# Maintaining Connectivity in Subterranean Emergency Response: A 5G and Wi-Fi Mesh Mobility Dataset

Nicolas Peugnet, Anne Fladenmuller, Thi-Mai-Trang Nguyen, Brigitte

Kervella, Julien Thourot

► To cite this version:

Nicolas Peugnet, Anne Fladenmuller, Thi-Mai-Trang Nguyen, Brigitte Kervella, Julien Thourot. Maintaining Connectivity in Subterranean Emergency Response: A 5G and Wi-Fi Mesh Mobility Dataset. NetMob 2025, Oct 2025, Paris, France. pp.37-38. hal-05314888

HAL Id: hal-05314888

https://hal.science/hal-05314888v1

Submitted on 14 Oct 2025

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# Maintaining Connectivity in Subterranean Emergency Response: A 5G and Wi-Fi Mesh Mobility Dataset

NICOLAS PEUGNET<fnref n="1" />, ANNE FLADENMULLER<fnref n="1" />, THI-MAI-TRANG NGUYEN<fnref n="1" />,<fnref n="2" />, BRIGITTE KERVELLA<fnref n="1" />,<fnref n="3" />, AND JULIEN THOUROT<fnref n="4" />

## 1. INTRODUCTION

Reliable communication is critical for firefighters during emergency interventions, especially in environments such as underground parking lots, where telecommunication infrastructures are often absent or unreliable. While private 5G networks offer a solution, their range is limited by physical obstacles. Wi-Fi mesh networks, used as mobile ad-hoc networks (MANETs), can help extend coverage through multi-hop relaying.

To explore the effectiveness of hybrid 5G/Wi-Fi mesh networks, we conducted a real-world experiment in collaboration with the Paris Fire Brigade (Brigade des Sapeurs-Pompiers de Paris, BSPP), during a live rescue training exercise. This paper presents the resulting dataset [1] of a realistic emergency response in a subterranean environment. The dataset provides mobility traces and connectivity logs and is made openly available to support further research in the field of resilient mobile networking for public safety applications.

## 2. INTERVENTION SCENARIO

The BSPP has a strictly defined operating mode. A team always consists of 6 members: a driver, a team leader (chef d'agrès) and two pairs of firefighters, each composed of a unit leader (chef d'équipe) and a servant. When multiple teams are deployed at the same location, a commanding officer (chef des opérations de secours, or COS) must also be present to coordinate the response.

The exercise scenario was designed in close collaboration with the firefighters to reflect realistic conditions, involving two operational teams and one commanding officer.

The experiment was carried out on one of the Sorbonne Université campuses underground parking (Figure 1), which comprises five interleaved levels, numbered -1 (topmost) to -5 (lowest). A single fire source was positioned in the left section of level -5.

Two firefighter teams participated in the drill. The movements of Team 1 (red arrows) and Team 2 (blue arrows) are annotated in Figure 1 with numbered steps corresponding to key events and their timestamps $t_i$ (see Figure 4).

At around 16:12 briefing of both teams in front of the parking at ground level. Then at 16:19 ($t_1$, arrow 1.1) Team 1 went down in the staircase via emergency access 1 and at 16:30 ($t_3$, arrow 1.2) the first pair of Team 1 entered the exclusion zone and simulated an accident. At 16:38 ($t_4$, arrow 1.3), after receiving an emergency signal, the second pair of Team 1 responded to evacuate the first pair (Fig. 2). The wireless router of pair 1 is left on site. Meanwhile at 16:22 ($t_2$, arrow 2.1), Team 2 went to the emergency access 2. Shortly after (arrow 2.3), the first pair of Team 2 entered the exclusion zone and at 16:42 ($t_5$), located the fire source and attached the lifeline to mark the path to the fire source. At 16:50 ($t_6$, arrow 2.4) Team 2 returned and the exercise ended at 16:58.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Plan of the campus underground parking. The lower-right part is a 3D view of the parking, the rest is a top view showing the positions of the various elements of the intervention and reflects the different steps of the scenario.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Screenshot from the demonstration video<fnref n="1" /> capturing a moment of the exercise. The second pair of Team 1 is rescuing the first one at 16:41, which corresponds to arrow 1.3 in Fig. 1.</figcaption></figure>

## 3. NETWORK TOPOLOGY

The demonstration showed that MANETs can be used to extend the range of private 5G networks and relay the information in remote places, thus motivating the use of a hybrid Wi-Fi and 5G

<footnote n="1">¹Sorbonne Université, CNRS, LIP6, F-75005 Paris, France {nicolas.peugnet,anne.fladenmuller,thi-mai-trang.nguyen,brigitte.kervella}@lip6.fr</footnote>

