In order to solve the contradiction between service quality and survival time of wireless sensor networks, a new energy consumption balance model is proposed by shuffled frog leaping algorithm (SFLA). In this model, the mathematical expression of energy consumption in the physical layer is given with transmit power at first, received power, and signal bandwidth, and the objective optimization function of energy consumption balance is built by the total sending energy consumption and transmission power of WSN. Secondly, the long-range dependent characteristic of signal is reduced with wavelet neural network, and the objective optimization function above is solved by shuffled frog leaping algorithm. Finally, the performances between this algorithm and others are studied in simulation experiment, and the results show that this algorithm has greater advantages such as the error frame, the number of survival nodes, and the network lifetime.

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Customer demand is dynamic and changeable; thus, optimality of the enterprise’s initial location cannot be guaranteed throughout the planning period in order to minimize site selection cost and maximize service reliability in the whole operation cycle. The enterprise planning period is divided into different stages, and a static location model is established at the fixed stage. In addition, a multi-stage dynamic location model is established by introducing the transfer cost between adjacent stages. To reduce the difficulty of solving the dynamic location model, first, we determined the optimal site selection and allocation strategy for each stage. Second, we designed a novel method that transforms the multi-stage dynamic location problem into the shortest path problem in graph theory. Finally, the Dijkstra algorithm was used to find the optimal dynamic location sequence so that its cumulative cost was the lowest in the whole planning period. Through a case study in China, we compare the costs of static and dynamic locations and the location cost under different objectives. The results show that this dynamic location generates more income (as it reduces cost) in comparison to the previous static location, and different location objectives have a substantial influence on location results. At the same time, the findings indicate that exploring the problem of enterprise location from a dynamic perspective could help reduce the operating cost and resources from a sustainable development perspective.

High-utility pattern mining is a research hotspot in the field of pattern mining, and one of its main research topics is how to improve the efficiency of the mining algorithm. Based on the study on the state-of-the-art high-utility pattern mining algorithms, this paper proposes an improved strategy that removes noncandidate items from the global header table and local header table as early as possible, thus reducing search space and improving efficiency of the algorithm. The proposed strategy is applied to the algorithm EFIM (EFficient high-utility Itemset Mining). Experimental verification was carried out on nine typical datasets (including two large datasets); results show that our strategy can effectively improve temporal efficiency for mining high-utility patterns.

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