Analysis of the Linkage Effect between Regional Economic Development and Logistics Competitiveness in China

Logistics industry, being the modern industry integrating information, forwarding, warehousing, and transportation, plays an important role in optimizing the industrial structure in regional economic development. There have been many experts and scholars interpreting the relationship between the level of regional economy and logistics industry from the aspect of econometric model. Referring to existing research results, Panel Vector Autoregressive Model and Factor Analysis are applied to study panel data of 5 coastal provinces in past 20 years and construct relevant indicators reflecting logistic competitiveness, the level of regional economy, and degree of openness in order to explore the linkage effect between logistic competitiveness and the level of regional economy. The results suggest that the 5 coastal provinces can merely achieve the long-term and steady development of regional economy by moving towards the linkage development between logistic industry and manufacturing industry

Datanet: Deep Learning Based Encrypted Network Traffic Classification in SDN Home Gateway

A smart home network will support various smart devices and applications, e.g., home automation devices, E-health devices, regular computing devices, and so on. Most devices in a smart home access the Internet through a home gateway (HGW). In this paper, we propose a software-defined network (SDN)-HGW framework to better manage distributed smart home networks and support the SDN controller of the core network. The SDN controller enables efficient network quality-of-service management based on real-time traffic monitoring and resource allocation of the core network. However, it cannot provide network management in distributed smart homes. Our proposed SDN-HGW extends the control to the access network, i.e., a smart home network, for better end-to-end network management. Specifically, the proposed SDN-HGW can achieve distributed application awareness by classifying data traffic in a smart home network. Most existing traffic classification solutions, e.g., deep packet inspection, cannot provide real-time application awareness for encrypted data traffic. To tackle those issues, we develop encrypted data classifiers (denoted as DataNets) based on three deep learning schemes, i.e., multilayer perceptron, stacked autoencoder, and convolutional neural networks, using an open data set that has over 200 000 encrypted data samples from 15 applications. A data preprocessing scheme is proposed to process raw data packets and the tested data set so that DataNet can be created. The experimental results show that the developed DataNets can be applied to enable distributed application-aware SDN-HGW in future smart home networks

Improved Correction Localization Algorithm Based on Dynamic Weighted Centroid for Wireless Sensor Networks

Abstract: For wireless sensor network applications that require location information for sensor nodes, locations of nodes can be estimated by a number of localization algorithms. However, precise location information may be unavailable due to the constraint in energy, computation, or terrain. An improved correction localization algorithm based on dynamic weighted centroid for wireless sensor networks was proposed in this paper. The idea is that each anchor node computes its position error through its neighbor anchor nodes in its range, the position error will be transform to distance error, according the distance between unknown node and anchor node and the anchor node's distance error, the dynamic weighted value will be computed. For each unknown node, it can use the coordinate of anchor node in its range and the dynamic weighted value to compute it's coordinate. Simulation results show that the localization accuracy of the proposed algorithm is better than the traditional centroid localization algorithm and weighted centroid localization algorithm, the position error of three algorithms is decreased along radius increasing, where the decreased trend of our algorithm is significant