An Adaptive Anycasting Solution for Crowd Sensing in Vehicular Environments

Abstract

Vehicular networks can be seen as the new key enablers of the future networked society. Vehicles traveling can act as mobile sensors and collect a variety of information that can be used to enable various new services such as environment monitoring, traffic management, urban surveillance, and so on. In this paper, we present “adaptive Anycasting solution for Vehicular Environments” (AVE), which is a message delivery protocol that combines geographical and topological information to dynamically adapt its behavior to network conditions. We focus on vehicle-to-infrastructure connectivity for cloud services, where the vehicles send the sensed information as individual and independent messages to a cloud service in the Internet. This scenario requires access to any available close-by roadside unit, thus making anycasting the ideal delivery mechanism. Simulations results show that the hybrid and adaptive approach of AVE is able to improve network performance. For example, regarding delivery ratio, AVE outperforms DYMO by 10% in sparse scenarios and outperforms delay-tolerant networking techniques by 10% in dense scenarios.