Reducing handoff delay of wireless access in vehicular environments by artificial neural network-based geographical fingerprint

Abstract

Wireless access in vehicular environments (WAVE) can offer service to improve traffic safety and efficiency, and to reduce vehicle pollution. Compared with handheld devices, the chief terminals in vehicular environments, vehicles, have higher mobility. Thus, it can be anticipated that in such environments, the terminal devices may often move from one WAVE mode basic service set (WBSS) to another to exchange data and its probability for the handoff to occur is undoubtedly higher than handheld terminal devices. However, without taking high mobility into consideration, traditional IEEE 802.11 wireless transmissions were insufficient under acute moves. Furthermore, vehicular communications usually take place outdoors and the transmission quality might be influenced by other factors, like the weather. Based on different vehicular environments, this paper probes into the handoff and proposes to establish the geographical fingerprint by artificial neural network (ANN) and adjust the transmitted power of access points (APs) according to the weathers to maintain the service region and signal strength. By the measurements in advance, the handoff points can be established to reduce the handoff delay and the suitable handoff points be adjusted in accordance with vehicle speed.