With the development of industrial network, Industrial Wireless Sensor Networks (IWSNs) outperform traditional wired automation system in terms of flexibility, scalability, and efficiency, etc. And IWSNs have critical demands on reliable and real-time data transmission. To meet these demands and enhance efficiency, a SDN-based routing mechanism for IWSNs is proposed in this paper, to determine the global optimal routing strategy based on the cognition of real-time network status. Firstly, a topology discovery algorithm based on multi-controller collaboration is proposed to improve the efficiency of the SDN controllers' cognition of network status, and each controller in the network can perceive the global network status by synchronizing information. Then, combined with the cognition of controllers for network status, a link quality-aware routing algorithm (LQAR) is proposed, further the global optimal routing is calculated and distributed to the field devices. Additionally, a node failure response strategy and a controller failure response strategy are proposed to enhance the reliability of the network. The simulation results show that the proposed routing mechanism can guarantee real-time data transmission and significantly improve the reliability compared with the traditional industrial wireless network routing mechanisms.

Information-Centric Networking (ICN), as a promising networking paradigm, is facing some challenges with its routing, such as content retrieval, efficiency, etc. In this paper, a Cat Swarm Optimization (CSO)based Quality of Service (QoS)Routing mechanism (CSOQR)is proposed to solve the routing problems in ICN. Firstly, a network model is built to imitate biological behaviors including link model and intelligent node model. Secondly, a user's Quality of Experience (QoE)is mapped to the corresponding QoS level, thus the user's QoS satisfaction is calculated. Then, the forwarding probability of an interface is calculated according to user's QoS satisfaction, and two forwarding interfaces are selected respectively based on Tracing Mode Face Choosing (TMFC)and Seeking Mode Face Choosing (SMFC). In this way, multiple content sources can be found through multiple paths to improve routing success rate, and we devise a migration strategy to balance node load. Finally, the proposed mechanism is simulated on both CERNET and CERNET2. Simulation results show that the proposed CSOQR has good performance in terms of routing success rate, routing hop count and node load.

In this paper, we propose a compression sampling system of vibration signal based on sparse AR (Auto Regression) model. It exploits the Compression Sensing (CS) theory and the architecture of Simple Random Sampling (SRS) system. A basis matrix is constructed based on sparse AR model for reconstructing the received vibration signal. The basis matrix is named SAR basis in this paper, in which the atoms are all prior vibration signal components. The signal can be reconstructed by optimization algorithms with the simple random sampling measurements and the SAR basis. For the case of signal sampling, SRS method scales down the sampling frequency efiectively. Additionally, since the SAR basis is a self-adaptive basis, a desired high reconstruction quality at a low sampling rate can be obtained. From both simulations and experiments, the results show the efiectiveness of the compression sampling system proposed in the terms of reconstruction accuracy (SNR) and Compression Ratio (CR).

A compression sampling system based on sparse AR (auto regression) model is designed in this paper. The sparse samples are non-uniform sampled using uniform random sampling (URS) method by mono-chip computer. To guarantee the reconstruction effectiveness, a basis matrix is constructed with prior signal information to represent the received signal sparsely. Then the received signal is recovered from the samples using optimization algorithms in PC. The URS method can scale down the sampling frequency effectively. The basis matrix is constructed based on the known AR model and named sparse AR (SAR) basis in this paper. Since the SAR basis is a self-adaptive basis, better reconstruction quality at a low sampling rate can be obtained. The performance of the proposed compression sampling system is illustrated using normal vibration signal. From both simulation and experiment, the transmitted signal can be reconstructed effectively and accurately

With the development of entrepreneurship in the world, more and more scholars begin to realize the importance of entrepreneurship, which was in golden stage in last 80's. Corporate entrepreneurship has been the important approach for continuous development of the modern business. It's very important for modern business that how to make proper entrepreneurship strategy according to its realities. Modern strategic management theory shows us that resources, capabilities and environment play a key role in the strategy-making, and the environment could be seen as a exogenous factor, enterprise feeds back information by the change of environment and adjusts strategy in time. So, we advised the comprehensive model of Corporate entrepreneurship strategy and proved it's correctness according to the local empirical data.