No abstract

The control of vehicles' radio communication behavior to deal with the constrained available wireless bandwidth has been identified as a key challenge in VANETs. As an element of congestion control, this paper addresses distributed transmission power control as a means to control the impact of periodic transmissions ('beacons') on the overall channel load. By also considering recently discussed fairness issues, we first examine the trade-off between the effectiveness of controlling the channel load on the one hand and the corresponding costs in terms of the required packet overhead on the other hand. We provide insights to the underlying estimation problems and present a sensitivity analysis with respect to non-homogeneous vehicular traffic densities and non-perfect channel conditions. Second, based on the analysis, we propose a segment-based power adjustment approach based on a distributed vehicle density estimation. The approach put forward in this paper reduces overhead by two orders of magnitude compared to previous approaches while still being effective in controlling the channel load.

Today's advanced simulators facilitate thorough studies on VANETs but are hampered by the computational effort required to consider all of the important influencing factors. In particular, large-scale simulations involving thousands of communicating vehicles cannot be served in reasonable simulation times with typical network simulation frameworks. A solution to this challenge might be found in hybrid simulations that encapsulate parts of a discrete-event simulation in an analytical model while maintaining the simulation's credibility. In this paper, we introduce a hybrid simulation model that analytically represents the probability of packet reception in an IEEE 802.11p network based on four inputs: the distance between sender and receiver, transmission power, transmission rate, and vehicular traffic density. We also describe the process of building our model which utilizes a large set of simulation traces and is based on general linear least squares approximation techniques. The model is then validated via the comparison of simulation results with the model output. In addition, we present a transmission power control problem in order to show the model's suitability for solving parameter optimization problems, which are of fundamental importance to VANETs.

To study the impact of inter-vehicle communications on (vehicular) transport efficiency, e.g., for traffic management purposes, there is a need for efficient and accurate largescale simulations that jointly consider both, the vehicular traffic and the communication system. To overcome the scalability limitations of current discrete event-based network simulators like NS-2, we propose a hybrid simulation approach that can significantly reduce the number of scheduled events by making use of statistical models. Basically, we treat some data traffic, which is not the primary concern of the simulation study, as 'noise' (e.g., beaconing of nodes). While accurately modeling this background traffic we only need to simulate via discrete event-based simulation the actual application we are interested in (e.g., a data dissemination protocol). We outline how the characterization of the background traffic is gained, statistically validated and used. The achievable speed-up is demonstrated in a first application study where a speed funnel is built using inter-vehicle communications. In this scenario, the conservatively estimated speed-up factor is about 500 compared to a pure discrete event-based simulation.