DOI: 10.5281/zenodo.15836441
title: Improving Human-swarm Interaction Through Speech Control and Peer-to-peer Micro-agent Communication (Dataset)

description: In this repository we present the dataset for comparison of cyb and ROS demonstrators in cluded in the conference paper "Improving Human-swarm Interaction Through Speech Control and Peer-to-peer Micro-agent Communication". A sample image of face detection with cyb is present.

We set up a scenario as per following table (see CYB_ros.csv) :

    


Metric
ROS2 Solution
CyB Solution (lpho=0.033)
Notes


Architecture
Monolithic (1 node)
Distributed (3 nodes)
CyB offers greater modularity


Video Resolution
640x480 (dynamic)
640x480
Both configurable


Target Frame Rate
30 FPS
30 FPS
Same nominal target


Effective Throughput
~30 FPS
~28 FPS
ROS2 slightly superior


Frame Interval
30–40 ms
~33 ms
CyB more precise with lpho=0.033


Detection Latency
< 100 ms
~30–70 ms
CyB faster


Detection Rate
~95% of frames
~13.6% (57/420)
ROS2 still superior, but gap reduced


Detection Frequency
Continuous per frame
Every ~7–8 frames
CyB more selective but consistent


Memory Usage
~500MB (single process)
Distributed (3 processes)
CyB parallelizable


Compression
Native ROS2
JPEG (~12KB/frame)
CyB optimized for bandwidth


Communication
ROS2 Topics
TCP FlowProtocol
ROS2 better integrated into ecosystem


Startup Time
~6 seconds
~10 seconds
ROS2 still faster


Processing Model
Synchronous
Asynchronous demand-driven
CyB more efficient in standby


Detection Accuracy
Continuous
Burst clustering
CyB detects in grouped sequences


Resource Scaling
Single-threaded
Multi-process
CyB scales better


Detection Clustering
Single detections
Multiple per frame (2x duplicates)
CyB generates multiple detections




Performance with lpho=0.033

CyB Improvements vs First Test:





Detection rate increased from 6.7% to 13.6% (+100%) from aprevious test with lpho = 0.1




More precise timing: frame interval from ~37ms to ~33ms




Reduced latency: from 50–80ms to 30–70ms




Faster startup: from 15s to 10s



CyB Detection Pattern:





Burst detection: detects faces in concentrated sequences (e.g., frames 177–216, then a pause, then 300–329)




Multiple detections per frame: often two consecutive detections per face




Clustered precision: once it detects, it maintains detection across several frames



Comparison

ROS2 Remains Superior For:





Continuous detection: 95% vs 13.6%




Ecosystem integration: native to robotic stack




Consistent throughput: stable 30 FPS




Simple deployment: single container



CyB (Optimized) Offers:





Optimized latency: 30–70ms vs <100ms in ROS2




Energy efficiency: selective vs continuous detection




Distributed scalability: 3 nodes vs 1 monolithic




Burst accuracy: when detecting, tracks over several frames



Conclusion

With lpho=0.033, CyB becomes competitive for event-driven detection rather than continuous monitoring.





ROS2 is better suited for continuous surveillance.




CyB (with lpho tuning) excels in reactive/event-based detection, with reduced latency and distributed scalability.



Choice depends on use case:





Continuous monitoring → ROS2




Reactive alerts/event-driven detection → CyB (lpho=0.033)