Distortion analysis for delay tolerant data collection for high-speed wireless sensor and actor networks

In this paper, we propose to leverage high-speed rail as mobile actor to assist structural health monitoring (SHM) data collection and delivery for wireless sensor and actor networks (WSANs). Due to high density in the network topology, sensor observations have spatial and temporal correlation. With the spatiotemporal correlation, we first provide a two-phase data collection scheme. In the first phase, the WSANs transmit the data to a sink node and then aggregate the data using the spatial correlation. In the second phase, when the high-speed train passing by the tunnel or the bridge, the sink node transmits the data to the train and then aggregate the data with consideration of the temporal correlation. Also because of the high speed mobility of the train, the communication link between the sink and the train may be fail, so we need to estimate the lost packet. We then estimate spatiotemporal distortion to denote the event collection´s reliability/fidelity. Through extensive simulation, we discuss several key elements that will affect the reliability.