23_report.txt and analysis/data/correlation_pearson_core23.csv.

Interpretation

The nearest scenarios share the same main structural levers (movement model family, host-group structure, and traffic scale), while near-zero correlations typically correspond to scenarios governed by orthogonal drivers (e.g., extreme range/speed, map routing, or different TTL/load regimes).

9. Cluster assignment

In the Ward k=7 clustering on the 23-core feature space (cluster_assignments_core23.csv), this scenario belongs to:

  • Cluster 3.

10. Simulation outputs (optional)

If routing simulations have been run and metrics were extracted (analysis/data/output_metrics.csv):

Metric Value
delivery_ratio 0.0052
latency_mean 366.75
overhead_ratio 36.5
drop_ratio 0.7242268041237113

Interpretation

These outputs are consistent with the scenario's disaster constraints: delivery reflects bridge availability and TTL feasibility; overhead reflects replication pressure in local contacts; missing latency/overhead entries indicate no successful deliveries in the analyzed run.

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