Although conventional business models have been increasingly affected in front of the big data technology application, it has also brought new opportunities and challenges for enterprise development. In order to create a higher value, enterprises should keep pace with the times and actively develop business innovation service models. The greatest value brought by data is to help enterprises find potential business value. It can provide a broader user market and channels, avoid homogeneous competition, and realize the integration of upstream and downstream value chains. In addition, it abandons the extensive development under the traditional model and allows enterprises to return to real value services, which is also an irresistible trend of business model transformation. This paper studies and analyzes business innovation service models. First, the business model as required is presented, and the management system and risk evaluation method are introduced. Then, the construction of the business service model is discussed, and the typical big data technologies are reviewed. Next, according to the evaluation theory of business model, the index system of business innovation service model is explored, which can examine the development of business model objectively and comprehensively. Last, the operations of the business model under the big data are analyzed. The research on the business model in this paper can be provided with universality and has a certain practical value for the development of business innovation service.
By facilitating the data delivery in wireless sensor networks, the movement of mobile sink can enhance the network connectivity and sensory coverage. However, the optimal path determination of mobile sink is a NP-hard optimization problem. By jointly considering the cluster-based routing and sink mobility, this paper proposes an enhanced ACO-based movement scheduling of mobile sink for data gathering in wireless sensor networks. To meet the delay requirements and balance the energy consumption of the sensor nodes, the optimal cluster heads selection is introduced. Then, an enhanced ACO-based movement scheduling algorithm is proposed to obtain the shortest path of mobile sink by traversing the network. The simulation results show that our proposed method can offer a promising performance in terms of reducing data delivery latency and extending the lifetime of the network.
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