Xiaojun Lv

Topology analysis based on linear wireless sensor networks in monitoring of high-speed railways

Nowadays, the application of wireless sensor networks in monitoring of high-speed railways is rare or immature in China. In this paper, a deployment strategy of nodes along the railway is proposed for minimizing the total energy consumption. As high-speed trains have high requirements on the real-time and reliability of monitoring networks, the real-time, robustness and lifetime of networks are analyzed for two kinds of topologies which are based on minimum and maximum distance transmission in linear wireless sensor networks (LWSNs). Especially, a group-based data transmission mechanism is proposed for the topology of maximum distance transmission, which aims to improve the performances of real-time and robustness. Results of theoretical analysis and simulations show that the proposed mechanism brings better performances of real-time and robustness for the topology based on maximum distance transmission. In addition, the network lifetime of the topology based on maximum distance transmission has an obvious advantage compared with the topology of minimum distance transmission when the spacing between two adjacent nodes is less than a certain value.

Modeling and analysis of WSN-based emergency braking control for high-speed trains

This paper investigates the problem of wireless sensor network (WSN)-based emergency braking control for high-speed trains. The WSN deployed along railway line, is used to monitor the situation of the railway in real-time and transmit the sensor data to the high-speed trains several kilometers away. Based on the analysis of emergency braking scene and the analysis of communication and time-delay in WSN, a model of emergency braking control is established for the service of the deployment of WSNs nodes in the high-speed railway. A simulation experiment is given, and the strategy of the emergency braking control is obtained for the train speed of 350 km/h.

Transmission performance analysis of wireless sensor networks under complex railway environment

Wireless sensor networks (WSNs) can be widely applied to the environmental monitoring and control systems in railway transportation industry. In this paper, an analytical framework is proposed to study the transmission performance of one-dimensional WSNs under complex railway environment. Specifically, the WSNs along the railway are comprised by two types of sensor nodes: the ordinary sensors and the powerful ones, of which the distributions both follow Poisson Point Process. Analytical results are obtained for the successful reception probabilities of both single-hop communication and end-to-end transmission under the composite channel model, which is the superposition of large-scale fading and Nakagami fading channel. To be specific, we also derive the closed-form expression of successful reception probability in condition of the free space propagation with the path loss exponent α = 2. A simulation platform is established to evaluate the impact of different factors on the transmission performance, and the analysis results are validated to be consistent with the simulations. The results in this paper can be applied to evaluate the transmission performance of WSNs and provide useful guidance for the network design under the railway scenarios.

Study on the framework of surveillance and management system for high-speed railway system

For most people, the high-speed railway has become an important and preferred way to travel. In order to improve the safety, it is of great importance to effectively monitor natural disasters, man-made destruction, and draw up reasonable emergency plan. However, we have great challenges to achieve effective surveillance and management for high-speed railway system, as it is a complex giant system. In order to solve this problem, we employ the theory of parallel system and the technology of big data. In this paper, we put forward the research frame of surveillance and management system for high-speed railway system so as to satisfy the practical requirements of monitoring and management. Firstly, the basic model of the artificial system is set up from the actual system, and then the design method of the computational experiment is given. Finally, the parallel execution of real and artificial systems is described in detail. The research framework proposed in this paper will provide reasonable solutions and suggestions for the parallel control and management of high-speed railway.

A Threshold-Based Kalman Filter with Recursive Covariance Estimation

In the existing works, a Kalman filter with recursive covariance estimation (KF-RCE) was proposed by Bo Feng et al. to sequentially estimating process noise covariance matrix. However, there may be a singular matrix existed in the KF-RCE algorithm, which would lead to unreasonable system state estimation in the system initial stage. In this paper, a threshold-based Kalman filter with recursive covariance estimation (TBKF-RCE), which uses a threshold-based switch scheme, is presented to suppress the singularity phenomenon brought by the singularity matrix in KF-RCE algorithm. Simulation results have shown that the singularity phenomenon which happened in the system initial stage has been weakened by using the TBKF-RCE algorithm.

Modeling for train-ground communication channel based on WSN

In this paper, wireless sensor network (WSN) is deployed along the high speed railway to monitor around environment and to communicate with the high speed trains, which will improve the security of high speed rail significantly. Therefore, modeling a communication channel between the train and the ground nodes has very important significance. In this paper, a deployment scheme of wireless sensor network on a viaduct is first given, and then, considering the unique characteristics of the high speed train runs on the viaduct, the train-ground communication channel is modeled as a finite-state Markov chain (FSMC). Unlike most existing channel models, which divide the location extent of a train into n non-overlapping intervals uniformly, in our channel model, the intervals are first divided based on path loss model. And then, we partition each interval uniformly into some smaller intervals. The simulated data of communication channel signal-to-noise ratio (SNR) is produced by MATLAB, and the accuracy improvement of channel model is illustrated by comparing with an existing channel model.