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In most sensor network applications, the information gathered by sensors will be meaningless without the location of the sensor nodes. Node localization has been a topic of active research in recent years. Accurate self-localization capability is highly desirable in wireless sensor network. This paper proposes a genetic algorithm based localization(GAL). The proposed genetic algorithm adopts two new genetic operators: single-vertex-neighborhood mutation and the descendbased arithmetic crossover. Four example problems are used to evaluate the performance of the proposed algorithm. Simulation results show that our algorithm can achieve higher accurate position estimation than semi-definite programming with gradient search localization (SDPL) [11] and simulated annealing based localization (SAL) [13]. Compared to the usual crossover operator: simple arithmetic crossover, whole arithmetic crossover and single-point crossover, the proposed crossover can obtain a lower mean position error.

Protein-protein interaction plays an important role in understanding biological processes. In order to resolve the parsing error resulted from modal verb phrases and the noise interference brought by appositive dependency, an improved tree kernel-based PPI extraction method is proposed in this paper. Both modal verbs and appositive dependency features are considered to define some relevant processing rules which can effectively optimize and expand the shortest dependency path between two proteins in the new method. On the basis of these rules, the effective optimization and expanding path is used to direct the cutting of constituent parse tree, which makes the constituent parse tree for protein-protein interaction extraction more precise and concise. The experimental results show that the new method achieves better results on five commonly used corpora.

Random mobility and energy constraint are two main factors affecting system performance in mobile sensor networks, which cause many difficulties to system design. It is necessary to develop high-efficiency algorithms and protocols for mobile sensor networks to adapt to dynamic network environment and energy limitation. In this paper, a new clustering algorithm based on residual energy difference ratio is presented to improve system performance. Firstly, it is an energy-efficient algorithm. The residual energy of sensor nodes and average residual energy of system are considered in the residual energy difference ratio, which effectively avoid the nodes with low residual energy being selected as cluster heads. An energy-optimal scheme is used in cluster formation phase to minimize energy consumption. Secondly, it is a dynamic algorithm. The system dynamically clusters the sensor nodes according to the data transmission delays. It makes the whole system adapt to the random mobility of sensor nodes. The NS2 software is used to simulate the new clustering algorithm. The simulation experiments can verify the validity of the proposed theory.

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