LIMoSim: A Framework for Lightweight Simulation of Vehicular Mobility in Intelligent Transportation Systems
With the convergence of mobility and communication in modern Intelligent Transportation Systems (ITS), researchers and developers require simulation tools that are capable of bringing both worlds together. Unlike existing approaches that couple specialize
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LIMoSim: A Framework for Lightweight Simulation of Vehicular Mobility in Intelligent Transportation Systems Benjamin Sliwa and Christian Wietfeld
11.1 Introduction Upcoming smart-city-based Intelligent Transportation Systems (ITS) will be dominated by the convergence of mobility and communication with challenges arising on both sides [20]. On the one hand, the deployment of safe autonomous driving requires coordination among the traffic participants by means of Ultra-Reliable Low-Latency Communication (URLLC). On the other hand, novel communication systems need to operate in highly dynamic environments where different technologies coexist and compete for the available radio resources [4]. Therefore, instead of treating the two worlds separately, upcoming communication systems will have to become mobility-aware in order to integrate the system-immanent dynamics of the network topology into the decision processes. Figure 11.1 illustrates example use-cases of anticipatory mobile networking [2] that exploit mobility knowledge for determining routing paths, performing handover and resource reservation as well as predictive steering of pencil beams in millimeter Wave (mmWave) systems [6]. In experimental evaluations [16, 17], we have integrated channel information as well as mobility knowledge in a machinelearning-based data rate prediction process that is used to schedule the transmission times of sensor data transmissions with respect to the achievable throughput. Similarly, in communication-aware mobility applications, vehicles adjust their path planning according to the available connectivity. While this type of behavior has already received great attention in the context of Unmanned Aerial Vehicle (UAV) networks, it will also be of relevance for cars acting as mobile sensor nodes for upcoming crowd sensing-based applications like distributed weather forecast [3].
B. Sliwa () · C. Wietfeld Communication Networks Institute, TU Dortmund University, Dortmund, Germany e-mail: [email protected]; [email protected] © Springer Nature Switzerland AG 2019 A. Virdis, M. Kirsche (eds.), Recent Advances in Network Simulation, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-030-12842-5_11
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5G Cellular 5G Direct IEEE 802.11p
Mobility-aware Cellular Handover
Predictive Gateway Selection
Context-aware Interface Selection
Fig. 11.1 Example scenarios for anticipatory mobile networking. ©[2018] IEEE. Reprinted, with permission, from [14]
In order to address these arising challenges, researchers and developers require tools that are able to simulate both worlds in a unified manner. Furthermore, easy access to data of both components is of tremendous importance in order to provide the required information for developing mobility-aware communication models for next-generation mobile networks. While full-featured traffic simulators like Simulation of Urban MObility (SUMO) [9] provide a multitude of highly accurate mobility models
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