An Integrated Framework for Network Emulation and Multi-vehicle Algorithm Testing

DEMO: An Integrated Framework for Network Emulation and Multi-vehicle Algorithm Testing Mauricio Rodriguez Universidade Estadual de Campinas (UNICAMP) Ariel Goes de Castro Universidade Estadual de Campinas (UNICAMP) Ramon Fontes Federal University of Rio Grande do Norte Fabricio Rodriguez Telefonica Research Christian Rothenberg Universidade Estadual de Campinas (UNICAMP) ABSTRACT As drones and autonomous vehicles become integral to smart city infrastructures, there is a growing need for tools that can accurately evaluate their behavior under realistic communication and mobility conditions. Existing frameworks often lack support for scalable scenarios, realistic wireless emulation, or the integration of heterogeneous vehicle types. This demonstration presents UNetyEmu as a novel framework that combines real-time network emulation with high-fidelity mobility simulation, enabling realistic experimentation with both aerial and non-aerial autonomous vehicles. This integration allows researchers to evaluate vehicle coordination under dynamic communication conditions typical of smart city scenarios. CCS CONCEPTS •Networks → Network experimentation;•Software and its engineering → Runtime environments;Integrated and visual development environments;•Information systems → Open source software; KEYWORDS UNetyEmu, Network Emulation, Drones, UAV, Autonomous Vehicles, Algorithm Testing, Realistic scenarios ACM Reference Format: Mauricio Rodriguez, Ariel Goes de Castro, Ramon Fontes, Fabricio Rodriguez, and Christian Rothenberg. 2025. DEMO: An Integrated Framework for Network Emulation and Multi-vehicle Algorithm Testing. In ACM SIGCOMM 2025 Posters and Demos (SIGCOMM Posters and Demos ’25), September 8–11, 2025, Coimbra, Portugal. ACM, New York, NY, USA, 3 pages. https://doi.org/10.1145/3744969.3748436 1 INTRODUCTION Aerial vehicles (e.g., drones) in urban environments are envisioned to change the delivery of goods and services. As cities evolve into interconnected ecosystems due to 5G and beyond, drones are increasingly seen as new actors of logistics and coordination, public safety, and agriculture, among others [4, 8]. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). SIGCOMM Posters and Demos ’25, September 8–11, 2025, Coimbra, Portugal ©2025 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-2026-0/25/09. https://doi.org/10.1145/3744969.3748436 Figure 1: Package delivery scenario using UNetyEmu. Existing experimental frameworks related to drone flight, such as CoppeliaSim [ 10 ], Gazebo [ 5 ], AirSim [ 12 ], and UTSim [ 1 ], facilitate the integration of advanced algorithms for drone navigation and control. However, CoppeliaSim has limitations in scenario scalability; Gazebo lacks 3D drone models, so drones must be created from scratch; and both AirSim and UTSim are challenging to control multiple drones simultaneously. When attempting to add in the same scenario other non-aerial vehicles (e.g., trucks), the frameworks do not consider real wireless communication and message exchange. Consequently, while significant progress has been made in autonomous drone navigation [ 6 , 14 , 15 ] and route planning [7, 9, 11], wireless communications remains underexplored [17]. Despite recent efforts to merge network simulation with urban scenarios [ 16 ], emulation in wireless communication will enable more realistic network aspects to be studied, such as propagation media, packet loss, and signal strength. In this context, it is essential to rely on experimental tools that allow integration of network emulation together with a realistic simulation of urban environments. This demo showcases UNetyEmu [ 2 ], a novel framework that bridges this gap by combining network emulation, high-fidelity simulations, and real-time algorithm testing. As shown in Fig. 1, by integrating Mininet-WiFi [3] and Unity [13], UNetyEmu yields a reliable and robust experimentation platform for realistic, multivehicle communication scenarios1. 2 ARCHITECTURE & DEMO UNetyEmu models realistic urban environments using 3D objects such as buildings, sidewalks, and trees. The scenarios are fully configurable and scalable, while UNetyEmu supports aerial vehicles (e.g., drones) with specific physical constraints such as battery capacity, vertical thrust, and payload limitations. Also, it supports non-aerial vehicles (e.g., trucks) whose torque, speed, and weight 1https://github.com/intrig-unicamp/UNetyEmu/ SIGCOMM Posters and Demos ’25, September 8–11, 2025, Coimbra, Portugal Rodriguez et al. Figure 2: UNetyEmu Architecture characteristics are configurable. The vehicles are controlled by a PID controller, allowing their integration with different algorithms such as obstacle avoidance, path planning, and logistics. The framework also includes different sensors such as depth cameras and 360degree LiDAR sensors, which can be adapted to various research requirements like obstacle avoidance and route planning. Figure 2 shows the system architecture consisting of two main components: (i) Mininet-WiFi for network emulation; and (ii) Unity for high-fidelity vehicle and urban simulation. The vehicles operate as virtual nodes managed