An Adaptive Anycasting Solution for Crowd Sensing in Vehicular Environments
An Adaptive Anycasting Solution for Crowd Sensing in Vehicular Environments
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DOI：
10.1109/tie.2015.2447505
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发表时间：
2015-12-01
影响因子：
7.7
通讯作者：
Manzoni, Pietro
中科院分区：
文献类型：
--
作者：
Baguena, Miguel;Calafate, Carlos T.;Manzoni, Pietro
车载网络可以被视为未来网络化社会的新关键推动者。行驶中的车辆可以充当移动传感器，收集各种信息，这些信息可用于实现各种新服务，如环境监测、交通管理、城市监控等。在本文中，我们提出了“车辆环境的自适应任播解决方案”（AVE），这是一种结合地理和拓扑信息来动态调整其行为以适应网络条件的消息传递协议。我们专注于云服务的车辆到基础设施的连接，其中车辆将感知到的信息作为单独和独立的消息发送到互联网中的云服务。此场景需要访问任何可用的附近路边单元，因此使任意铸造成为理想的传递机制。仿真结果表明，AVE的混合自适应方法能够提高网络性能。例如，在传输率方面，AVE在稀疏场景下比DYMO高出10%，在密集场景下比容延迟网络技术高出10%。
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.