<footnote n="2">²Université Sorbonne Paris Nord, L2TI, 93430 Villetaneuse, France</footnote>

<footnote n="3">³Université Picardie Jules Verne, UPJV, Amiens, France</footnote>

<footnote n="4">⁴Ingénieur en Chef de l'Armement (ICA), Brigade des sapeurs Pompiers de Paris (BSPP)</footnote>

<footnote n="1">¹Accessible online at: https://www.youtube.com/watch?v=VL96Zk4tzs0</footnote>
architecture. Each firefighter in the experiment carried a terminal device connected to a Wi-Fi router. One router was assigned for each pair and additional routers were carried individually by the other firefighters. Wireless routers provided by *Green Communications* created a wireless mesh routing infrastructure using QOLSR [2] based routing algorithm.

The firefighter trucks were connected in a tactical 5G bubble through a private 5G base station, with coverage extended by 5G/Wi-Fi and Wi-Fi routers. Some routers (R-5g-xx) featuring 5G capability are able to connect directly to the truck antennas, which were deployed at fixed locations as shown in Fig. 1.

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Screenshot of the preview application<fnref n="2" /> showing the network obtained with the VPN, omitting the 5G backhaul.</figcaption></figure>

Due to limitations with the available hardware and software we had to use a QOLSR aware VPN to join together the mesh networks through the 5G core network. Fig. 3 shows the resulting topology, with the dashed lines being the virtual links between each client and the VPN server. The 5g-01 and 5g-02 routers are each connected to a different 5G cell, but as they are connected to the VPN server, behind the 5G core-network, they can act as a bridge between the disconnected meshes. From their point of view, each router sees only one big network that contains all of them.

## 4. COLLECTED DATA

Each router logged the network status every second, storing it in GraphML [3] files. These files contain directed graphs with 30 nodes: 15 routers, 13 terminal devices, and 2 5G base stations. Node and link attributes (listed in Table 1) vary over the 46-minute experiment, as does the number of links (see Fig. 4).

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Router nodes' degree (number of links from this node) evolution over time, only accounting router to router links. The timestamps $t_i$ correspond to the events described in Section 2.</figcaption></figure>

The dataset [1] contains two archives, the raw data collected by each router (raw.zip) and the preprocessed data in multiple formats (artifacts.zip). This latter archive has been produced by merging the data from all routers into a single directory of GraphML files, providing a global state of the network, then exporting it in JSON and aggregating it in CSV spreadsheets.

<table><caption>**Table 1.** Main link and node attributes</caption><thead><tr><th colspan="2">Link attributes</th><th>Node attributes</th></tr></thead><tbody><tr><td>rx-bytes*</td><td>tx-bytes*</td><td>hostname</td></tr><tr><td>rx-packets*</td><td>tx-packets*</td><td>type</td></tr><tr><td>connected-time (seconds)*</td><td>tx-retries*</td><td>main-ip-addr</td></tr><tr><td>signal-avg (RSSI)*</td><td>tx-failed*</td><td>routes**</td></tr><tr><td>ssi†</td><td></td><td>imei†</td></tr></tbody></table>

* Only for Wi-Fi links, obtained from nl80211 [4]

** Only for router nodes, obtained with ip -d -j r [5]

† Only for 5G links/nodes

## 5. CONCLUSION

This dataset captures firefighter connectivity and mobility traces during a live intervention scenario, showcasing a real-world use of a hybrid 5G/Wi-Fi network. This open-access dataset allows for simulations of realistic emergency response situations.

## REFERENCES

1. N. Peugnet, A. Fladenmuller, T.-M.-T. Nguyen, and B. Kervella, "ENE5AI Dataset (5G and Wi-Fi Mesh Mobility)," https://doi.org/10.57745/DFMCYS (2025).

2. H. Badis and K. Al Agha, "QOLSR, QoS routing for ad hoc wireless networks using OLSR," Eur. Trans. on Telecommun. 16, 427 (2005).

3. U. Brandes, M. Eiglsperger, J. Lerner, and C. Pich, "Graph Markup Language (GraphML)," in Handbook of graph drawing and visualization, R. Tamassia, ed. (Chapman & Hall, London, 2010), pp. 517–541.

4. J. Berg, "Linux wireless," https://wireless/docs.kernel.org/ (2024).

5. M. Litvak, ip-route(8) — Linux manual page (2012).

<footnote n="2">²Accessible online at: https://ene5ai.poj.lip6.fr/</footnote>