Big Data Capability,Knowledge Dynamic Capability,and Business Model Innovation:The Moderating Effect of Innovation Legitimacy

Based on dynamic capability theory and legitimacy theory,a theoretical model is constructed to examine how big data capability,through the mediation of knowledge dynamic capability,drive business model innovation under the moderation effect of innovation legitimacy.The eanalys is isconducted using regression analysis and fuzzy set qualitative comparative analysis(fsQCA)on survey data from 302 enterprises that have already implemented big data application practices.The study finds the following four conclusions.(1)Big data capability has a significant positive impact on business model innovation.(2)Dynamic knowledge capability partially mediates the relationship between big data capability and business model innovation.(3)Innovation legitimacy positively influences business model innovation and positively moderates the relationship between big data capability and businessmodel innovation.(4)Through further qualitative comparative analysis,two causal paths that influence business model innovation are identified.

An Adjust Duty Cycle Method for Optimized Congestion Avoidance and Reducing Delay for WSNs

With the expansion of the application range and network scale of wireless sensor networks in recent years,WSNs often generate data surges and delay queues during the transmission process,causing network paralysis,even resulting in local or global congestion.In this paper,a dynamically Adjusted Duty Cycle for Optimized Congestion based on a real-time Queue Length(ADCOC)scheme is proposed.In order to improve the resource utilization rate of network nodes,we carried out optimization analysis based on the theory and applied it to the adjustment of the node’s duty cycle strategy.Using this strategy to ensure that the network lifetime remains the same,can minimize system delay and maximize energy efficiency.Firstly,the problems of the existing RED algorithm are analyzed.We introduce the improved SIG-RED algorithm into the ADCOC mechanism.As the data traffic changes,the RED protocol cannot automatically adjust the duty cycle.A scheduler is added to the buffer area manager,referring to a weighted index of network congestion,which can quickly determine the status of network congestion.The value of the weighting coefficient W is adjusted by the Bayesian method.The scheduler preferably transmits severely urgent data,alleviating the memory load.Then we combined improved data fusion technology and information gain methods to adjust the duty cycle dynamically.By simulating the algorithm,it shows that it has faster convergence speed and smaller queue jitter.Finally,we combine the adjusted congestion weight and the duty cycle growth value to adjust the data processing rate capability in the real-time network by dynamically adjusting it to adapt to bursts of data streams.Thus,the frequency of congestion is reduced to ensure that the system has higher processing efficiency and good adaptability.

Optimization-Based Fragmental Transmission Method for Video Data in Opportunistic Networks

Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods.