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Application environments of wireless sensor networks (WSNs) include heterogeneous nodes with different packet sizes, transmission abilities and tolerable delay times. This study aims to design a reasonable network topology and transmission timing for these heterogeneous nodes to improve the quality of service (QoS) of networks.

In this paper, the authors treat node urgency and data packets as the basis of network clustering and to extend the network lifetime. The flow, energy consumption and residual energy of a node are included in the cluster head election. We also propose a delay evaluation function.

All the nodes in the network are guaranteed to transmit to the sink nodes efficiently by planning the transmission order in each cluster.

The simulation results show that the proposed method can balance node urgency and data packets path planning, which not only extends the lifetime of the network but also decreases network delay and improves the overall efficiency.

The owners of mega projects typically assemble multiple academic research units and enterprises to form an innovation alliance, which carries out knowledge transfer and knowledge creation targeting technical challenges in the process of engineering construction. Due to high technical and management complexity of mega projects, factors affecting knowledge transfer among innovation subjects are complex and diverse. This study proposes a mixed system dynamics (SD) method to build and simulate the process of knowledge transfer in mega projects innovation and analyzes the driving mechanism that enhances knowledge stock of enterprises and engineering innovation results.

First, this paper proposes a conceptual model for knowledge transfer in mega projects by adopting event analysis of the data gained from investigations and interviews. Then, a qualitative model of knowledge transfer that considers mutual influences of the owner, academic research unit and enterprises is developed. Based on that, mathematical relationship among variables of the qualitative model is determined and a quantitative model of knowledge transfer that considers heterogeneity of knowledge sender is built. Finally, simulation is achieved using Vensim software.

The factors affecting knowledge stock of enterprises are analyzed from three aspects: (1) the individual motives and capability of academic research units and enterprises; (2) the gap between academic research units and enterprises; (3) the heterogeneity of academic research units. The results show that the willingness and capability of knowledge reception by enterprises, specific knowledge transfer context such as relational distance and organization distance between academic research units and enterprises and academic research units with high knowledge stock have key influences on the knowledge stock of enterprises.

Factors affecting knowledge transfer within the alliance of innovation in mega projects and their correlations are highly complicated and difficult to determine. Despite massive investigations and interviews on many long-span bridges in China in this study, it is barely possible to directly obtain accurate data for all variables in the model. Limitations of historical data result in limitations on applications of the proposed model.

By building the mega projects knowledge transfer model and conducting simulation analysis, this paper has generated practical values for the owners of mega projects on fostering, organizing, coordinating and managing of innovations. Especially, this study provides specific strategies and suggestions on selection of innovation subjects, motivation and guaranteed efficiency of knowledge transfer and knowledge creation of academic research units and enterprises.

This study proposes a conceptual model for factors affecting knowledge transfer that applies to innovations in mega project context, which fills the gap in the research of knowledge management in mega project innovations. Additionally, combining with the method of SD, the unique role of owner in knowledge transfer of mega projects and the differences among various knowledge senders and their influences on knowledge stocks of enterprises are thoroughly considered, and the research method of modeling and simulation of knowledge transfer mechanism is supplemented and extended.