Meiling Xu

A Data-Emergency-Aware Scheduling Scheme for Internet of Things in Smart Cities

With the applications of Internet of Things (IoT) for smart cities, the real-time performance for a large number of network packets is facing serious challenge. Thus, how to improve the emergency response has become a critical issue. However, traditional packet scheduling algorithms cannot meet the requirements of the large-scale IoT system for smart cities. To address this shortcoming, this paper proposes EARS, an efficient data-emergency-aware packet scheduling scheme for smart cities. EARS describes the packet emergency information with the packet priority and deadline. Each source node informs the destination node of the packet emergency information before sending the packets. The destination node determines the packet scheduling sequence and processing sequence according to emergency information. Moreover, this paper compares EARS with a first-come, first-served, multilevel queue algorithm and a dynamic multilevel priority packet scheduling algorithm. Simulation results show that EARS outperforms these previous scheduling algorithms in terms of packet loss rate, average packet waiting time, and average packet end-to-end delay.

Multivariate Chaotic Time Series Prediction Based on Improved Grey Relational Analysis

In multivariate chaotic time series prediction, correlation analysis is important for reducing input dimensions and improving prediction performance. Grey relational analysis (GRA) has proved to be an effective method for data correlation analysis, especially for inexact data and incomplete data. In GRA, points are usually regarded as objects, and the distance between points or the concave and convex degree are mostly used to measure the correlations. However, with discrete variables, correlation analysis results always tend to have some deviations when using prior GRA methods. Furthermore, GRA methods cannot directly use vector datasets. Therefore, in this paper, an improved GRA method is proposed based on vector projections. The input and output variables are expressed as vectors by linking two adjacent points. The vectors, instants of the points, are regarded as the objects, and the projection length of input variables to output variables is used to measure the correlations. The smaller the difference between the projection length and the input variables, the higher the correlation. Then, a hybrid variable selection and prediction model is proposed based on the improved GRA method for multivariate chaotic time series predictions, in order to overcome the negative effects of irrelevant and redundant variables caused by phase-space reconstruction. The experimental results based on the gas furnace dataset and San Francisco river runoff dataset demonstrate that the improved GRA method is effective for data correlation analysis, and the prediction accuracy is better than prior GRA-based methods.

Finite-time combination synchronization of uncertain complex networks by sliding mode control

Over the last few years, the combination synchronization of chaotic system receives a special attention. This paper introduces the idea of combination synchronization into complex networks. Based on sliding mode control (SMC) principle, the finite-time combination synchronization (FTCS) of four uncertain complex networks which combining as the form of A+B+C-D is investigated. And there are unknown parameters and uncertain disturbances in all the network models. By using finite time stability theory and linear matrix inequality, designing appropriate network sliding mode surface and control input, then obtain the synchronization condition of the FTCS and corresponding update laws of unknown parameters. The proposed method has a strong robustness which can overcome the influence of uncertain disturbances and identify the unknown parameters of nodes. Furthermore, the method can estimate the high bounds of the synchronous settling time. Simulation experiment proves the feasibility and effectiveness of the proposed method.