S5_TwoLayer_StudentsStaff - Radega1993/the-one-scenario-corpus GitHub Wiki

Scenario S5 — S5_TwoLayer_StudentsStaff

1. Overview

Scenario ID: S5
Name: S5_TwoLayer_StudentsStaff
Family: Social
Settings file: corpus_v1/06_social/S5_TwoLayer_StudentsStaff.settings

Objective

Two-layer structure (students + staff). Distinct mobility and contact patterns per layer; tests protocol under heterogeneous groups.

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	30000000
aspect_ratio	0.8333
N	75
nrofHostGroups	2
speed_mean	1.6
wait_mean	105
mm_WDM	0
mm_RWP	1
mm_MapRoute	0
mm_Cluster	0
mm_Bus	0
mm_Linear	0
transmitRange	11
bufferSize	50000000
transmitSpeed	2000000
msgTtl	10000
event_interval_mean	102.5
event_size_mean	57500
nrof_event_generators	1
pattern_burst	0
pattern_hub_target	0
workDayLength	Not recorded	Not used in this scenario
ownCarProb	Not recorded	Not used in this scenario
clusterRange_mean	Not recorded	Mean cluster radius if ClusterMovement

3. Mobility model

Social scenarios use movement models that create community structure: ClusterMovement (S1, S6), RandomWaypoint with mixing parameters (S2, S3, S4), or two-layer configurations (S5).

DTN implication

Social scenarios stress community structure, bridge nodes, and temporal patterns (periodic vs random). Delivery depends on inter-community relays; protocols must exploit or tolerate sparse cross-cluster contacts.

4. Traffic pattern

MessageEventGenerator with interval and size tuned per scenario. Uniform or hub-target patterns.

DTN implication

Traffic interacts with community structure: messages within clusters benefit from local density; cross-cluster delivery requires patience or bridge exploitation.

5. Expected network behavior

Contact opportunities driven by community structure and mixing.
Delivery sensitive to bridge presence and TTL.
Overhead can rise with flooding in dense local clusters.
Latency varies: low within clusters, high across partitions.

6. Role in the corpus

This scenario represents a social communication regime contributing diversity in community structure, mixing, and temporal patterns relative to Urban/Campus/Rural baselines.

7. Distinguishing characteristics

Social-focused configuration with explicit community or layer structure.
Tests protocol behaviour under structured vs random mixing.
Complements other Social scenarios with a distinct lever (cluster size, mixing, periodicity, layers).

8. Correlation with other scenarios (core 23)

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

Most similar (top 3):
- S2_WeakCommunities_HighMixing — r ≈ 0.89
- D7_HighLoad_TrafficStorm — r ≈ 0.88
- S4_RandomMixing_NoHotspots — r ≈ 0.72
Most different (top 3) (smallest |r|):
- R2_VillagesTrails_ThreeClusters — r ≈ 0.00
- T5_VeryLongTtl_6to24h — r ≈ 0.00
- T10_HighRateLowSpeed_Congestion — r ≈ 0.02

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

Interpretation

Similar scenarios share structural levers (ClusterMovement, density, mixing). Near-zero correlations correspond to scenarios governed by orthogonal drivers.

9. Cluster assignment

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

Cluster 7.

10. Simulation outputs (optional)

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

Metric	Value
delivery_ratio	0.0853
latency_mean	10002.4892
overhead_ratio	67.6757
drop_ratio	5.493087557603687