EVALUATION OF A METHODOLOGY FOR SCALABLE 1 DYNAMIC VEHICULAR AD-HOC NETWORKS IN A 2 WELL-CALIBRATED VEHICULAR MOBILITY TEST BED

1 Connected vehicles are becoming ubiquitous with each passing year. Increase in mobile 2 computing is proliferating the possible applications of connected vehicles. Many of these 3 applications involve a continuous need for vehicles to connect to the communication 4 infrastructure. This could result in congestion of the communication network. In this 5 study we evaluate a novel “dynamic grouping” methodology that combines vehicle-to6 vehicle (V2V) and vehicle-to-infrastructure (V2I) communication schemes to make the 7 optimal use of the communication infrastructure. The methodology for dynamic grouping 8 of instrumented vehicles is implemented in a realistic and well-calibrated microscopic 9 traffic simulation test bed of the New Jersey Turnpike for the application of sensor data 10 collection. A reduction in communication infrastructure load of 66-91% can be achieved 11 using the dynamic grouping for systematic aggregation of vehicular information. The 12 maximum bandwidth usage is used as a measure to show that the name-address mapping 13 is scalable. We show that the dynamic grouping methodology is very scalable with 14 negligible loss in data quality as compared to the scenario where each vehicle connects to 15 the communication infrastructure independently. The scalability is shown by generating 16 response surfaces for the load on communication channels for different market 17 penetration and communication ranges. These response surfaces can also be useful in 18 predicting the channel load under future scenarios with increasing market penetration and 19 power of communication radios. The data quality is validated using reported speed and 20 estimated travel times over the network. It is shown that on an average the error in speed 21 is 5.5-8% albeit using far lesser bandwidth using the dynamic grouping approach. 22 Similarly, travel time along different paths is shown to be within 5% during regular 23 conditions and within 10% during non-recurrent congestion. 24

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