collaboratively by Mininet-WiFi and Unity, appearing as a single unified entity. For the network, we consider two types of communication: (1) online, where aerial vehicles interact with base stations (BSs) through wireless interfaces; and (2) offline, where communication is maintained through a command-line interface (off/CLI), allowing vehicles to continue operating in Mininet-WiFi even in the absence of BS connectivity. This approach ensures that the vehicle is treated as a single entity, even when its functionality is distributed between Mininet-WiFi and Unity. Tables 1 and 2 detail the communication workflow when a vehicle uses online and offline communication, respectively. During the demo: To validate the proposed framework, we present a relevant use case for future smart city applications leveraging the network connectivity in urban environments. The demo scenario simulates a collaborative package delivery in a small urban environment, involving two drones and a truck. The operation includes delivering two packages to different customers, whose positions Table 1: Online Communication messages 1 A command (e.g., position) is sent to a Scapy subprocess 2 Scapy encapsulates the command in the payload of a TCP/IP packet and sends it to the vehicle 3 On the vehicle, a Scapy sniffer receives the packet and forwards it to the Mininet-WiFi processing the instruction 4 Also on the vehicle, Scapy encapsulates an ACK message for each incoming message and sends it back to Unity 5 Unity receives the ACK and process the message are managed from a logistics center. The truck is seen as a backup to support the drones in case of loss of connectivity or low battery. During execution, one of the drones completes its delivery and returns to its starting point, operating within the coverage area of the base station and with sufficient battery power. Meanwhile, the other drone begins its task normally, but encounters battery and connectivity problems mid-mission. Unable to complete the delivery or return to its starting position, it replans its route and lands in the trunk of the backup truck, which was standing at a predetermined location known to the drone. Finally, the truck takes over the delivery to the final customer while transporting the drone in its trunk for further battery recharging. A demo video is available in the repository documentation.2 3 CONCLUSIONS & FUTURE WORK This demo presents UNetyEmu, a scalable and customizable framework that bridges real-time network emulation with high-fidelity mobility simulation, enabling the study of complex multi-vehicle scenarios in smart city environments. By integrating Mininet-WiFi and Unity, UNetyEmu enables real-time testing of different types of algorithms in realistic urban and network conditions. The selected showcase scenario confirmed the platform’s ability to simulate heterogeneous drone behaviors and responses to challenges such as loss of connectivity and limited battery, making it a suitable tool for analyzing scenarios such as urban logistics and air traffic management within 5G (and beyond) networks. Several promising directions remain for further enhancement of UNetyEmu. For instance, vehicles are seen as Docker containers in Mininet-WiFi, and different applications (e.g., path planning) could be offloaded into vehicles, exploring heterogeneous processing and memory capabilities while enhancing battery models. Also, weather conditions will be introduced to assess their impact on mobility and communication performance. Facilitating the integration of AI and Python-based algorithms is also part of our roadmap, along with extending the framework to consider edge computing facilities. ACKNOWLEDGMENTS This work was supported by Ericsson Telecomunicações Ltda., and by the São Paulo Research Foundation (FAPESP) , grant 2021/00199-8 , CPE SMARTNESS . Also, this study was supported by CAPES/Brasil’ postdoctoral grant, process 88887.005666/ 2024-00, and partially funded by CAPES/Brazil, finance code 001. 2https://github.com/intrig-unicamp/UNetyEmu/wiki/Videos-and-Tutorials Table 2: Offline Communication messages 1 When the vehicle is disconnected from the BS, the online communication will be unavailable. Scapy encapsulates the state in the packet and sends it to Unity 2 Once Scapy receives the new vehicle state, it converts the message into a known instruction to be applied to the vehicle in Unity 3 A command (e.g., position) is sent to a Scapy subprocess 4 On Mininet-WiFi, a socket server process bypasses the vehicle and processes the message in an offline manner DEMO: An Integrated Framework for Network Emulation and Multi-vehicle Algorithm Testing SIGCOMM Posters and Demos ’25, September 8–11, 2025, Coimbra, Portugal REFERENCES [1] Amjed Al-Mousa, Belal H Sababha, Nailah Al-Madi, Amro Barghouthi, and Remah Younisse. 2019. UTSim: A framework and simulator for UAV air traffic integration, control, and communication. International Journal of Advanced Robotic Systems 16, 5 (2019), 1729881419870937. https://doi.org/10.1177/1729881419870937 [2] Mauricio Rodriguez Cesen, Ariel Góes de Castro, Ibini Santana, Ramon Fontes, Fabricio R. Cesen, and Christian Esteve Rothenberg. 2025. 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