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 specialized simulators using Inter-Process Communication (IPC), the proposed Lightweight Information and Communications Technology-centric Mobility Simulation (LIMoSim) uses a shared codebase for mobility and communication algorithms, enabling interactions between both worlds in a native way and offering transparent integration of vehicular mobility for all INET-based extension frameworks. The proposed framework relies on selected, well-known analytical models and requires only a single process for the actual execution of the simulation runs, thus enabling lean setups without synchronization-related overhead. In this chapter, we introduce LIMoSim and its possible applications using different case-studies.

[1]  Nicola Bui,et al.  A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques , 2016, IEEE Communications Surveys & Tutorials.

[2]  Christian Wietfeld,et al.  Machine Learning Based Context-Predictive Car-to-Cloud Communication Using Multi-Layer Connectivity Maps for Upcoming 5G Networks , 2018, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall).

[3]  Christian Wietfeld,et al.  B.A.T.Mobile: Leveraging Mobility Control Knowledge for Efficient Routing in Mobile Robotic Networks , 2016, 2016 IEEE Globecom Workshops (GC Wkshps).

[4]  Mario Di Francesco,et al.  Adaptive configuration of lora networks for dense IoT deployments , 2018, NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium.

[5]  Nicolas Lefebvre,et al.  MATSim-T , 2009, Multi-Agent Systems for Traffic and Transportation Engineering.

[6]  Gabriel-Miro Muntean,et al.  A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches , 2015, IEEE Communications Surveys & Tutorials.

[7]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[8]  Mahadev Satyanarayanan,et al.  Live Synthesis of Vehicle-Sourced Data Over 4G LTE , 2017, MSWiM.

[9]  Peter Vortisch,et al.  Microscopic Traffic Flow Simulator VISSIM , 2010 .

[10]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[11]  Martin Treiber,et al.  Traffic Flow Dynamics , 2013 .

[12]  Juan-Carlos Cano,et al.  Estimating rainfall intensity by using vehicles as sensors , 2017, 2017 Wireless Days.

[13]  Christian Wietfeld,et al.  On the Potential of 5G mmWave Pencil Beam Antennas for UAV Communications: An Experimental Evaluation , 2018, WSA.

[14]  Christian Wietfeld,et al.  Efficient Machine-Type Communication Using Multi-Metric Context-Awareness for Cars Used as Mobile Sensors in Upcoming 5G Networks , 2018, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring).

[15]  Mitra Pourabdollah,et al.  Calibration and evaluation of car following models using real-world driving data , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[16]  Andrea Zanella,et al.  Internet of Things for Smart Cities , 2014, IEEE Internet of Things Journal.

[17]  Christian Wietfeld,et al.  Lightweight joint simulation of vehicular mobility and communication with LIMoSim , 2017, 2017 IEEE Vehicular Networking Conference (VNC).

[18]  Martin Treiber,et al.  Traffic Flow Dynamics: Data, Models and Simulation , 2012 .

[19]  Ismail Güvenç,et al.  UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges , 2017, IEEE Communications Magazine.

[20]  Christian Wietfeld,et al.  LIMoSim: A Lightweight and Integrated Approach for Simulating Vehicular Mobility with OMNeT++ , 2017, ArXiv.